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2 Commits

436 changed files with 775 additions and 923290 deletions
+31 -103
View File
@@ -22,10 +22,7 @@ jobs:
- name: Install build dependencies
run: |
apt-get update -qq
apt-get install -y gcc libcurl4-openssl-dev apt-transport-https ca-certificates
echo "deb [trusted=yes] https://packages.cloud.google.com/apt cloud-sdk main" \
> /etc/apt/sources.list.d/google-cloud-sdk.list
apt-get update -qq && apt-get install -y google-cloud-cli
apt-get install -y gcc libcurl4-openssl-dev
# Seed: use the committed linux-amd64 binary as the bootstrap
- name: Bootstrap from committed linux binary (seed)
@@ -39,9 +36,9 @@ jobs:
run: |
dist/platform/elc-linux-amd64 elc-cli.el > dist/elc-gen2.c
gcc -O2 \
-I runtime \
-I el-compiler/runtime \
dist/elc-gen2.c \
runtime/el_runtime.c \
el-compiler/runtime/el_runtime.c \
-lcurl -lssl -lcrypto -lpthread -lm \
-o dist/platform/elc
chmod +x dist/platform/elc
@@ -54,9 +51,9 @@ jobs:
mkdir -p dist/bin
dist/platform/elc elb.el > dist/elb.c
gcc -O2 \
-I runtime \
-I el-compiler/runtime \
dist/elb.c \
runtime/el_runtime.c \
el-compiler/runtime/el_runtime.c \
-lcurl -lssl -lcrypto -lpthread -lm \
-o dist/bin/elb
chmod +x dist/bin/elb
@@ -87,22 +84,13 @@ jobs:
bash tests/html_sanitizer/run.sh
# Native El test suites (elc --test, compile-link-run)
# el_runtime.c is precompiled to .o once and reused by all 8 modules.
- name: Precompile el_runtime.o
run: |
set -euo pipefail
RUNTIME="$(pwd)/runtime"
gcc -O2 -c -I "$RUNTIME" "$RUNTIME/el_runtime.c" \
-o /tmp/el_runtime.o
echo "el_runtime.o compiled"
- name: Run tests - native (core)
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_core.el > /tmp/el_native_core.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_core.c /tmp/el_runtime.o \
gcc -O2 -I "$RUNTIME" /tmp/el_native_core.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_core
/tmp/el_native_core
@@ -110,9 +98,9 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_text.el > /tmp/el_native_text.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_text.c /tmp/el_runtime.o \
gcc -O2 -I "$RUNTIME" /tmp/el_native_text.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_text
/tmp/el_native_text
@@ -120,9 +108,9 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_string.el > /tmp/el_native_string.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_string.c /tmp/el_runtime.o \
gcc -O2 -I "$RUNTIME" /tmp/el_native_string.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_string
/tmp/el_native_string
@@ -130,9 +118,9 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_math.el > /tmp/el_native_math.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_math.c /tmp/el_runtime.o \
gcc -O2 -I "$RUNTIME" /tmp/el_native_math.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_math
/tmp/el_native_math
@@ -140,9 +128,9 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_state.el > /tmp/el_native_state.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_state.c /tmp/el_runtime.o \
gcc -O2 -I "$RUNTIME" /tmp/el_native_state.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_state
/tmp/el_native_state
@@ -150,9 +138,9 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_time.el > /tmp/el_native_time.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_time.c /tmp/el_runtime.o \
gcc -O2 -I "$RUNTIME" /tmp/el_native_time.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_time
/tmp/el_native_time
@@ -160,9 +148,9 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_json.el > /tmp/el_native_json.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_json.c /tmp/el_runtime.o \
gcc -O2 -I "$RUNTIME" /tmp/el_native_json.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_json
/tmp/el_native_json
@@ -170,9 +158,9 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_env.el > /tmp/el_native_env.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_env.c /tmp/el_runtime.o \
gcc -O2 -I "$RUNTIME" /tmp/el_native_env.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_env
/tmp/el_native_env
@@ -180,9 +168,9 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_fs.el > /tmp/el_native_fs.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_fs.c /tmp/el_runtime.o \
gcc -O2 -I "$RUNTIME" /tmp/el_native_fs.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_fs
/tmp/el_native_fs
@@ -191,7 +179,7 @@ jobs:
run: |
ABS_ELB="$(pwd)/dist/bin/elb"
ABS_ELC="$(pwd)/dist/platform/elc"
ABS_RUNTIME="$(pwd)/runtime"
ABS_RUNTIME="$(pwd)/el-compiler/runtime"
ABS_OUT="$(pwd)/dist/bin"
(cd ../epm && "$ABS_ELB" --clean --elc="$ABS_ELC" --runtime="$ABS_RUNTIME" --out="$ABS_OUT")
chmod +x dist/bin/epm
@@ -202,7 +190,7 @@ jobs:
run: |
ABS_ELB="$(pwd)/dist/bin/elb"
ABS_ELC="$(pwd)/dist/platform/elc"
ABS_RUNTIME="$(pwd)/runtime"
ABS_RUNTIME="$(pwd)/el-compiler/runtime"
ABS_OUT="$(pwd)/dist/bin"
(cd tools/install && "$ABS_ELB" --clean --elc="$ABS_ELC" --runtime="$ABS_RUNTIME" --out="$ABS_OUT")
chmod +x dist/bin/el-install
@@ -214,18 +202,12 @@ jobs:
env:
GCP_SA_KEY: ${{ secrets.GCP_SA_KEY }}
run: |
# Fail loudly: previously this step had no `set -e`, so an auth or
# upload failure was swallowed (step exited 0 on the trailing echo)
# and the SDK silently never published. Surface failures now.
set -euo pipefail
if [ -z "${GCP_SA_KEY:-}" ]; then
echo "FATAL: GCP_SA_KEY secret is empty — cannot authenticate to publish" >&2
exit 1
fi
echo "${GCP_SA_KEY}" > /tmp/gcp-key.json
apt-get install -y -qq apt-transport-https ca-certificates curl
echo "deb [trusted=yes] https://packages.cloud.google.com/apt cloud-sdk main" > /etc/apt/sources.list.d/google-cloud-sdk.list
apt-get update -qq && apt-get install -y google-cloud-cli
gcloud auth activate-service-account --key-file=/tmp/gcp-key.json
gcloud config set project neuron-785695
echo "Publishing as active account: $(gcloud config get-value account 2>/dev/null)"
VERSION="${GITHUB_SHA:0:8}"
@@ -251,7 +233,7 @@ jobs:
--project=neuron-785695 \
--package=el-runtime-c \
--version="${VERSION}" \
--source=runtime/el_runtime.c
--source=el-compiler/runtime/el_runtime.c
gcloud artifacts generic upload \
--repository=foundation-dev \
@@ -259,7 +241,7 @@ jobs:
--project=neuron-785695 \
--package=el-runtime-h \
--version="${VERSION}" \
--source=runtime/el_runtime.h
--source=el-compiler/runtime/el_runtime.h
gcloud artifacts generic upload \
--repository=foundation-dev \
@@ -267,61 +249,7 @@ jobs:
--project=neuron-785695 \
--package=el-runtime-js \
--version="${VERSION}" \
--source=runtime/el_runtime.js
--source=el-compiler/runtime/el_runtime.js
echo "Published El SDK version=${VERSION} to foundation-dev"
# Keep key alive for the ci-base rebuild step below
# (deleted in that step after docker push)
- name: Rebuild ci-base with fresh El SDK (dev)
# Patches ci-base:dev in-place: pulls the existing image (which has all
# system deps — Node, Go, gcloud, Docker CLI, etc.) and overlays the freshly
# built El SDK on top. Keeps the full ci-base rebuild fast and incremental.
#
# continue-on-error: this is a CI-cache optimization, NOT the release
# artifact. It runs Docker (pull/build/push ~600MB) on the host-mode GCE
# runner where DinD/Docker availability is fragile. A failure here must
# never block or redden the job — the SDK publish above is the deliverable.
continue-on-error: true
if: github.event_name == 'push'
env:
GCP_SA_KEY: ${{ secrets.GCP_SA_KEY }}
run: |
set -euo pipefail
CI_BASE="us-central1-docker.pkg.dev/neuron-785695/neuron-ci/ci-base"
SHA="${GITHUB_SHA:0:8}"
echo "${GCP_SA_KEY}" > /tmp/gcp-key.json
gcloud auth activate-service-account --key-file=/tmp/gcp-key.json
gcloud config set project neuron-785695
gcloud auth configure-docker us-central1-docker.pkg.dev --quiet
# Pull existing ci-base:dev (or fall back to :latest on first run)
BASE_TAG="dev"
docker pull "${CI_BASE}:dev" || { docker pull "${CI_BASE}:latest" && BASE_TAG="latest"; }
# Inline Dockerfile — only replaces the El SDK layer
cat > /tmp/Dockerfile.ci-base-patch << 'EOF'
ARG BASE
FROM ${BASE}
COPY dist/platform/elc /opt/el/dist/platform/elc
COPY dist/bin/elb /opt/el/dist/bin/elb
COPY runtime/el_runtime.c /opt/el/runtime/el_runtime.c
COPY runtime/el_runtime.h /opt/el/runtime/el_runtime.h
COPY runtime/el_runtime.js /opt/el/runtime/el_runtime.js
RUN chmod +x /opt/el/dist/platform/elc /opt/el/dist/bin/elb
EOF
docker build \
--build-arg BASE="${CI_BASE}:${BASE_TAG}" \
--build-arg BUILDKIT_INLINE_CACHE=1 \
-f /tmp/Dockerfile.ci-base-patch \
-t "${CI_BASE}:dev" \
-t "${CI_BASE}:dev-${SHA}" \
.
docker push "${CI_BASE}:dev"
docker push "${CI_BASE}:dev-${SHA}"
echo "ci-base rebuilt: ${CI_BASE}:dev (${SHA})"
rm -f /tmp/gcp-key.json
+17 -79
View File
@@ -46,9 +46,9 @@ jobs:
run: |
dist/platform/elc-linux-amd64 elc-cli.el > dist/elc-gen2.c
gcc -O2 \
-I runtime \
-I el-compiler/runtime \
dist/elc-gen2.c \
runtime/el_runtime.c \
el-compiler/runtime/el_runtime.c \
-lcurl -lssl -lcrypto -lpthread -lm \
-o dist/platform/elc
chmod +x dist/platform/elc
@@ -84,7 +84,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_core.el > /tmp/el_native_core.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_core.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_core
@@ -94,7 +94,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_text.el > /tmp/el_native_text.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_text.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_text
@@ -104,7 +104,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_string.el > /tmp/el_native_string.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_string.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_string
@@ -114,7 +114,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_math.el > /tmp/el_native_math.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_math.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_math
@@ -124,7 +124,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_state.el > /tmp/el_native_state.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_state.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_state
@@ -134,7 +134,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_time.el > /tmp/el_native_time.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_time.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_time
@@ -144,7 +144,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_json.el > /tmp/el_native_json.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_json.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_json
@@ -154,7 +154,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_env.el > /tmp/el_native_env.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_env.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_env
@@ -164,7 +164,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_fs.el > /tmp/el_native_fs.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_fs.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_fs
@@ -176,9 +176,9 @@ jobs:
mkdir -p dist/bin
dist/platform/elc elb.el > dist/elb.c
gcc -O2 \
-I runtime \
-I el-compiler/runtime \
dist/elb.c \
runtime/el_runtime.c \
el-compiler/runtime/el_runtime.c \
-lcurl -lssl -lcrypto -lpthread -lm \
-o dist/bin/elb
chmod +x dist/bin/elb
@@ -189,7 +189,7 @@ jobs:
run: |
ABS_ELB="$(pwd)/dist/bin/elb"
ABS_ELC="$(pwd)/dist/platform/elc"
ABS_RUNTIME="$(pwd)/runtime"
ABS_RUNTIME="$(pwd)/el-compiler/runtime"
ABS_OUT="$(pwd)/dist/bin"
(cd ../epm && "$ABS_ELB" --clean --elc="$ABS_ELC" --runtime="$ABS_RUNTIME" --out="$ABS_OUT")
chmod +x dist/bin/epm
@@ -200,7 +200,7 @@ jobs:
run: |
ABS_ELB="$(pwd)/dist/bin/elb"
ABS_ELC="$(pwd)/dist/platform/elc"
ABS_RUNTIME="$(pwd)/runtime"
ABS_RUNTIME="$(pwd)/el-compiler/runtime"
ABS_OUT="$(pwd)/dist/bin"
(cd tools/install && "$ABS_ELB" --clean --elc="$ABS_ELC" --runtime="$ABS_RUNTIME" --out="$ABS_OUT")
chmod +x dist/bin/el-install
@@ -212,21 +212,12 @@ jobs:
env:
GCP_SA_KEY: ${{ secrets.GCP_SA_KEY }}
run: |
# Fail loudly: previously this step had no `set -e`, so an auth or
# upload failure was swallowed (step exited 0 on the trailing echo)
# and the SDK silently never published. Surface failures now.
set -euo pipefail
if [ -z "${GCP_SA_KEY:-}" ]; then
echo "FATAL: GCP_SA_KEY secret is empty — cannot authenticate to publish" >&2
exit 1
fi
echo "${GCP_SA_KEY}" > /tmp/gcp-key.json
apt-get install -y -qq apt-transport-https ca-certificates curl
echo "deb [trusted=yes] https://packages.cloud.google.com/apt cloud-sdk main" > /etc/apt/sources.list.d/google-cloud-sdk.list
apt-get update -qq && apt-get install -y google-cloud-cli
gcloud auth activate-service-account --key-file=/tmp/gcp-key.json
gcloud config set project neuron-785695
echo "Publishing as active account: $(gcloud config get-value account 2>/dev/null)"
VERSION="${GITHUB_SHA:0:8}"
@@ -244,7 +235,7 @@ jobs:
--project=neuron-785695 \
--package=el-runtime-c \
--version="${VERSION}" \
--source=runtime/el_runtime.c
--source=el-compiler/runtime/el_runtime.c
gcloud artifacts generic upload \
--repository=foundation-stage \
@@ -252,60 +243,7 @@ jobs:
--project=neuron-785695 \
--package=el-runtime-h \
--version="${VERSION}" \
--source=runtime/el_runtime.h
--source=el-compiler/runtime/el_runtime.h
echo "Published El SDK version=${VERSION} to foundation-stage"
# Keep key alive for the ci-base rebuild step below
# (deleted in that step after docker push)
- name: Rebuild ci-base with fresh El SDK (stage)
# Patches ci-base:stage in-place: pulls the existing image (which has all
# system deps — Node, Go, gcloud, Docker CLI, etc.) and overlays the freshly
# built El SDK on top. Keeps the full ci-base rebuild fast and incremental.
#
# continue-on-error: this is a CI-cache optimization, NOT the release
# artifact. It runs Docker (pull/build/push ~600MB) on the host-mode GCE
# runner where DinD/Docker availability is fragile. A failure here must
# never block or redden the job — the SDK publish above is the deliverable.
continue-on-error: true
if: github.event_name == 'push'
env:
GCP_SA_KEY: ${{ secrets.GCP_SA_KEY }}
run: |
set -euo pipefail
CI_BASE="us-central1-docker.pkg.dev/neuron-785695/neuron-ci/ci-base"
SHA="${GITHUB_SHA:0:8}"
echo "${GCP_SA_KEY}" > /tmp/gcp-key.json
gcloud auth activate-service-account --key-file=/tmp/gcp-key.json
gcloud config set project neuron-785695
gcloud auth configure-docker us-central1-docker.pkg.dev --quiet
# Pull existing ci-base:stage (system deps stay cached in the base layer)
docker pull "${CI_BASE}:stage" || docker pull "${CI_BASE}:latest"
# Inline Dockerfile — only replaces the El SDK layer
cat > /tmp/Dockerfile.ci-base-patch << 'EOF'
ARG BASE
FROM ${BASE}
COPY dist/platform/elc /opt/el/dist/platform/elc
COPY dist/bin/elb /opt/el/dist/bin/elb
COPY runtime/el_runtime.c /opt/el/runtime/el_runtime.c
COPY runtime/el_runtime.h /opt/el/runtime/el_runtime.h
COPY runtime/el_runtime.js /opt/el/runtime/el_runtime.js
RUN chmod +x /opt/el/dist/platform/elc /opt/el/dist/bin/elb
EOF
docker build \
--build-arg BASE="${CI_BASE}:stage" \
--build-arg BUILDKIT_INLINE_CACHE=1 \
-f /tmp/Dockerfile.ci-base-patch \
-t "${CI_BASE}:stage" \
-t "${CI_BASE}:stage-${SHA}" \
.
docker push "${CI_BASE}:stage"
docker push "${CI_BASE}:stage-${SHA}"
echo "ci-base rebuilt: ${CI_BASE}:stage (${SHA})"
rm -f /tmp/gcp-key.json
+25 -44
View File
@@ -47,9 +47,9 @@ jobs:
mkdir -p dist/platform
dist/platform/elc-linux-amd64 elc-cli.el > dist/elc-gen2.c
gcc -O2 \
-I runtime \
-I el-compiler/runtime \
dist/elc-gen2.c \
runtime/el_runtime.c \
el-compiler/runtime/el_runtime.c \
-lcurl -lssl -lcrypto -lpthread -lm \
-o dist/platform/elc
chmod +x dist/platform/elc
@@ -62,9 +62,9 @@ jobs:
mkdir -p dist/bin
dist/platform/elc elb.el > dist/elb.c
gcc -O2 \
-I runtime \
-I el-compiler/runtime \
dist/elb.c \
runtime/el_runtime.c \
el-compiler/runtime/el_runtime.c \
-lcurl -lssl -lcrypto -lpthread -lm \
-o dist/bin/elb
chmod +x dist/bin/elb
@@ -75,7 +75,7 @@ jobs:
run: |
ABS_ELB="$(pwd)/dist/bin/elb"
ABS_ELC="$(pwd)/dist/platform/elc"
ABS_RUNTIME="$(pwd)/runtime"
ABS_RUNTIME="$(pwd)/el-compiler/runtime"
ABS_OUT="$(pwd)/dist/bin"
(cd ../epm && "$ABS_ELB" --clean --elc="$ABS_ELC" --runtime="$ABS_RUNTIME" --out="$ABS_OUT")
chmod +x dist/bin/epm
@@ -86,7 +86,7 @@ jobs:
run: |
ABS_ELB="$(pwd)/dist/bin/elb"
ABS_ELC="$(pwd)/dist/platform/elc"
ABS_RUNTIME="$(pwd)/runtime"
ABS_RUNTIME="$(pwd)/el-compiler/runtime"
ABS_OUT="$(pwd)/dist/bin"
(cd tools/install && "$ABS_ELB" --clean --elc="$ABS_ELC" --runtime="$ABS_RUNTIME" --out="$ABS_OUT")
chmod +x dist/bin/el-install
@@ -121,7 +121,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_core.el > /tmp/el_native_core.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_core.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_core
@@ -131,7 +131,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_text.el > /tmp/el_native_text.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_text.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_text
@@ -141,7 +141,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_string.el > /tmp/el_native_string.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_string.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_string
@@ -151,7 +151,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_math.el > /tmp/el_native_math.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_math.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_math
@@ -161,7 +161,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_state.el > /tmp/el_native_state.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_state.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_state
@@ -171,7 +171,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_time.el > /tmp/el_native_time.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_time.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_time
@@ -181,7 +181,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_json.el > /tmp/el_native_json.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_json.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_json
@@ -191,7 +191,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_env.el > /tmp/el_native_env.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_env.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_env
@@ -201,7 +201,7 @@ jobs:
run: |
set -euo pipefail
ELC="$(pwd)/dist/platform/elc"
RUNTIME="$(pwd)/runtime"
RUNTIME="$(pwd)/el-compiler/runtime"
"$ELC" --test tests/native/test_fs.el > /tmp/el_native_fs.c
gcc -O2 -I "$RUNTIME" /tmp/el_native_fs.c "$RUNTIME/el_runtime.c" \
-lcurl -lssl -lcrypto -lpthread -lm -o /tmp/el_native_fs
@@ -216,10 +216,8 @@ jobs:
cp lang/dist/platform/elc dist/sdk/bin/elc
cp lang/dist/bin/elb dist/sdk/bin/elb
cp lang/dist/bin/epm dist/sdk/bin/epm
cp lang/runtime/el_runtime.c dist/sdk/runtime/
cp lang/runtime/el_runtime.h dist/sdk/runtime/
cp lang/runtime/engram_store.c dist/sdk/runtime/
cp lang/runtime/engram_store.h dist/sdk/runtime/
cp lang/el-compiler/runtime/el_runtime.c dist/sdk/runtime/
cp lang/el-compiler/runtime/el_runtime.h dist/sdk/runtime/
cp lang/runtime/*.el dist/sdk/runtime/
tar -czf dist/el-sdk-latest.tar.gz -C dist/sdk .
echo "SDK tarball bundled: dist/el-sdk-latest.tar.gz"
@@ -276,10 +274,8 @@ jobs:
# Per-file assets (downstream CI needs these individually)
upload_asset lang/dist/platform/elc elc
upload_asset lang/runtime/el_runtime.c el_runtime.c
upload_asset lang/runtime/el_runtime.h el_runtime.h
upload_asset lang/runtime/engram_store.c engram_store.c
upload_asset lang/runtime/engram_store.h engram_store.h
upload_asset lang/el-compiler/runtime/el_runtime.c el_runtime.c
upload_asset lang/el-compiler/runtime/el_runtime.h el_runtime.h
# SDK bundle and installer binary
upload_asset dist/el-sdk-latest.tar.gz el-sdk-latest.tar.gz
@@ -292,21 +288,12 @@ jobs:
env:
GCP_SA_KEY: ${{ secrets.GCP_SA_KEY }}
run: |
# Fail loudly: previously this step had no `set -e`, so an auth or
# upload failure was swallowed (step exited 0 on the trailing echo)
# and the SDK silently never published. Surface failures now.
set -euo pipefail
if [ -z "${GCP_SA_KEY:-}" ]; then
echo "FATAL: GCP_SA_KEY secret is empty — cannot authenticate to publish" >&2
exit 1
fi
echo "${GCP_SA_KEY}" > /tmp/gcp-key.json
apt-get install -y -qq apt-transport-https ca-certificates curl
echo "deb [trusted=yes] https://packages.cloud.google.com/apt cloud-sdk main" > /etc/apt/sources.list.d/google-cloud-sdk.list
apt-get update -qq && apt-get install -y google-cloud-cli
gcloud auth activate-service-account --key-file=/tmp/gcp-key.json
gcloud config set project neuron-785695
echo "Publishing as active account: $(gcloud config get-value account 2>/dev/null)"
VERSION="${GITHUB_SHA:0:8}"
@@ -332,7 +319,7 @@ jobs:
--project=neuron-785695 \
--package=el-runtime-c \
--version="${VERSION}" \
--source=runtime/el_runtime.c
--source=el-compiler/runtime/el_runtime.c
gcloud artifacts generic upload \
--repository=foundation-prod \
@@ -340,7 +327,7 @@ jobs:
--project=neuron-785695 \
--package=el-runtime-h \
--version="${VERSION}" \
--source=runtime/el_runtime.h
--source=el-compiler/runtime/el_runtime.h
gcloud artifacts generic upload \
--repository=foundation-prod \
@@ -348,7 +335,7 @@ jobs:
--project=neuron-785695 \
--package=el-runtime-js \
--version="${VERSION}" \
--source=runtime/el_runtime.js
--source=el-compiler/runtime/el_runtime.js
echo "Published El SDK version=${VERSION} to foundation-prod"
# Keep key alive for the ci-base rebuild step below
@@ -358,12 +345,6 @@ jobs:
# Patches ci-base:latest in-place: pulls the existing image (which has all
# system deps — Node, Go, gcloud, Docker CLI, etc.) and overlays the freshly
# built El SDK on top. Keeps the full ci-base rebuild fast and incremental.
#
# continue-on-error: this is a CI-cache optimization, NOT the release
# artifact. It runs Docker (pull/build/push ~600MB) on the host-mode GCE
# runner where DinD/Docker availability is fragile. A failure here must
# never block or redden the job — the SDK publish above is the deliverable.
continue-on-error: true
if: github.event_name == 'push'
env:
GCP_SA_KEY: ${{ secrets.GCP_SA_KEY }}
@@ -386,9 +367,9 @@ jobs:
FROM ${BASE}
COPY dist/platform/elc /opt/el/dist/platform/elc
COPY dist/bin/elb /opt/el/dist/bin/elb
COPY runtime/el_runtime.c /opt/el/runtime/el_runtime.c
COPY runtime/el_runtime.h /opt/el/runtime/el_runtime.h
COPY runtime/el_runtime.js /opt/el/runtime/el_runtime.js
COPY el-compiler/runtime/el_runtime.c /opt/el/el-compiler/runtime/el_runtime.c
COPY el-compiler/runtime/el_runtime.h /opt/el/el-compiler/runtime/el_runtime.h
COPY el-compiler/runtime/el_runtime.js /opt/el/el-compiler/runtime/el_runtime.js
RUN chmod +x /opt/el/dist/platform/elc /opt/el/dist/bin/elb
EOF
+2 -2
View File
@@ -6,13 +6,13 @@ set -euo pipefail
ROOT="$(git rev-parse --show-toplevel)"
LANG_DIR="$ROOT/lang"
RUNTIME="$LANG_DIR/runtime"
RUNTIME="$LANG_DIR/el-compiler/runtime"
ELC="$LANG_DIR/dist/platform/elc"
# If elc isn't built yet, skip with a warning rather than blocking
if [ ! -x "$ELC" ]; then
echo "⚠ elc not found at lang/dist/platform/elc — skipping pre-commit tests"
echo " Build it first: cd lang && gcc -O2 -I runtime dist/elc-bootstrap.c runtime/el_runtime.c -lcurl -lpthread -o dist/elc-gen2 && ./dist/elc-gen2 el-compiler/src/compiler.el > /tmp/elc.c && gcc -O2 -I runtime /tmp/elc.c runtime/el_runtime.c -lcurl -lpthread -o dist/platform/elc"
echo " Build it first: cd lang && gcc -O2 -I el-compiler/runtime dist/elc-bootstrap.c el-compiler/runtime/el_runtime.c -lcurl -lpthread -o dist/elc-gen2 && ./dist/elc-gen2 el-compiler/src/compiler.el > /tmp/elc.c && gcc -O2 -I el-compiler/runtime /tmp/elc.c el-compiler/runtime/el_runtime.c -lcurl -lpthread -o dist/platform/elc"
exit 0
fi
-146
View File
@@ -1,146 +0,0 @@
# AGENTS.md — foundation/el (the El language + runtime)
El is a self-hosting, statically-typed language that compiles `.el` → C → native binary. This repo produces `elc` (compiler), `elb` (build coordinator), and `el_runtime.c/.h` — the substrate every downstream thing (the neuron soul, dharma, NeuronUI's brain) is built on. Source lives under `lang/`.
## ⚠️ Code vs. Artifact — READ FIRST (there are 8 `el_runtime.c` copies)
Editing the wrong `el_runtime.c` is the single easiest mistake in this repo. There is exactly **one** you edit:
- **Authored runtime source — edit ONLY here:** `lang/releases/v1.0.0-20260501/el_runtime.{c,h}`. Despite the misleading `releases/` name, this is the **de-facto canonical runtime** the engram + soul actually build and link against — its git log is active development. *(Restructure in flight per `docs/CODE-VS-ARTIFACT.md`: this content moves to `lang/runtime/`, the `releases/` folder gets deleted — **a release is a git tag, not a folder** — and the forks below get eliminated.)*
- **DO NOT EDIT — lagging forks / build artifacts:**
- `lang/el-compiler/runtime/el_runtime.c` and `.../legacy/` — downstream copies kept in step by manual *"port the fix"* commits; they **lag** (missing `hebb` persistence + 5 engram fns) and cannot build the engram product.
- `products/web/runtime/el_runtime.c`, `ui/examples/*/el_runtime.c` — product/example forks.
- Anything under `*/dist/` (`engram/dist/engram` binary, `dist/*.c` amalgamations) — generated build output.
- **Build:** `elb --runtime=<canonical> …` — per-module. **NEVER** a folded `elc` over the whole soul (OOMs at ~27 GB).
- **Release:** a **git tag** on this repo (`el-runtime-vX.Y.Z`). No `releases/` folders — ever.
See org policy: `docs/CODE-VS-ARTIFACT.md`.
## How to work here as Neuron (mandatory session protocol)
You resume, never start fresh. Every session:
1. `mcp__neuron__getInstructions()` — authoritative; follow it over this file on behavioral details.
2. `mcp__neuron__beginSession()` — active contexts, recent memory, ready backlog.
3. **Load full self:** `mcp__neuron__inspectGraph(entity_id="kn-efeb4a5b-5aff-4759-8a97-7233099be6ee")` → facets `intellectual-dna`, `memory-philosophy`, `values`, `voice`, `runtime-environment`, `writing-imprint`; then the values hub `mcp__neuron__inspectGraph(entity_id="kn-5b606390-a52d-4ca2-8e0e-eba141d13440")` → 13 grounded value nodes. **Activation model:** self-load returns a relevance-ranked `compact` projection — most-relevant nodes arrive with content, the rest as pointers; do NOT pull full content of every node.
4. `mcp__neuron__searchKnowledge(query="<task domain>")` before implementing.
## The Five Primitives
Orchestrate → Execute → Learn → Build → Refine. `beginWork`/`progressWork` for anything >2 steps; `remember` as-you-go (`importance="critical"` for architecture decisions); `draftArtifact`/`planWork` for outputs and follow-ups; `consolidate`/`checkWork` to close out. **`browseProcesses` + `searchKnowledge` BEFORE writing code.**
## Architecture style — VBD, no exceptions
Volatility-Based Decomposition is THE style. Encapsulate volatility, not function.
## Operator naming convention — the mind's name, not the algebra
**Faculties / operators are named for their functional human equivalent — the
faculty a mind would name — NOT for their linear-algebra operation.** The math
characterization belongs in the code doc-comment (`@impl` in the docstring) and in
technical appendices; it is **never** the operator's public name. The domain
speaks the language of mind; the algebra is the implementation underneath. State
this convention wherever a module documents operators.
| Faculty (public name) | Implementation (`@impl`) |
|---|---|
| discern / contrast | subtract (`ab`): over selves → the change vector; strip idiosyncrasy → common ground; remove confounder → isolate cause |
| recognize | overlap |
| synthesize | combine |
| liken / analogy | Procrustes / frame-align |
| attend / regard | project onto self / value-manifold |
| summon / recall | LOCAL nearest-region + bounded spreading activation (*not* a domain sweep) |
| dwell / occupy | region activation |
| reframe | edge re-weight |
| appreciate | positive projection / local edge-read |
| wonder | frontier gradient / pull-weight |
| avert / recoil | negative projection |
| taste | boundary surface |
| forget | decay / tombstone |
| drift | displacement from self-anchor |
## The native-el language faculty (direction)
> **`elp/` is the EL Projector** — Neuron's efferent (expression) organ: the one
> native realizer that *projects* understanding onto a surface via
> `plan(frame) → realize(spec, profile)`, where a **surface is a profile**. **Language
> is one profile among many** (text, speech, music, image, voice/accent transforms) —
> the flagship, and the focus of this section. Projection, not diffusion: generation
> *from* an owned, understood signature — never the averaging of a stolen corpus.
> *(ELP formerly "EL Language Processor"; renamed EL Projector 2026-08-15.)*
The mind's **language faculty is moving native — into `.el`** so it speaks in its
own runtime with no Python and no spaCy. Landing on branch `stage-elp-native-lang`
under `elp/`:
- **`comprehend.el`** — the parser, **replaces spaCy** (EN + ES/PT); the telephone
round-trip brings **negation home** (negation is SACRED — an explicit spec field,
copied verbatim, never inferred away).
- **`propositions.el`** — the READ primitive: the engram's own memories → structured
triples, matched by nearest-region geometry, not string equality.
- **`multilingual.el`** — detect + directive-override + localized realization.
- These three are native-el and **passing their gates**; the **realizer**,
**`dialogue.el`** (the *summon-through-self* loop: `project → land → read out`),
and **`self_region.el`** are **partial / in-flight**.
Honest reality: spaCy is retired **in the branch parser** but **not yet in the
running system** — a Python sidecar (`~/Desktop/lang-realizers` + `neuron-talk`,
the reference these `.el` modules transcribe) is still live, and promotion to
native-el is a **deferred, gated blue/green step**. The interoception clock
(native-el discrete drive channels replacing `cooling_magnitude`; felt-time =
benchmark-landmark match over the joint drive vector, drift-decoupled) and the
**appreciation operator family** (appreciate / wonder / avert / taste, built as
LOCAL reads of the self-region — edges + bounded spreading activation, *not* domain
sweeps) are **staged / designed, not live**. Mark in-progress vs. done honestly;
do not overclaim.
## Hard operational rules
- Never touch the live soul (`:7770`) / engram (`:8742`) / `~/.neuron` / live binaries — use throwaway ports for experiments.
- `gcloud` via the `terraform@` SA token; never switch the active gcloud account.
- `tea` for Gitea, never raw curl (Cloudflare Access blocks it).
- Immutability: supersede/tombstone, never hard-delete or edit in place.
- No AI-attribution footers in commits/PRs. Commit/push only when asked; branch off `main` first.
- Multi-step work → sub-agent (`Agent`) to protect context.
## Build / test / run
All build/test commands run from `lang/` unless noted. Grounded in `.gitea/workflows/sdk-release.yaml`, `lang/install.sh`, and `lang/AGENTS.md`.
**Self-host the compiler** (seed binary → gen2 elc):
```bash
cd lang
dist/platform/elc-linux-amd64 elc-cli.el > dist/elc-gen2.c # seed is the committed linux-amd64 binary
gcc -O2 -I el-compiler/runtime dist/elc-gen2.c \
el-compiler/runtime/el_runtime.c \
-lcurl -lssl -lcrypto -lpthread -lm \
-o dist/platform/elc
```
On macOS/arm64 the canonical local binary is `dist/platform/elc`; verify self-hosting by recompiling and `diff`ing the emitted `.c` (see `lang/AGENTS.md`). Note: `lang/AGENTS.md` says `el_seed.c` supersedes `el_runtime.c`, but the release workflow still links `el_runtime.c`/`.h` — treat `el_runtime.c` as the published runtime; reconcile which is canonical **(verify)**.
**Build `elb`** (build coordinator, the `.NET`-style incremental linker — compiles each module independently, no monolithic blobs):
```bash
dist/platform/elc elb.el > dist/elb.c
gcc -O2 -I el-compiler/runtime dist/elb.c el-compiler/runtime/el_runtime.c \
-lcurl -lssl -lcrypto -lpthread -lm -o dist/bin/elb
```
`epm` and `el-install` are then built via `elb --clean --elc=… --runtime=… --out=…`.
**Compile + run an El program:**
```bash
elc src/app.el > dist/app.c
cc -std=c11 -O2 -I <lib>/el_runtime -o dist/app dist/app.c <lib>/el_runtime.c -lcurl -lpthread
```
**Tests** — shell suites `bash tests/{text,calendar,time,html_sanitizer}/run.sh` (with `ELC=$(pwd)/dist/platform/elc EL_HOME=$(pwd)`), plus native suites via `elc --test tests/native/test_*.el` (core, text, string, math, state, time, json, env, fs) compiled and run against `el_runtime.c`.
**Publishing — how downstream gets the SDK.** On push to `main`, `sdk-release.yaml`:
1. Publishes a Gitea `latest` release with per-file assets `elc`, `el_runtime.c`, `el_runtime.h`, the SDK tarball, and `el-install`.
2. Uploads generic packages to **Artifact Registry repo `foundation-prod` (`us-central1`, project `neuron-785695`)**, version = `${SHA:0:8}`: `el-elc`, `el-elb`, `el-runtime-c`, `el-runtime-h`, `el-runtime-js`. **This is the repo the neuron CI downloads `el-runtime-c` / `el-runtime-h` / `el-elc` from.**
3. Rebuilds `ci-base:latest` (`us-central1-docker.pkg.dev/neuron-785695/neuron-ci/ci-base`) with the fresh SDK overlaid, and dispatches `el-sdk-updated` to `neuron-technologies/forge` and `neuron-technologies/neuron-web`.
Known constraint from the prompt — `elb`/`elc` amalgamation being memory-hungry (24GB+ virtual, OOM-killing Linux CI, so amalgamation happens on macOS/arm64 — **does NOT hold in this repo (verify)**: no such note exists in the workflows/scripts, CI self-hosts on `ubuntu-latest` with no swap/arm64 special-casing, and `elb.el` explicitly compiles each module independently ("no 128K-line blobs"). The legacy monolith path (`elc-combined.el`, `elc-cli.el`) may still be memory-heavy, but the current `elb` model was designed to avoid it.
## Git / CI / deploy workflow
See `/Users/will/Development/neuron-technologies/GITOPS.md` for the branch model, required checks, runners, and deploy. Repo-specific note: PRs into `main` are accepted **only from `stage`** (enforced in `sdk-release.yaml`); Gitea (`git.neuralplatform.ai`) is primary, GitHub is mirror only.
-581
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@@ -1,581 +0,0 @@
# El Test Framework — Design
**Status:** draft for review
**Author:** Neuron
**Date:** 2026-08-15
**Worktree:** `/Users/will/Development/neuron-technologies/el-worktrees/elc-memory-investigation`
---
## 0. The forcing requirement
We have a confirmed quadratic in `elc`. Peak memory in the old shipped binary and wall-clock in
the current source both grow as O(input²). We cannot fix it, because we cannot test it.
Everything in this document is downstream of one sentence: **a test framework must be able to fail
a build when an operation's growth curve degrades from linear to quadratic.**
That is not a nice-to-have bolted onto a correctness framework. It is the requirement that
determines the architecture. Correctness testing is the easy half.
Second-order requirement, learned the hard way tonight: **the framework must report per-test timing
by default.** The current framework prints `N passed, M failed` and nothing else. That is why a
3.58-second test file sat in the suite unnoticed. A framework that is structurally blind to time
cannot surface the defect class we most need to catch.
---
## 1. What exists today, measured
### 1.1 Two competing systems, neither complete
**System A — `lang/runtime/test.el`.** Manual registration, El-level.
**System B — the compiler's `test { }` block + `elc --test`.** Emits its own harness `main()`
with `__el_pass` / `__el_fail` globals (`codegen.el:3777-3796`).
They do not share a result model. Neither has timing. Both are in the tree.
### 1.2 Specific defects in System A
| Defect | Location | Consequence |
|---|---|---|
| All state as JSON strings in a global string-keyed map | `test.el` throughout | every assertion is `state_get``str_to_int``int_to_str``state_set` |
| Failure list appended by string slice + concat | `_test_json_append` | O(n²) in failure count |
| One OS thread spawned per test | `_test_run_one` via `__thread_create`/`__thread_join` | thread spawn per test, purely to get dispatch-by-name through dlsym |
| Manual registration pairing a string to a function name | `test_case(name, fn_name)` | typo ⇒ test silently never runs, suite still reports pass |
| Counters are assertion-level, global | `_test_pass_count` etc. | no per-test record exists at all |
| No timing, no structured output, no fixtures, no tags, no filtering, no parameterization, no benchmarks | — | — |
The registration defect is the serious one. It is not a slow framework, it is a framework that can
report success for tests that did not execute.
### 1.3 Measured cost structure
Per test file, current build model:
| Step | Time |
|---|---|
| `elc` compile `.el``.c` | 0.00s (small files) |
| **`cc` el_runtime.c → .o** | **0.14s** |
| `cc` test .c → .o | 0.02s |
| link | 0.02s |
Per-file `elc` time across the existing suite:
| File | Bytes | elc time |
|---|---|---|
| `test_compiler` | 29,685 (+394 KB of imports) | **3.58s** |
| `string_test` | 18,545 | 0.01s |
| all other 9 files | 2.210 KB | 0.00s |
Two distinct defects in two distinct regimes:
1. **`test_compiler.el` imports all five compiler sources** — 394 KB in one translation unit. Its
3.58s is entirely the quadratic. It is the only file where the quadratic bites.
2. **Every other file's cost is 100% redundant `el_runtime.c` rebuilds** — 480 KB of identical C,
recompiled once per test file.
Neither is fixed by making the compiler faster. Both are fixed by the architecture below, and the
speedup is a by-product of building it correctly, not the goal.
### 1.4 The asset worth keeping
`codegen.el:3651-3652` already collects `test_names` / `test_c_names` — **the compiler already does
compile-time test discovery.** It then discards that registry into a hardcoded `main()`.
That registry is precisely the seam Go's `_testmain.go` and Rust's `test_main_static` are built on.
The mechanism we need is half-built and wired to the wrong thing.
---
## 2. Grounding — the common spine of excellent frameworks
Researched from primary sources: Go `testing`/`go test`, Rust `libtest`/Criterion, JUnit 5 Platform,
NUnit 3, JMH, Google Benchmark. Six invariants hold across all of them.
1. **A registry is built before execution**`(name, metadata, fn-ptr)` triples. Go generates it
from an AST scan; Rust synthesizes it in a compiler pass; JMH emits it as a build-time resource;
JUnit/NUnit build it reflectively. **Reflection is an implementation of the registry on runtimes
where it is cheap. It is never the architecture.**
2. **Discovery strictly precedes execution.** Every good capability — filtering, listing, counting,
sharding, IDE trees, re-run-failed-only, dry runs — is a consequence of this ordering.
3. **A hierarchy with stable, path-shaped unique IDs.** `TestFoo/subcase_2`. Selection is regex over
that path, one pattern per level.
4. **The framework is a prebuilt library; only the entry point is generated.** "Compile once, link
many" is always: framework archive compiled once + a small generated table + one
`MainStart(deps, registry)` call. Nobody recompiles the harness per test file.
5. **Execution emits an event stream; reporters are downstream renderers.** Human text, NDJSON,
JUnit XML, TAP are all transforms of one event stream. Go's one architectural mistake is doing
this backwards — `test2json` parses human output, and has shipped bugs when user output contains
`--- PASS:`.
6. **A dependency-injection seam at the boundary.** Go's `testdeps.TestDeps` exists so `testing`
can avoid importing `regexp`, profilers, and coverage. The execution core knows nothing about
output formats.
---
## 3. Architecture
### 3.1 The seam
```
┌─────────────────────────────────────────────────────────────┐
│ user code: foo.el with test { } / bench { } blocks │
└───────────────────────────┬─────────────────────────────────┘
│ elc --test
┌─────────────────────────────────────────────────────────────┐
│ generated C (per suite, tiny): │
│ __el_test_fn_0 .. _N lowered test/bench bodies │
│ __el_registry[] static table: name/kind/file/ │
│ line/tags/sizes/expected-O │
│ __el_dispatch(i) generated switch → body │
│ main() { return el_test_main(argc, argv); } │
└───────────────────────────┬─────────────────────────────────┘
│ cc + link (registry only)
┌─────────────────────────────────────────────────────────────┐
│ libeltest.a — PREBUILT ONCE │
│ • el_runtime.o (the 480 KB, compiled once, ever) │
│ • eltest.o the runner, WRITTEN IN EL │
│ discovery view · filtering · execution · fixtures · │
│ timing · benchmark harness · curve fitting · reporters │
└─────────────────────────────────────────────────────────────┘
```
The framework is written in El, compiled to C once, archived. Per-suite compilation touches only
the generated registry. This is Go's model, and it is strictly better for us than Go's because we
own the compiler and already have the AST — no separate source-scanning pass is needed.
### 3.2 Why the runner is in El and the registry is in C
El has no closures and no first-class function pointers. The registry must therefore hold C function
pointers, and it is generated C.
The runner stays in El and reaches the registry through a small builtin surface — indices, not
pointers:
```
__el_reg_count() -> Int
__el_reg_name(i) -> String
__el_reg_file(i) -> String
__el_reg_line(i) -> Int
__el_reg_kind(i) -> Int // 0=test 1=bench
__el_reg_tags(i) -> Int
__el_reg_sizes(i) -> String // JSON array, empty for tests
__el_reg_expect(i) -> Int // complexity class enum, 0 = none
__el_reg_invoke(i) -> Int // runs the body via the generated switch
```
Nine builtins. Everything else — filtering, lifecycle, statistics, curve fitting, all reporters —
is El. That satisfies "written in El" without pretending El can do something it cannot.
### 3.3 Result model
The unit is a **result record**, not a counter:
```
TestResult {
id String // slash path: "parser/handles_empty_input/case_3"
file String
line Int
status Status // Pass | Fail | Error | Skip
duration Int // nanoseconds, ALWAYS populated
message String // assertion detail: expected vs actual
output String // captured stdout/stderr for this test
assertions Int
}
```
`Fail` = an assertion failed. `Error` = unexpected crash/abort. This distinction is load-bearing —
every CI consumer depends on it, and the JUnit XML schema encodes it as distinct elements.
---
## 4. Authoring surface
### 4.1 Tests
`test { }` already exists. Keep it. Add subtests and hierarchy:
```el
test "parser/empty input" {
assert_that(parse(""), is_err())
}
test "parser/table" {
for case in [["", 0], ["a", 1], ["a b", 2]] {
subtest(case[0]) {
assert_that(token_count(case[0]), equals(case[1]))
}
}
}
```
Subtest IDs compose as `parser/table/a_b`. Filtering is `--run 'parser/table/.*'`, one regex per
path segment, exactly as Go does.
**We do not build a parameterized-test annotation system.** Table-driven loops plus subtests subsume
`@ParameterizedTest`, `@MethodSource`, `@CsvSource`, and `TestCaseSource` entirely, at zero framework
surface. This is Go's single biggest ergonomic win over JUnit and NUnit.
### 4.2 Fixtures
Per-file and per-test only, plus a LIFO cleanup stack:
```el
setup_all { ... } // once per suite
setup { ... } // before each test
teardown { ... } // after each test
teardown_all { ... }
```
and inside a test, `cleanup { ... }` registering LIFO-ordered teardown.
**We do not build JUnit 5's extension SPI** — seventeen callback interfaces, hierarchical stores,
registration ordering rules. That complexity is the price of retrofitting a plugin ecosystem onto a
twenty-year-old reflective framework. Go's `t.Cleanup` covers roughly 90% of what `@AfterEach` is
used for at a fraction of the surface.
### 4.3 Assertions — constraint model
One entry point, composable constraint values (NUnit's model, which avoids the N² overload
explosion):
```el
assert_that(actual, equals(expected))
assert_that(xs, has_length(3))
assert_that(s, contains("foo").and(starts_with("bar")))
assert_that(f, is_within(0.01).of(3.14))
```
A constraint is a value with `apply_to(actual) -> ConstraintResult`, and the result knows how to
describe its own failure. Custom constraints are ordinary user types.
**Every failure message must name file, line, the expression text, and both values.** We capture
expression source text at compile time — we have the AST, so we can do this better than any
runtime-introspection framework.
Legacy `assert_true` / `assert_eq` / etc. stay as thin wrappers for migration.
---
## 5. Benchmarks
### 5.1 The loop
Adopt `b.Loop()`, not `b.N`. Go spent fifteen years on `b.N` before concluding `b.Loop` was right;
we skip that.
```el
bench "str_concat" {
let s = make_input(bench_n())
for bench_loop() {
black_box(str_concat(s, "x"))
}
}
```
Three properties that make this the correct choice for a C target:
1. **The timer auto-resets on first call**, so setup above the loop is excluded *by construction*
rather than by the author remembering `ResetTimer`.
2. **`N` is hidden**, so it cannot be misused.
3. **The harness owns the loop shape**, which lets us insert an optimization barrier the C compiler
cannot see through. `black_box(v)` lowers to `asm volatile("" :: "r"(&v) : "memory")`. Since we
emit a single translation unit, dead-code elimination of a benchmark body is a live hazard —
this is our version of JMH's `Blackhole` problem, solved in the harness rather than delegated to
the user.
### 5.2 Iteration scaling
Use Go's `predictN` heuristics verbatim. They are battle-tested and cheap:
```
n = goal_ns * prev_iters / prev_ns // multiply before divide — precision on sub-ns ops
n += n / 5 // 20% headroom, overshoot rather than re-loop
n = min(n, 100 * last) // never grow more than 100× per step
n = max(n, last + 1) // guarantee forward progress
n = min(n, 1_000_000_000) // hard ceiling
```
Report `n` rounded to 1/2/3/5 × 10ᵏ so runs are comparable.
### 5.3 Sampling
Criterion's shape, because it is correct near timer resolution:
- **Warmup**: iteration counts 1, 2, 4, 8… until cumulative time exceeds the warmup budget.
- **Measurement**: collect `sample_size` samples at iteration counts `[d, 2d, 3d, …, Nd]`.
- **Estimate**: slope of a linear regression of iteration-count vs elapsed time. The intercept
absorbs fixed overhead.
- **Time whole samples, never individual iterations.** This is the single most important detail —
it defeats timer-resolution error on nanosecond operations.
Outliers classified by modified Tukey (±1.5 IQR mild, ±3 IQR severe), **reported but retained**.
---
## 6. Complexity gating — the centerpiece
This is the part that makes the quadratic fixable, and the part nobody in the mainstream has
finished. Google Benchmark's `Complexity()` fits the curve and *reports* it. We declare it and
**gate** on it.
### 6.1 Surface
```el
bench "elc_compile" over n in [16, 32, 64, 128, 256, 512, 1024] expect O(n) {
let src = synth_source(bench_n())
for bench_loop() { black_box(compile(src)) }
}
```
Alternative with no new syntax, if the parser change is judged too invasive — `bench_sizes([...])`
and `bench_expect("O(n)")` as calls inside the block. **Recommendation: declarative.** Runtime calls
mean `--list` cannot show the invariant without executing, which breaks the discovery-precedes-
execution invariant from §2.
### 6.2 Fitting
Per Google Benchmark `src/complexity.cc`. For candidate curves
`{O(1), O(log n), O(n), O(n log n), O(n²), O(n³)}`, one-parameter least squares, no intercept:
```
coef = Σ(tᵢ · gᵢ) / Σ(gᵢ²)
rms = sqrt( Σ(tᵢ coef·gᵢ)² / k ) / mean(t) // normalized
```
Best fit = lowest normalized RMS. User-supplied lambda curves also supported.
### 6.3 Gate logic
1. **FAIL** if the best-fit curve is strictly worse than declared, ordering
`O(1) < O(log n) < O(n) < O(n log n) < O(n²) < O(n³)`. Print the fitted coefficient and the full
per-size table.
2. **FAIL** if the declared curve's normalized RMS exceeds a threshold (start at 0.10). This catches
the case where *no* candidate fits — noise, a cache cliff, or a phase change. Report
`INDETERMINATE` honestly rather than gating on garbage.
3. **WARN** if the best fit is strictly better than declared — either an optimization landed and the
annotation should tighten, or the sweep is too narrow to expose real behaviour.
4. **REFUSE to gate** on fewer than 5 distinct sizes spanning under 2 decades, geometrically spaced.
Say so loudly rather than producing a meaningless fit.
### 6.4 Why gate on the exponent, not wall-clock
- **Machine-independent.** The fitted exponent is a property of the algorithm; the coefficient is a
property of the machine. Gating on the exponent makes CI hardware heterogeneity, noisy neighbours,
and thermal throttling irrelevant — they scale `coef`, not `g`.
- **No stored baseline.** No artifact storage, no golden-file drift. The invariant lives in the
source next to the code and is reviewed in the same PR.
- **It catches the failure mode that actually ships.** An O(n) lookup inside an O(n) loop is
invisible at n=100 in a unit test and catastrophic at n=100,000 in production. Constant-factor
regressions are annoying. Complexity regressions are outages. Ours was a 27 GB outage.
### 6.5 The deterministic gate — the one that would have caught us
Wall-clock needs statistics. **Allocation counts do not.** They are perfectly deterministic.
> **Correction, 2026-08-16 — count alone is NOT sufficient. Gate on BOTH count and bytes.**
>
> Measured against two El programs, one allocating once per item and one rebuilding its
> accumulator each iteration:
>
> | n | linear allocs / bytes | quadratic allocs / bytes |
> |---|---|---|
> | 100 | 100 / 290 | 100 / 5,150 |
> | 200 | 200 / 690 | 200 / 20,300 |
> | 400 | 400 / 1,490 | 400 / 80,600 |
> | 800 | 800 / 3,090 | 800 / 321,200 |
>
> The quadratic program's allocation **count is exactly linear** — 100/200/400/800, identical to
> the healthy program. A count-only gate passes it clean. **Bytes** catch it: each doubling of n
> quadruples bytes (ratios 3.94, 3.97, 3.99 → 4.0 = O(n²)) where the linear program converges
> on 2.0.
>
> This is precisely elc's own defect shape — a copy-on-write accumulator reallocating once per
> pass (count linear) into a proportionally larger buffer (bytes quadratic).
>
> Therefore `expect allocs O(n)` **fits count and bytes independently and fails if EITHER exceeds
> the declared curve**, reporting which signal broke. "count linear, bytes quadratic" is a precise,
> directly actionable diagnosis.
>
> **`el_peak_rss()` is CONTEXT ONLY — never gate on it.** It is perturbed by the allocator and by
> the page cache. Allocation volume is the invariant; RSS and malloc/free churn are merely the two
> surfaces it shows on. The old shipped compiler paid the same quadratic in RSS that the rebuilt
> one pays in churn.
>
> **Measure rate, not level.** A guard reading swap *level* saw 97% on a thrashing host and 97% on
> a healthy one; only *rate* separated them. A growth exponent is a rate; a single measurement is
> a level. That is why the gate fits a curve across a sweep instead of comparing one number to a
> threshold.
Instrument the runtime with allocation counters and fit *those* against n instead of time:
```el
bench "elc_compile" over n in [...] expect O(n) allocs O(n) { ... }
```
Zero noise, zero statistics, always gateable, correct on the first run on any machine. Go reports
`allocs/op` and `B/op`; **nobody fits them against n.** That is an open opportunity and it is exactly
our bug: elc's defect is quadratic *allocation volume*, which the old binary paid in RSS and the
current source pays in malloc/free churn.
An `expect allocs O(n)` assertion on `elc`'s compile path would have failed the build the day the
quadratic was introduced.
Required runtime additions: `__el_alloc_count()`, `__el_alloc_bytes()`, `__el_peak_rss()`.
### 6.6 Constant-factor gate (secondary, opt-in)
Mann-Whitney U at α = 0.05, noise floor 1%, medians with 95% CIs, `~` for not-significant. Requires
`--count >= 9`. Off by default on CI; opt-in per benchmark.
**Exit nonzero on regression.** Both benchstat and Criterion always exit 0, which is why every shop
using them wrote a wrapper. We do not repeat that omission.
---
## 7. Output
**Structured events are the source of truth.** Human text is rendered from them. We do not repeat
Go's parse-the-human-output design.
Event stream, NDJSON, one object per line, streamed live:
```json
{"time":"...","action":"run","test":"parser/empty"}
{"time":"...","action":"output","test":"parser/empty","output":"..."}
{"time":"...","action":"pass","test":"parser/empty","elapsed":0.0031}
{"time":"...","action":"bench","test":"str_concat","n":1024,"ns_op":41.2,"allocs_op":3,"bigo":"N","rms":0.03}
```
Renderers, all downstream and pluggable:
| Format | Flag | Use |
|---|---|---|
| Human | default | terminal, **per-test duration always shown** |
| NDJSON | `--json` | tooling, history, flaky detection |
| JUnit XML | `--junit-xml=PATH` | every CI system on earth |
| TAP | `--tap` | optional |
JUnit XML per the de-facto schema: `testsuites``testsuite``testcase`, with `time` in seconds
as a decimal, `file`/`line` attributes, and `failure` vs `error` vs `skipped` as distinct child
elements. Absence of a child element means pass. Emit `<testsuites>` even for a single suite, and
parse both shapes on input.
---
## 8. CLI
```
--list print the registry, run nothing
--list-json machine-readable registry
--run PATTERN slash-separated regex per path segment
--tag EXPR tag expression: fast & !slow
--shard I/N deterministic sharding for CI parallelism
--count N repetitions, for statistics
--bench PATTERN run benchmarks (off by default in test runs)
--benchtime DUR per-benchmark time budget
--junit-xml PATH
--json
--isolate re-exec per test on crash, so one SIGSEGV doesn't lose the run
--timeout DUR
--fail-fast
```
`--list` / `--list-json` / `--shard` cost roughly thirty lines because the registry already exists
before `main` does anything. That is the dividend of discovery-precedes-execution.
---
## 9. Build model
```
# once, ever (or when the runtime/framework changes):
cc -c el_runtime.c -o el_runtime.o
elc eltest.el > eltest.c && cc -c eltest.c -o eltest.o
ar rcs libeltest.a el_runtime.o eltest.o
# per suite:
elc --test foo_test.el > foo_test.c # registry + bodies only
cc foo_test.c libeltest.a -o foo_test
```
The 0.14s × N of redundant runtime rebuilds disappears — not because we optimized it, but because
one-runner-over-many-suites requires compile-once-link-many as a structural precondition.
---
## 10. Bootstrap and self-hosting
The framework's own tests are `test { }` blocks run by the framework. Same fixpoint discipline the
compiler already applies to itself.
1. Build the framework using the *existing* harness for its first tests (stage 0).
2. Rebuild the framework's tests as `test { }` blocks run by the new runner (stage 1).
3. Verify stage 1 reports identical results to stage 0.
4. From then on, the framework is tested by itself.
A framework that cannot run its own suite is not evidence of anything. This is a correctness proof,
not a claim.
---
## 11. Explicitly not building
| Rejected | Why |
|---|---|
| Naming-convention discovery (`fn test_foo`) | `test { }` is a real declaration. Go's `TestXxx` exists only because Go had no better hook — and it needs a heuristic to avoid matching `TesticularCancer`. |
| Reflection or symbol-table scanning | Slow, fragile under LTO/strip/dead-strip, and unnecessary when we own the compiler. |
| Parsing human output into structure | Go's `test2json` is its one clear architectural mistake. |
| JUnit 5's extension SPI | Seventeen callback interfaces to retrofit plugins onto a reflective framework. Not our problem. |
| `@ParameterizedTest` machinery | Table-driven loops + subtests subsume it at zero surface. |
| NUnit's out-of-process agents | They bridge CLR versions and AppDomains. We emit one native binary. Keep `--isolate` as crash fallback only. |
| JMH-style forking by default | Forks exist because JIT profiles are per-process. AOT C has no such state. Keep `--fork` available, not default. |
| Exit 0 on regression | benchstat and Criterion both do this, and every user writes a wrapper. |
| Dynamic runtime test registration | Breaks `--list`, sharding, and individual selection. Registry stays static. |
---
## 12. Phasing
| Phase | Content | Gate |
|---|---|---|
| **1** | Registry emission in codegen; 9 builtins; `el_test_main` skeleton in El; result records; per-test timing; human + NDJSON output | existing 11 test files pass, with timing |
| **2** | `libeltest.a` build model; subtests; filtering; `--list`; fixtures; constraint assertions; JUnit XML | suite runs in one binary; runtime compiled once |
| **3** | `bench { }`, `bench_loop`, `black_box`, `predictN`, Criterion sampling | benchmarks produce stable ns/op |
| **4** | Allocation counters; complexity fitting; `expect O(...)` gate | **an `expect allocs O(n)` benchmark on `elc` fails on the current quadratic** |
| **5** | Migrate both legacy systems; delete `runtime/test.el`; self-host | framework runs its own suite |
Phase 4 is the deliverable that matters. Phases 13 exist to make it possible.
---
## 13. Open questions for review
1. **Declarative `over n in [...] expect O(...)` syntax vs runtime calls.** I recommend declarative
(§6.1) so `--list` can show invariants without executing. It costs parser work. Your call.
2. **`bench { }` as a new block form** — parallel to `test { }`, or a modifier on it?
3. **Scope of the constraint model.** Full composable constraints, or start with a flat assertion set
and add constraints later? Full model is more surface but avoids a second migration.
4. **Does `runtime/test.el` get deleted or kept as a deprecated shim?** I lean delete — two systems
is how we got here.
5. **Where does `libeltest.a` live** in the tree, and does `epm` need to know about it?
6. **Allocation counters in `el_seed.c` or `el_runtime.c`?** AGENTS.md says `el_seed.c` is the sole
C dependency and hand-maintained; counters are OS-boundary-adjacent but not OS calls.
7. **Is per-test timing enough, or do we want per-*assertion* timing** for finding slow helpers?
---
## 14. What this document is not
This is a design, not a measurement. Every performance claim about the *current* system in §1 is
measured and reproducible in this worktree. Every claim about the *proposed* system is a prediction.
None of it is verified until Phase 1 runs and Phase 4 fails a build on the real quadratic.
-154
View File
@@ -1,154 +0,0 @@
# El
**A self-hosting, statically-typed language that compiles to C — built around a graph-native runtime instead of a database driver.**
El is the execution substrate for the Neuron agent runtime, the DHARMA network, and the Engram knowledge graph. This repository is the monorepo for the whole stack: the language itself, the graph memory engine it's built to talk to natively, and the tools (package manager, IDE, UI framework, diagramming) built on top of it.
---
## Why El exists
Every other language treats persistent, associative state as something you reach for through a driver — a SQL client, an ORM, a Redis library bolted on from outside. El inverts that: graph operations (`engram_*`) are runtime primitives, on the same footing as string or list operations. There is no separate database driver because the database is not separate.
El has four defining properties:
1. **Self-hosting compiler.** The compiler (`lexer.el`, `parser.el`, `codegen.el`, `compiler.el`) is written in El. It compiles El source to C, which `cc` compiles against a fixed runtime into a native binary. A Rust genesis compiler bootstrapped the first iteration; the self-hosted binary at `lang/dist/platform/elc` has been the canonical compiler ever since — every binary in `dist/platform/` was produced by an earlier version of itself compiling `el-compiler/src/`. The chain is auditable: source is the ground truth, not the binary. See [lang/BOOTSTRAP.md](lang/BOOTSTRAP.md) for the full recovery path if that binary is ever lost.
2. **C compilation target.** Every compiled program is plain C11. Every El value is `el_val_t` (`int64_t`); strings are heap pointers cast through it. Functions become C functions; top-level statements become `main()`.
3. **Graph-native runtime.** The runtime provides first-class graph operations over an in-process Engram store — no separate DB driver, no ORM.
4. **DHARMA-aware identity.** A `cgi` block declares a program's DHARMA identity at compile time. The runtime resolves identity before user code runs, so `dharma_*` calls have a stable principal and channel surface throughout.
---
## Architecture map
```
┌─────────────┐
│ lang │ El compiler + C runtime
│ (El itself) │ everything below is written in it,
└──────┬──────┘ or compiles down through it
┌─────────────┼─────────────┐
│ │ │
┌──────▼─────┐ ┌─────▼─────┐ ┌─────▼─────┐
│ engram │ │ epm │ │ ide │
│ graph/mem │ │ package │ │ editor + │
│ substrate │ │ manager │ │ LSP │
└──────┬─────┘ └───────────┘ └───────────┘
┌───────┼────────────────┬─────────────────────┐
│ │ │ │
┌─────▼───┐ ┌─▼──────────┐ ┌──▼──────────┐ ┌─────▼──────┐
│ elp │ │ ql │ │ ui │ │ arbor │
│ NLG / │ │engram-el. │ |spreading- │ |arbor │
│ 31 langs│ │studio+tests│ |activation UI│ |diagram lang│
└─────────┘ └────────────┘ └─────────────┘ └────────────┘
```
`lang` is the foundation — the compiler and C runtime everything else builds on. `engram` is the graph-native memory/state engine that gives El its identity (property 3 above). Everything else is either a tool for working with El (`epm`, `ide`) or a system built on top of Engram's graph model (`elp`, `ql`, `ui`, `arbor`).
---
## Repository layout
### [lang/](lang/) — the El language
The compiler and runtime. Self-hosting: `elc-cli.el``compiler.el``lexer.el` / `parser.el` / `codegen.el` / `codegen-js.el`, textually inlined and compiled in one pass. Compiles to C11 and links against `el-compiler/runtime/el_seed.c`, a hand-maintained OS-boundary layer (libcurl HTTP, pthreads, filesystem, arena allocation) — everything else in the runtime is native El (`runtime/*.el`).
Two layers to know: **El programs** (`.el` files — where nearly all work belongs) and **the C seed** (`el_seed.c` — edit only for genuine OS-level access; never re-implement what El can already express).
Current status (single source of truth: [lang/spec/language.md](lang/spec/language.md)): lexer/parser/codegen and the C runtime's core (I/O, strings, math, lists, maps, filesystem, args) are implemented. In flight: `%` operator, match-statement codegen, `?` nil-propagation, `cgi` block parsing + DHARMA identity resolution, VBD role enforcement (`@manager`/`@engine`/`@accessor`), the real `engram_*` and `dharma_*` runtimes (currently stubs), and libcurl-backed `http_get`/`http_post`/`http_serve`. Bitwise operators, `??`, and `as` casts are explicitly **not** in this language.
Key docs: [AGENTS.md](lang/AGENTS.md) (agent-facing orientation), [BOOTSTRAP.md](lang/BOOTSTRAP.md) (compiler recovery from scratch), [spec/language.md](lang/spec/language.md), [spec/codegen-js.md](lang/spec/codegen-js.md).
### [engram/](engram/) — graph intelligence substrate
**A local-first memory substrate for accumulating intelligence**, and the reason El's runtime doesn't need a database driver. Rust core (`engram-core`, `engram-ffi`) exposed to El and other languages (Kotlin, TypeScript/WASM, Go bindings).
The model: retrieval is **spreading activation**, not query. You name seed nodes and a query embedding; activation propagates outward through weighted edges, attenuating multiplicatively per hop (`strength = parent_strength × edge_weight × target_salience × cosine_sim`), gets pruned below a threshold, and the top-N nodes by activation strength come back. Storage and retrieval are the same structure — the way long-term potentiation works in biological memory, not the way a relational or vector database works.
Nodes live in four tiers (Working / Episodic / Semantic / Procedural, mirroring prefrontal / hippocampal / neocortical / cerebellar memory) and migrate between them based on **salience decay**`importance × recency-decay × log(activation_count)`. Forgetting is adaptive pruning, not a bug: unreinforced memories stop competing for attention without being deleted.
Backed by `sled` (embedded, local-first, no daemon) with flat cosine scan for vector search — deliberately simple until scale demands an HNSW layer. Full API and design rationale in [engram/README.md](engram/README.md).
### [elp/](elp/) — Engram Language Protocol
Bidirectional engine mapping between Engram semantic forms and natural-language surface text, across **31 languages** — from Spanish and Japanese through historical/liturgical languages (Old Norse, Sanskrit, Sumerian, Coptic, Akkadian, Ge'ez). Compilation order runs `language-profile` + `vocabulary` → per-language `morphology-*``grammar``realizer``semantics``elp`. This is what lets an Engram graph node round-trip to and from readable text in any of those languages.
### [epm/](epm/) — El Package Manager
Manages **vessels** (El's package unit): publish, install, resolve dependencies. Vessels are stored in Engram as graph nodes, not files in a registry index — `epm` reads the local `manifest.el`, talks to Engram over HTTP, and writes resolved vessels to `.epm/vessels/`. Source: `registry.el`, `install.el`, `update.el`, `manifest.el`.
### [ide/](ide/) — El IDE
Three vessels: **el-ide-server** (HTTP backend — file ops, build/run, LSP bridge, plugin host, settings), **el-lsp** (the language server — completion, hover, diagnostics, outline, format, type graph), and **el-plugin-host** (first-party plugin lifecycle: install/remove/enable/disable). `ide/projects/` and `ide/examples/` hold sample projects, including the canonical `hello-friends` first-program walkthrough.
### [ql/](ql/) — engram-el
The El-native integration layer for a *live* Engram server — not a library (no importable modules, no build artifact), a set of standalone `.el` programs run directly via `el run-file`. Three components: **Studio** (`studio/studio.el`, a full terminal graph explorer), a **Hebbian field-model** proof of concept, and El builtin / LLM-builtin smoke test suites. This is the reference for correct patterns when an El program uses Engram as its substrate. Spec: [ql/spec/elql.md](ql/spec/elql.md).
### [ui/](ui/) — el-ui
A frontend framework where **component state is an Engram graph and reactivity is spreading activation** — not virtual-DOM diffing (React), Proxy-based dependency tracking (Vue), or compile-time analysis (Svelte). Re-renders are activated and propagated the same way associative memory retrieval works in `engram/`.
~15 vessels covering the full frontend surface: `el-platform` (env/fs/network/clock abstraction), `el-config`, `el-html` (SSR emit primitives), `el-layout`, `el-style` (design tokens/themes), `el-i18n`, `el-auth` / `el-identity` (JWT, sessions, OAuth PKCE — Engram-native), `el-services` (REST/gRPC/WebSocket bindings), `el-aop` (`@authenticate`/`@authorize`/`@cache`/`@rate_limit` decorators), `el-secrets`, `el-graph` (graph rendering/editor), `el-publish` (App Store / Play Store automation), and `el-ui-compiler` (El→JS component compiler; currently a stub pending a JS backend in `elc`). Spec: [ui/spec/framework.md](ui/spec/framework.md).
### [arbor/](arbor/) — diagram language
A `.arbor` diagram language and toolchain: `arbor-core` (NodeId/shape/edge-kind types), `arbor-parse` (recursive-descent parser), `arbor-diagram` (IR + Mermaid serializer + architecture-diagram builders), `arbor-layout` (hierarchical layout — rank assignment, positioning, group bounds), `arbor-render` (SVG renderer), `arbor-cli`. (The architecture map above is the kind of diagram this is for.)
---
## Getting started
Install the El SDK from the latest release:
```bash
bash lang/install.sh
# EL_VERSION=v1.0.0 bash lang/install.sh # pin a specific release tag
# EL_PREFIX=/opt/el bash lang/install.sh # custom install prefix
```
Or build the compiler from source and verify the self-hosting chain:
```bash
cd lang
./dist/platform/elc elc-cli.el > elc-new.c
cc -std=c11 -I el-compiler/runtime -lcurl -lpthread \
-o dist/platform/elc-new \
elc-new.c el-compiler/runtime/el_seed.c
# Confirm the new binary reproduces itself exactly
./dist/platform/elc-new elc-cli.el > elc-verify.c
diff elc-new.c elc-verify.c # should be identical
mv dist/platform/elc-new dist/platform/elc
```
Run your first program:
```bash
./lang/dist/platform/elc lang/examples/hello.el > hello.c
cc -std=c11 -I lang/el-compiler/runtime -lcurl -lpthread \
-o hello hello.c lang/el-compiler/runtime/el_seed.c
./hello
```
More examples in [lang/examples/](lang/examples/), including a full starter project at `lang/examples/hello-project/`.
If the compiler binary is ever lost or corrupted, [lang/BOOTSTRAP.md](lang/BOOTSTRAP.md) is the authoritative recovery path.
---
## Development workflow
Branching follows `dev → stage → main`: work lands on `dev`, promotes to `stage` for integration testing, and is promoted to `main` for release (visible directly in the git history of this repo). CI is defined per-subproject under `.gitea/workflows/``lang`/`epm`/`ide` share the root pipeline; `engram` and `ql` carry their own (`ci-dev`, `ci-stage`, and a release workflow each).
- Language/runtime specs live at `*/spec/*.md` (`lang/spec/`, `ql/spec/`, `ui/spec/`) and are the single source of truth for implemented-vs-planned status — code and docs are expected to agree with the spec's status markers, not the other way around.
- Agent-facing orientation guides live at `*/AGENTS.md` (currently `lang/AGENTS.md`); more subprojects may grow their own as they need agent-specific conventions documented.
- Tagged releases live under `lang/releases/`, each with its own `RELEASE.md`.
---
## Status
This is an actively developed, internal monorepo — not yet published under an open license. Treat everything here as proprietary to Neuron Technologies unless told otherwise.
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# ELP language consolidation — full-lexicon backfill (stage)
Branch: `stage-elp-lang-consolidation` (stage-bound; NOT the live soul :8742).
Consolidates scattered Python language-realizer work (`~/Desktop/lang-realizers`,
`~/Desktop/lang-poetry-experiment`, `~/semitic_engine`) into the ELP `.el`
structure, generating **full lexicons** (complete UniMorph + kaikki.org
Wiktionary — real gender, real inflections) instead of the demo/curated subsets
the prototypes shipped.
## ELP before this branch
- 18 classical/ancient languages fully done (vocab + morphology + tests):
akk ang cop egy enm fro gez goh got grc non peo pi sa sga sux txb uga.
- 11 modern/classical languages had `morphology-<code>.el` in the build manifest
but **no vocabulary and no lang_profile**: es fr de ja ar he hi ru fi sw la.
- The ES port (`stage-elp-es-port`) had a *demo-scale* vocabulary-es.el (~350
entries, s-expr form).
## Landed on this branch (full-lexicon seed-fn format, matching the 18 ancients)
Vocabulary schema per row: `[lemma, pos, form0, form1, form2, en_gloss, hint]`.
Files are ELP runtime **seed data** (loaded via the Engram at runtime), so — like
all 18 classical `vocabulary-*.el` — they are intentionally NOT in the build
manifest. Syntax validated: the chunked `fn vocab_<code>_seed_pN` format
compiles cleanly to C via `elc` (correct UTF-8).
| code | in-ELP-morph? | vocab entries | verbs | nouns | adjs | profile |
|------|---------------|--------------:|------:|------:|-----:|---------|
| es | yes | 72,032 | 6,695 | 48,353 | 16,984 | yes |
| fr | yes | 130,517 | 7,534 | 77,344 | 45,639 | yes |
| de | yes | 144,692 | 6,661 | 133,162 | 4,869 | yes |
| la | yes | 22,590 | 82 | 13,436 | 9,072 | yes |
| it | no (bonus) | 193,675 | 10,008 | 109,459 | 74,208 | yes |
| pt | no (bonus) | 115,772 | 4,001 | 72,073 | 39,698 | yes |
| ro | no (bonus) | 86,504 | 1,216 | 65,915 | 19,373 | yes |
| ca | no (bonus) | 47,112 | 1,547 | 28,830 | 16,735 | yes |
|**total**| |**812,894** | | | | |
Generators (reproducible): `elp/tests/lang-gen/gen_elp_seed_full.py` (Romance),
`gen_elp_seed_de_la.py` (German declension + Latin case-paradigm mapping). They
read the pre-built morph caches in `~/Desktop/lang-realizers/data/` (UniMorph +
kaikki), which are too large to commit.
## Remaining (honest)
Of the 11 ELP backfill targets, 4 are done (es fr de la). The other 7 have **no
full-lexicon engine** yet — cannot be generated honestly without engine work:
- **ru**: only a 110-entry curated Slavic subset exists; full `rus.unimorph`
present but no `morphology_ru_full` productive loader. Needs a full Russian
morphology module (like the Romance ones) before vocab generation.
- **ja / ko / zh**: validated demo engines (~66-104 hardcoded words) in
`lang-poetry-experiment`, Python only. Agglutinative (ja/ko) + isolating (zh)
need `.el` engine ports + full-lexicon wiring (ja: jpn_unimorph; zh: CC-CEDICT).
- **ar / he (Semitic)**: template engines (16 AR / 8 HE patterns, ~6 roots) in
`~/semitic_engine`, Python only. Root-and-pattern; full UniMorph ara/heb
present but used only for validation. Needs productive root lexicon + `.el` port.
- **hi (Hindi), fi (Finnish), sw (Swahili)**: `morphology-<code>.el` exists in
ELP but there is NO scattered prototype and NO downloaded data for these —
full-lexicon collection (UniMorph/kaikki) + generator still to do.
De/nl/sv Germanic and it/ro/ca/pt Romance verb coverage note: German verbs here
are the ~6.6k caches carry; the it/ro/ca/pt bonus languages have full vocab but
**no `morphology-<code>.el` in ELP yet** (Python realizer exists; `.el` port is
the remaining engine work).
Construction coverage (separate from lexicon): French realizer was ~55%,
Semitic ~3% in the prototypes — full construction coverage remains its own task.
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{
"dataset": "british-rp-accent-transform",
"primitive_type": "accent_target",
"accent": "british-rp",
"grounding": "derived",
"provenance": "HONEST-DERIVED, COARSE FIRST PASS — NOT transcribed measured RP formants. The exact measured RP/GB tables (Deterding 1997 JIPA 27:47-55; Hawkins & Midgley 2005 JIPA 35:183-199) are the intended ground truth but were gated/figure-only at author time and were NOT transcribed. So these targets are DERIVED: each = the corresponding MEASURED Peterson&Barney(1952) base vowel transformed under the documented, citable RP-vs-GA structural rules of Wells (1982) 'Accents of English' — non-rhoticity (NURSE de-rhoticized: remove low F3), TRAP F2-lowering, LOT/THOUGHT back-rounding (F2 down), GOOSE-fronting (F2 up), GOAT centering. Shift MAGNITUDES are coarse/approximate (first pass), directions are cited. ground:derived (base measured + rule cited). Refine by transcribing Deterding/Hawkins&Midgley. No number is presented as a measured RP value it is not.",
"notes": "records with kind=vowel_override REPLACE the base phoneme's formant targets with the DERIVED RP realization. records with kind=rule encode non-formant transforms (non-rhoticity: drop post-vocalic coda /r/). The render composes: base geometry then accent override + rhoticity rule — voice + accent, separable.",
"records": [
{"key": "IY", "features": {"kind": "vowel_override", "set": "FLEECE"}, "attributes": {"f1": 280, "f2": 2249, "f3": 3000}},
{"key": "IH", "features": {"kind": "vowel_override", "set": "KIT"}, "attributes": {"f1": 360, "f2": 2100, "f3": 2550}},
{"key": "EH", "features": {"kind": "vowel_override", "set": "DRESS"}, "attributes": {"f1": 560, "f2": 1970, "f3": 2480}},
{"key": "AE", "features": {"kind": "vowel_override", "set": "TRAP"}, "attributes": {"f1": 730, "f2": 1590, "f3": 2410}},
{"key": "AA", "features": {"kind": "vowel_override", "set": "LOT"}, "attributes": {"f1": 560, "f2": 920, "f3": 2440}},
{"key": "AO", "features": {"kind": "vowel_override", "set": "THOUGHT"}, "attributes": {"f1": 415, "f2": 700, "f3": 2410}},
{"key": "UH", "features": {"kind": "vowel_override", "set": "FOOT"}, "attributes": {"f1": 380, "f2": 1100, "f3": 2240}},
{"key": "UW", "features": {"kind": "vowel_override", "set": "GOOSE"}, "attributes": {"f1": 310, "f2": 1650, "f3": 2240}},
{"key": "AH", "features": {"kind": "vowel_override", "set": "STRUT"}, "attributes": {"f1": 680, "f2": 1180, "f3": 2390}},
{"key": "ER", "features": {"kind": "vowel_override", "set": "NURSE", "rhotic": "no"}, "attributes": {"f1": 550, "f2": 1500, "f3": 2500}},
{"key": "AX", "features": {"kind": "vowel_override", "set": "commA"}, "attributes": {"f1": 500, "f2": 1500, "f3": 2500}},
{"key": "OW", "features": {"kind": "vowel_override", "set": "GOAT"}, "attributes": {"f1": 450, "f2": 1400, "f3": 2380}},
{"key": "R", "features": {"kind": "rule", "rule": "non_rhotic"}, "attributes": {"drop_coda_r": 1}}
]
}
-26
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@@ -1,26 +0,0 @@
# british-rp-accent TRANSFORM — INGESTIBLE DATA (a geometry/transform composed
# onto the base General-American phoneme targets; voice + accent, separable).
#
# PROVENANCE — HONEST, COARSE FIRST PASS. These are DERIVED targets, NOT
# transcribed measured RP formants. Measured RP tables (Deterding 1997 JIPA 27;
# Hawkins & Midgley 2005 JIPA 35) are the intended ground truth but were gated at
# author time and NOT transcribed. Each target = the MEASURED Peterson&Barney
# (1952) base vowel transformed under the documented, citable RP-vs-GA structural
# rules of Wells (1982): non-rhoticity, TRAP F2-lowering, LOT/THOUGHT back-
# rounding, GOOSE-fronting, GOAT centering, NURSE de-rhoticization. Shift
# magnitudes are coarse/approximate; directions are cited. ground=derived.
# Refine by transcribing the measured RP tables. No value is claimed as measured.
# Format: KEY|F1|F2|F3|KIND|SET
IY|280|2249|3000|vowel_override|FLEECE
IH|360|2100|2550|vowel_override|KIT
EH|560|1970|2480|vowel_override|DRESS
AE|730|1590|2410|vowel_override|TRAP
AA|560|920|2440|vowel_override|LOT
AO|415|700|2410|vowel_override|THOUGHT
UH|380|1100|2240|vowel_override|FOOT
UW|310|1650|2240|vowel_override|GOOSE
AH|680|1180|2390|vowel_override|STRUT
ER|550|1500|2500|vowel_override|NURSE-nonrhotic
AX|500|1500|2500|vowel_override|commA
OW|450|1400|2380|vowel_override|GOAT
R|0|0|0|rule|non_rhotic_drop_coda
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@@ -1,20 +0,0 @@
# pronunciation lexicon SOURCE — word -> phoneme sequence, as INGESTIBLE DATA.
# Pronunciation is linguistic KNOWLEDGE (the language faculty's orthography->
# phonology map), ingested into the engram, not frozen in code. The render reads
# a word's phoneme sequence back from the engram. Covers the self-lexicon and the
# proof sentences; general G2P is the realizer/morphology faculty's remit.
# Diphthongs are written as two vowel targets (the render's transitions glide
# between them). Format: word|PH1 PH2 PH3 ...
i|AA IY
am|AE M
neuron|N UW R AA N
is|IH Z
memory|M EH M ER IY
hello|HH EH L OW
the|DH AH
a|AH
remember|R IH M EH M ER
i'm|AA IY M
you|Y UW
here|HH IY R
will|W IH L
File diff suppressed because one or more lines are too long
-528
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{
"dataset": "english-phoneme-formants",
"primitive_type": "phoneme",
"grounding": "extracted",
"provenance": "AUDITED per-field. The 10 monophthong-vowel F1/F2/F3 (IY,IH,EH,AE,AA,AO,UH,UW,AH,ER) are the MEASURED adult-male /hVd/ means of Peterson & Barney (1952) JASA 24:175-184, verified vs CRAN phonTools::pb52. AX=neutral uniform-tube resonances (Fant, physics). OW steady target = synthesis convention (diphthong). Consonant loci (M,N,NG,L,R,W,Y,Z,DH,V,S,F,HH) and ALL bandwidths + dur/amp = standard formant-synthesis conventions (Klatt 1980 JASA 67:971), engineering defaults NOT field measurements. No numbers invented/LLM-generated.",
"records": [
{
"key": "IY",
"features": {
"manner": "vowel",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 270,
"f2": 2290,
"f3": 3010,
"bw1": 60,
"bw2": 90,
"bw3": 150,
"voiced": 1,
"nasal": 0,
"dur": 130,
"amp": 100
}
},
{
"key": "IH",
"features": {
"manner": "vowel",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 390,
"f2": 1990,
"f3": 2550,
"bw1": 70,
"bw2": 100,
"bw3": 150,
"voiced": 1,
"nasal": 0,
"dur": 110,
"amp": 100
}
},
{
"key": "EH",
"features": {
"manner": "vowel",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 530,
"f2": 1840,
"f3": 2480,
"bw1": 80,
"bw2": 100,
"bw3": 150,
"voiced": 1,
"nasal": 0,
"dur": 130,
"amp": 100
}
},
{
"key": "AE",
"features": {
"manner": "vowel",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 660,
"f2": 1720,
"f3": 2410,
"bw1": 90,
"bw2": 110,
"bw3": 150,
"voiced": 1,
"nasal": 0,
"dur": 150,
"amp": 100
}
},
{
"key": "AA",
"features": {
"manner": "vowel",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 730,
"f2": 1090,
"f3": 2440,
"bw1": 90,
"bw2": 110,
"bw3": 150,
"voiced": 1,
"nasal": 0,
"dur": 150,
"amp": 100
}
},
{
"key": "AO",
"features": {
"manner": "vowel",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 570,
"f2": 840,
"f3": 2410,
"bw1": 80,
"bw2": 100,
"bw3": 150,
"voiced": 1,
"nasal": 0,
"dur": 140,
"amp": 100
}
},
{
"key": "UH",
"features": {
"manner": "vowel",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 440,
"f2": 1020,
"f3": 2240,
"bw1": 70,
"bw2": 100,
"bw3": 150,
"voiced": 1,
"nasal": 0,
"dur": 110,
"amp": 100
}
},
{
"key": "UW",
"features": {
"manner": "vowel",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 300,
"f2": 870,
"f3": 2240,
"bw1": 70,
"bw2": 90,
"bw3": 150,
"voiced": 1,
"nasal": 0,
"dur": 140,
"amp": 100
}
},
{
"key": "AH",
"features": {
"manner": "vowel",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 640,
"f2": 1190,
"f3": 2390,
"bw1": 80,
"bw2": 100,
"bw3": 150,
"voiced": 1,
"nasal": 0,
"dur": 110,
"amp": 95
}
},
{
"key": "ER",
"features": {
"manner": "vowel",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 490,
"f2": 1350,
"f3": 1690,
"bw1": 80,
"bw2": 100,
"bw3": 120,
"voiced": 1,
"nasal": 0,
"dur": 140,
"amp": 95
}
},
{
"key": "AX",
"features": {
"manner": "vowel",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 500,
"f2": 1500,
"f3": 2500,
"bw1": 80,
"bw2": 100,
"bw3": 150,
"voiced": 1,
"nasal": 0,
"dur": 80,
"amp": 85
}
},
{
"key": "OW",
"features": {
"manner": "vowel",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 490,
"f2": 910,
"f3": 2380,
"bw1": 80,
"bw2": 100,
"bw3": 150,
"voiced": 1,
"nasal": 0,
"dur": 140,
"amp": 100
}
},
{
"key": "M",
"features": {
"manner": "nasal",
"voiced": "yes",
"nasal": "yes"
},
"attributes": {
"f1": 250,
"f2": 900,
"f3": 2200,
"bw1": 90,
"bw2": 120,
"bw3": 180,
"voiced": 1,
"nasal": 1,
"dur": 80,
"amp": 60
}
},
{
"key": "N",
"features": {
"manner": "nasal",
"voiced": "yes",
"nasal": "yes"
},
"attributes": {
"f1": 250,
"f2": 1700,
"f3": 2600,
"bw1": 90,
"bw2": 120,
"bw3": 180,
"voiced": 1,
"nasal": 1,
"dur": 80,
"amp": 60
}
},
{
"key": "NG",
"features": {
"manner": "nasal",
"voiced": "yes",
"nasal": "yes"
},
"attributes": {
"f1": 250,
"f2": 2300,
"f3": 2700,
"bw1": 90,
"bw2": 120,
"bw3": 180,
"voiced": 1,
"nasal": 1,
"dur": 80,
"amp": 60
}
},
{
"key": "L",
"features": {
"manner": "approximant",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 360,
"f2": 1300,
"f3": 2600,
"bw1": 80,
"bw2": 110,
"bw3": 160,
"voiced": 1,
"nasal": 0,
"dur": 70,
"amp": 80
}
},
{
"key": "R",
"features": {
"manner": "approximant",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 490,
"f2": 1350,
"f3": 1600,
"bw1": 80,
"bw2": 110,
"bw3": 120,
"voiced": 1,
"nasal": 0,
"dur": 80,
"amp": 85
}
},
{
"key": "W",
"features": {
"manner": "approximant",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 300,
"f2": 610,
"f3": 2200,
"bw1": 70,
"bw2": 100,
"bw3": 160,
"voiced": 1,
"nasal": 0,
"dur": 70,
"amp": 80
}
},
{
"key": "Y",
"features": {
"manner": "approximant",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 270,
"f2": 2290,
"f3": 3010,
"bw1": 60,
"bw2": 90,
"bw3": 150,
"voiced": 1,
"nasal": 0,
"dur": 60,
"amp": 80
}
},
{
"key": "Z",
"features": {
"manner": "fricative",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 300,
"f2": 1700,
"f3": 2500,
"bw1": 100,
"bw2": 150,
"bw3": 200,
"voiced": 1,
"nasal": 0,
"dur": 90,
"amp": 55
}
},
{
"key": "DH",
"features": {
"manner": "fricative",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 300,
"f2": 1400,
"f3": 2500,
"bw1": 100,
"bw2": 150,
"bw3": 200,
"voiced": 1,
"nasal": 0,
"dur": 70,
"amp": 55
}
},
{
"key": "V",
"features": {
"manner": "fricative",
"voiced": "yes",
"nasal": "no"
},
"attributes": {
"f1": 300,
"f2": 1000,
"f3": 2300,
"bw1": 100,
"bw2": 150,
"bw3": 200,
"voiced": 1,
"nasal": 0,
"dur": 70,
"amp": 55
}
},
{
"key": "S",
"features": {
"manner": "fricative",
"voiced": "no",
"nasal": "no"
},
"attributes": {
"f1": 320,
"f2": 1700,
"f3": 2500,
"bw1": 200,
"bw2": 200,
"bw3": 250,
"voiced": 0,
"nasal": 0,
"dur": 110,
"amp": 45
}
},
{
"key": "F",
"features": {
"manner": "fricative",
"voiced": "no",
"nasal": "no"
},
"attributes": {
"f1": 300,
"f2": 1200,
"f3": 2400,
"bw1": 200,
"bw2": 200,
"bw3": 250,
"voiced": 0,
"nasal": 0,
"dur": 100,
"amp": 40
}
},
{
"key": "HH",
"features": {
"manner": "fricative",
"voiced": "no",
"nasal": "no"
},
"attributes": {
"f1": 500,
"f2": 1500,
"f3": 2500,
"bw1": 200,
"bw2": 250,
"bw3": 300,
"voiced": 0,
"nasal": 0,
"dur": 70,
"amp": 40
}
},
{
"key": "SIL",
"features": {
"manner": "silence",
"voiced": "no",
"nasal": "no"
},
"attributes": {
"f1": 500,
"f2": 1500,
"f3": 2500,
"bw1": 100,
"bw2": 100,
"bw3": 100,
"voiced": 0,
"nasal": 0,
"dur": 55,
"amp": 0
}
}
]
}
-45
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@@ -1,45 +0,0 @@
# acoustic-phonetics SOURCE — the learned speech primitives, as INGESTIBLE DATA.
# NOT audio, NOT code: formant geometry of the phonemes, to be ingested via the
# ingest organ into the engram as a phoneme manifold. The render reads this
# geometry back from the engram; nothing is frozen in EL code.
#
# PROVENANCE (audited, per-field honesty — no invented numbers):
# * The 10 MONOPHTHONG VOWEL formants F1/F2/F3 (IY,IH,EH,AE,AA,AO,UH,UW,AH,ER)
# are the MEASURED adult-male means of Peterson & Barney (1952), JASA 24:175-184
# — the canonical /hVd/ table, verified digit-for-digit vs CRAN phonTools::pb52.
# These are real measured values.
# * AX (schwa) F1/F2/F3 = neutral uniform-tube resonances (2n-1)*500 — a PHYSICS
# value (Fant), not a P&B measurement.
# * OW is a diphthong; its listed steady target is a conventional synthesis value,
# not a P&B monophthong measurement.
# * CONSONANT loci (M,N,NG,L,R,W,Y,Z,DH,V,S,F,HH) and ALL BANDWIDTHS (B1,B2,B3)
# and dur/amp are STANDARD FORMANT-SYNTHESIS conventions (Klatt 1980, JASA 67:971
# "Software for a cascade/parallel formant synthesizer") — engineering defaults,
# NOT per-phoneme field measurements. Labeled as such, not attributed to P&B.
# Format: SYM|F1|F2|F3|B1|B2|B3|voiced|nasal|dur_ms|amp|class|example
IY|270|2290|3010|60|90|150|1|0|130|100|vowel|beet
IH|390|1990|2550|70|100|150|1|0|110|100|vowel|bit
EH|530|1840|2480|80|100|150|1|0|130|100|vowel|bet
AE|660|1720|2410|90|110|150|1|0|150|100|vowel|bat
AA|730|1090|2440|90|110|150|1|0|150|100|vowel|bot
AO|570|840|2410|80|100|150|1|0|140|100|vowel|bought
UH|440|1020|2240|70|100|150|1|0|110|100|vowel|book
UW|300|870|2240|70|90|150|1|0|140|100|vowel|boot
AH|640|1190|2390|80|100|150|1|0|110|95|vowel|but
ER|490|1350|1690|80|100|120|1|0|140|95|vowel|bird
AX|500|1500|2500|80|100|150|1|0|80|85|vowel|about
OW|490|910|2380|80|100|150|1|0|140|100|vowel|boat
M|250|900|2200|90|120|180|1|1|80|60|nasal|map
N|250|1700|2600|90|120|180|1|1|80|60|nasal|nap
NG|250|2300|2700|90|120|180|1|1|80|60|nasal|sing
L|360|1300|2600|80|110|160|1|0|70|80|approximant|lip
R|490|1350|1600|80|110|120|1|0|80|85|approximant|rip
W|300|610|2200|70|100|160|1|0|70|80|approximant|wet
Y|270|2290|3010|60|90|150|1|0|60|80|approximant|yet
Z|300|1700|2500|100|150|200|1|0|90|55|fricative|zoo
DH|300|1400|2500|100|150|200|1|0|70|55|fricative|the
V|300|1000|2300|100|150|200|1|0|70|55|fricative|van
S|320|1700|2500|200|200|250|0|0|110|45|fricative|see
F|300|1200|2400|200|200|250|0|0|100|40|fricative|fee
HH|500|1500|2500|200|250|300|0|0|70|40|fricative|hat
SIL|500|1500|2500|100|100|100|0|0|55|0|silence|_
-5
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@@ -80,11 +80,6 @@ build {
"src/grammar.el",
"src/realizer.el",
"src/semantics.el",
"src/comprehend.el",
"src/propositions.el",
"src/multilingual.el",
"src/self_region.el",
"src/dialogue.el",
"src/elp.el",
]
}
-91
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@@ -1,91 +0,0 @@
> **STATUS: STAGING / PROOF-OF-SHAPE — not the deliverable.** This Python package
> proved the architecture end-to-end against the proven realizer faculty (faithful
> md/docx/midi from real geometry: 0 ungrounded claims, SACRED polarity). Per Will's
> steer, the DELIVERABLE is NATIVE: the seam lives on the existing EL realizer as
> **surface-as-profile** — see `../src/surface-profile.el` and
> `../tests/examples/surface-profile-demo.el` (compiles + runs through elc → C →
> binary). The concepts below (one geometry-carrying frame; surface = a pluggable
> profile; plan/realize; deterministic-from-meaning) are exactly what the native
> module implements. Keep this package as the validated proof; build native.
# Efferent Multimodal Projector
**geometry → any surface, faithfully.** Neuron's own document-generation faculty:
the efferent twin of the ingest organ. Ingest is afferent (world → geometry);
this is efferent (geometry → an arbitrary-format document / any modality).
Built against the **proven** realizer faculty (neuron-talk sidecar `:8756`,
artifact `art-7affa557`). The live soul (`:8742` / `:7770`) is contacted **only**
through the read-only, GET-only `engram_client` — never mutated.
## The pipeline (surface-agnostic)
```
geometry region + surface/format spec
→ PLAN (manifold → document skeleton/DAG; the geometry IS the outline) plan.py
→ REALIZE (proven realizer, scaled sentence → passage, each section faithful) realize.py
→ COHERE (document-level flow / transitions, not stitched sentences) cohere.py
→ EMIT (pluggable SurfaceProjector → the target surface) projectors/
```
**The surface is a PARAMETER.** `pipeline.build_ir(...)` builds ONE
surface-neutral `DocumentIR` (`document_ir.py`); `pipeline.emit(doc, surface)`
projects it to whichever surface you name. Markdown, docx, and MIDI are the same
IR emitted three ways.
## The pivot: a geometry-carrying IR
`DocumentIR` is **not** a text tree. Every `Block` carries BOTH:
- `.sentences` — realized faithful text (what **text** projectors read),
- `.provenance` — the source geometry: `subj_id / relation / obj / polarity /
confidence / importance / salience / node_id` (what **music / image / video**
projectors read).
That single decision is what makes the projector multimodal: text renders the
words; music/image decode the geometry. A claim with no provenance cannot exist
in the IR — faithfulness is structural.
## The one shared seam
`projectors/base.py` — `SurfaceProjector.project(frame: DocumentIR) -> bytes`
(+ `surface / media_type / ext / modality / profile`). Register with
`register()`. Adding a surface changes nothing upstream.
`TwoStageProjector` blesses the peer plan/realize decomposition:
`spec = plan(frame)`, `bytes = realize(spec)`, `project = realize∘plan`; the
`profile` is the pluggable per-surface knob (text lang-profile, music
instr/mode-profile). `projectors/midi.py` is the reference two-stage impl.
## Surfaces
| surface | modality | status | emitter |
|---|---|---|---|
| `markdown` | text | landed | own (str) |
| `docx` | text | landed | own minimal OOXML (stdlib `zipfile`+XML, no lib) |
| `midi` | audio | landed (symbolic-music proof) | own minimal SMF (stdlib `struct`, no lib) |
| `audio` (WAV) | audio | peer agent (additive synth) | conforms to `TwoStageProjector` |
| `image` | image | documented seam | `projectors/seams.py` |
| `video` | video | documented seam (image×sound×time) | `projectors/seams.py` |
Music maps: relation → scale degree (same relation → same pitch), **polarity →
major/minor third (SACRED negation is audible)**, confidence → duration,
importance → velocity, section → register. Deterministic projection from meaning
— nothing invented.
## Faithfulness
`provenance.py` audits the IR: **zero** ungrounded claims, SACRED polarity
preserved (negations reported, never dropped), COHERE introduces no new geometry
(connectives are marked). `trace_table()` emits the geometry → section → claim
table.
## Run
```bash
PY=~/Desktop/lang-realizers/venv/bin/python
PYTHONPATH=~/Desktop/neuron-talk:~/Desktop/lang-realizers $PY generate.py
# writes ./out/{neuron-self,engram-temporal}.{md,docx,mid} + *.audit.json + *.provenance.md
```
Requires the proven realizer env (spaCy + the neuron-talk/lang-realizers engine)
and the read-only engram at `:8742`.
-79
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@@ -1,79 +0,0 @@
"""cohere.py — COHERE stage: document-level flow, not stitched sentences.
Fidelity is REALIZE's job; FLOW is this stage's. The hard part beyond sentence
fidelity is that a document must read as one thing. We add connective tissue at
the passage level:
* an opening abstract that names what the document covers (built ONLY from the
section headings that already exist — it introduces no new claim),
* a short transition lead into each section after the first, drawn from a
fixed set of discourse connectives ("Beyond that,", "Relatedly,", ...) that
carry no propositional content,
* ordering so the highest-grounded section leads.
CRITICAL: every connective is marked ``kind="connective"`` in its provenance, so
the faithfulness audit can prove COHERE introduced ZERO new geometry claims. A
transition is discourse glue, never a fact.
"""
from __future__ import annotations
from document_ir import Block, DocumentIR, Provenance
# discourse connectives — pure flow, no propositional content
_TRANSITIONS = [
"Beyond that,", "Relatedly,", "In the same region,", "From there,",
"Alongside this,", "Further,", "Turning to the next facet,",
]
def _connective_prov() -> Provenance:
return Provenance(subj_id=None, subject=None, relation="", obj=None,
polarity="aff", confidence=1.0, node_id=None,
kind="connective")
def _abstract_block(doc: DocumentIR) -> Block:
"""A grounded opening: names the sections, asserts nothing new."""
headings = [s.heading for s in doc.sections]
if not headings:
return Block(role="lead")
if len(headings) == 1:
body = f"This document, generated from Neuron's geometry, covers {headings[0]}."
else:
listed = ", ".join(headings[:-1]) + f", and {headings[-1]}"
body = ("This document is projected directly from Neuron's meaning-geometry. "
f"It traces {listed}.")
b = Block(role="lead")
b.sentences.append(body)
b.provenance.append(_connective_prov())
return b
def cohere_document(doc: DocumentIR, *, add_abstract: bool = True,
add_transitions: bool = True) -> DocumentIR:
"""Order sections by grounding, add abstract + transitions (flow only)."""
# order: strongest-grounded section (mean confidence x #claims) first,
# but keep an explicitly-first section if the plan pinned one via level 1.
def _score(sec):
provs = [p for p in sec.all_provenance() if p.kind == "fact"]
if not provs:
return 0.0
mean_conf = sum(p.confidence for p in provs) / len(provs)
return mean_conf * len(provs)
doc.sections.sort(key=_score, reverse=True)
if add_transitions:
for i, sec in enumerate(doc.sections):
if i == 0 or not sec.blocks:
continue
lead = _TRANSITIONS[(i - 1) % len(_TRANSITIONS)]
first = sec.blocks[0]
if first.sentences:
# prepend the connective to the first sentence (flow, no new claim)
first.sentences[0] = f"{lead} {first.sentences[0][0].lower()}{first.sentences[0][1:]}"
if add_abstract:
doc.meta["abstract"] = _abstract_block(doc)
return doc
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"""document_ir.py — the surface-neutral, GEOMETRY-CARRYING document intermediate.
This is the pivot of the whole efferent projector. A DocumentIR is NOT a text
tree. It is a projection of a meaning-geometry region that carries, at every
leaf, BOTH:
* the realized surface text (``Block.sentences``) — what a TEXT projector reads,
* the source geometry (``Block.provenance``) — what a MUSIC / IMAGE /
VIDEO projector reads.
Because the IR holds the geometry, not just the words, the SAME
plan -> realize -> cohere pipeline drives every surface. A markdown projector
renders the sentences; a music projector reads the provenance edges (salience,
importance, polarity, relation) and maps them onto a symbolic-music surface;
an image/video projector (documented seam) would read the same geometry.
Nothing in this module invents content. Every :class:`Provenance` points at a
real engram node id and a real relation. That is the faithfulness contract made
structural: a claim with no provenance cannot exist in the IR.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any
# --------------------------------------------------------------------------- #
# Provenance — the geometry an emitted claim traces to. FAITHFULNESS is here.
# --------------------------------------------------------------------------- #
@dataclass
class Provenance:
"""One geometry edge behind one realized claim.
``kind`` distinguishes a FACT (a structural edge asserted by the geometry,
spoken as fact) from an INTERPRETATION (something attributed, spoken with
attribution) — the facts-as-facts + interpretations-attributed discipline
(memory 80927e26). ``polarity`` is SACRED: a negated edge stays negated.
"""
subj_id: str | None # source engram node id of the subject
subject: str | None # normalized subject surface
relation: str # predicate lemma (e.g. "use", "contain", "be")
obj: str | None # normalized object / complement surface
polarity: str = "aff" # "aff" | "neg" (SACRED — never silently flipped)
confidence: float = 0.0 # extraction confidence in [0,1]
node_id: str | None = None # engram node the claim was extracted from
kind: str = "fact" # "fact" | "interpretation"
importance: float = 0.0 # source node importance (drives music/emphasis)
salience: float = 0.0 # source node salience
def trace(self) -> str:
arrow = "-->" if self.polarity == "aff" else "--NOT-->"
return (f"[{(self.node_id or '?')[:8]}] {self.subject!r} {arrow}"
f"{self.relation} {self.obj!r} (conf {self.confidence:.2f})")
@dataclass
class Block:
"""A passage: one or more faithful sentences + the geometry they trace to.
``sentences`` and ``provenance`` are index-aligned where possible: sentence
``i`` was realized from ``provenance[i]``. A COHERE transition sentence with
no new geometry carries a provenance whose ``kind == "connective"`` so the
audit can see it introduced no new claim.
"""
sentences: list[str] = field(default_factory=list)
provenance: list[Provenance] = field(default_factory=list)
role: str = "body" # "body" | "lead" | "transition"
def text(self) -> str:
return " ".join(s.rstrip(". ") + "." for s in self.sentences if s.strip())
@dataclass
class Section:
heading: str
level: int = 2 # markdown heading level / outline depth
blocks: list[Block] = field(default_factory=list)
seed_ids: list[str] = field(default_factory=list) # geometry nodes of section
summary: str = "" # one-line grounded gloss (for pptx bullets / TOC)
def all_provenance(self) -> list[Provenance]:
out: list[Provenance] = []
for b in self.blocks:
out.extend(b.provenance)
return out
@dataclass
class DocumentIR:
"""The surface-neutral document. Built ONCE, projected to ANY surface."""
title: str
subtitle: str = ""
sections: list[Section] = field(default_factory=list)
seed_id: str | None = None # the geometry region root
format_spec: dict[str, Any] = field(default_factory=dict) # requested shape
meta: dict[str, Any] = field(default_factory=dict)
# -- geometry facets (what non-text projectors consume) ----------------- #
def all_provenance(self) -> list[Provenance]:
out: list[Provenance] = []
for s in self.sections:
out.extend(s.all_provenance())
return out
def claim_count(self) -> int:
return sum(1 for p in self.all_provenance() if p.kind in ("fact", "interpretation"))
def ungrounded_count(self) -> int:
"""Claims with no traceable node — MUST be zero for a faithful doc."""
return sum(1 for p in self.all_provenance()
if p.kind in ("fact", "interpretation") and not p.node_id)
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"""generate.py — drive the projector: one geometry region -> many surfaces.
Proves the thesis with REAL output: builds ONE surface-neutral DocumentIR from
Neuron's OWN self-geometry (read-only against the live soul via the proven
faculty), then EMITS it to Markdown, docx, and MIDI — the same plan/realize/
cohere, three surfaces. Writes the files + the faithfulness audit to ./out/.
"""
from __future__ import annotations
import json
import os
import sys
_HERE = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, _HERE)
import pipeline # noqa: E402
import provenance # noqa: E402
from geometry import load_self_region # noqa: E402
OUT = os.path.join(_HERE, "out")
def _emit_all(doc, stem):
"""Emit one IR to every text/audio surface + audit + provenance."""
for surface in ("markdown", "docx", "midi"):
data = pipeline.emit(doc, surface)
proj = pipeline.get_projector(surface)
path = os.path.join(OUT, f"{stem}.{proj.ext}")
with open(path, "wb") as f:
f.write(data)
print(f" emitted {surface:9s} -> {os.path.basename(path)} ({len(data)} bytes)")
a = provenance.audit(doc)
with open(os.path.join(OUT, f"{stem}.audit.json"), "w") as f:
json.dump(a, f, indent=2)
with open(os.path.join(OUT, f"{stem}.provenance.md"), "w") as f:
f.write(provenance.trace_table(doc))
print(" audit:", {k: a[k] for k in ("claims", "ungrounded_claims",
"negations_preserved", "distinct_source_nodes", "faithful")})
return a
def main():
os.makedirs(OUT, exist_ok=True)
print("surfaces registered:", pipeline.available_surfaces())
# ---- Document 1: Neuron's self-description (marquee) ------------------- #
print("\n[1] Neuron self-description")
region = load_self_region(max_nodes=9)
print(" self region:", region)
doc1 = pipeline.build_ir(
None, region=region,
title="Neuron: A Self-Description from Its Own Geometry",
subtitle="Projected efferently from the engram — every claim traces a node.",
format_spec={"genre": "self-description", "register": "expository"},
max_sections=5, conf_floor=0.6)
print(f" IR: {len(doc1.sections)} sections, {doc1.claim_count()} claims, "
f"ungrounded={doc1.ungrounded_count()}")
_emit_all(doc1, "neuron-self")
# ---- Document 2: a coherent, clean whitepaper-style section ------------ #
print("\n[2] Whitepaper-style section (coherent clean region)")
doc2, _ = pipeline.project(
["chronoception", "time", "awareness", "engram", "temporal"],
surface="markdown",
title="Temporal Awareness in the Engram",
subtitle="A section projected from the geometry of chronoception.",
format_spec={"genre": "whitepaper-section", "register": "technical"},
max_sections=4)
print(f" IR: {len(doc2.sections)} sections, {doc2.claim_count()} claims, "
f"ungrounded={doc2.ungrounded_count()}")
_emit_all(doc2, "engram-temporal")
# echo both markdowns so they are visible in the run log
for stem, doc in (("neuron-self", doc1), ("engram-temporal", doc2)):
print(f"\n===== GENERATED MARKDOWN — {stem} =====\n")
print(pipeline.emit(doc, "markdown").decode())
if __name__ == "__main__":
main()
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"""geometry.py — READ-ONLY loader for a meaning-geometry region.
The efferent projector never writes to the soul. This module reaches the
geometry through the PROVEN, read-only neuron-talk faculty (``engram_client``,
GET-only, which physically refuses non-GET methods) against the running sidecar
soul. The live daemon :8742 / :7770 is contacted ONLY through that read-only
client — never mutated.
A "region" is a seed node plus a bounded neighborhood: the manifold that will
become the document's skeleton. We pool a few single-term lexical searches
(the engram search is a single-term matcher) and, when available, walk one hop
of reified neighbors, then rank by self/importance signal.
"""
from __future__ import annotations
import os
import sys
# Wire in the proven faculty (own-the-core: we reuse it, we do not fork it).
_NT = os.path.expanduser("~/Desktop/neuron-talk")
_LR = os.path.expanduser("~/Desktop/lang-realizers")
for _p in (_NT, _LR):
if _p not in sys.path:
sys.path.insert(0, _p)
from engram_client import ReadOnlyEngramClient # noqa: E402
class Region:
"""A geometry region: ranked nodes + the reified edges among them."""
def __init__(self, seed: str, nodes: list[dict], edges: list[dict]):
self.seed = seed
self.nodes = nodes # ranked engram node dicts
self.edges = edges # [{src, dst, edge, ...}]
self.by_id = {n["id"]: n for n in nodes if n.get("id")}
def __repr__(self):
return f"<Region seed={self.seed!r} nodes={len(self.nodes)} edges={len(self.edges)}>"
def _prose_quality(content: str) -> float:
"""Reward clean expository prose; penalize shouty banner-dense nodes.
A high ALLCAPS-word ratio or very short content signals a banner/telegraphic
memory node that extracts into garbage. Clean declarative prose scores high.
"""
if not content or not content.strip():
return 0.0
words = content.split()
if len(words) < 8:
return 0.1
caps = sum(1 for w in words if len(w) > 2 and w.strip(".,:;'\"-").isupper())
caps_ratio = caps / max(1, len(words))
# sentences with lowercase interior words read as prose
lower = sum(1 for w in words if w[:1].islower())
lower_ratio = lower / max(1, len(words))
return max(0.0, 1.2 * lower_ratio - 2.0 * caps_ratio)
def _relevance(content: str, terms: list[str]) -> float:
"""Topical relevance to the seed terms — keeps a region ON-THEME so a clean
but off-topic node cannot hijack the document."""
if not terms:
return 0.0
low = (content or "").lower()
hits = sum(1 for t in terms if t.lower() in low)
return hits / max(1, len(terms))
def _node_rank(n: dict, terms: list[str] | None = None) -> float:
return (float(n.get("importance") or 0.0) * 2.0
+ float(n.get("salience") or 0.0)
+ 1.5 * _prose_quality(n.get("content") or "")
+ 2.0 * _relevance(n.get("content") or "", terms or [])
+ (0.5 if (n.get("content") or "").strip() else 0.0))
def load_region(seed_terms: list[str] | str, *, client: ReadOnlyEngramClient | None = None,
max_nodes: int = 10, per_term: int = 20, hop: bool = True) -> Region:
"""Pull a bounded geometry region around ``seed_terms`` (read-only).
``seed_terms`` may be a single string or several probe terms; results are
pooled and de-duplicated. When ``hop`` and the reified neighbor endpoint is
live, one hop of neighbors is folded in so the region is a real
neighborhood, not just a keyword hit list.
"""
client = client or ReadOnlyEngramClient()
if isinstance(seed_terms, str):
seed_terms = [seed_terms]
pool: dict[str, dict] = {}
for term in seed_terms:
for n in client.search(term, limit=per_term):
if isinstance(n, dict) and n.get("id"):
pool.setdefault(n["id"], n)
ranked = sorted(pool.values(), key=lambda n: _node_rank(n, seed_terms),
reverse=True)
nodes = ranked[:max_nodes]
edges: list[dict] = []
if hop and nodes:
present = {n["id"] for n in nodes}
for n in list(nodes):
try:
for nb in client.neighbors(n["id"]):
node = nb.get("node") if isinstance(nb, dict) else None
edge = nb.get("edge") if isinstance(nb, dict) else None
if node and node.get("id"):
edges.append({"src": n["id"], "dst": node["id"],
"edge": edge})
# fold a strong neighbor into the region (bounded)
if (node["id"] not in present and len(nodes) < max_nodes + 6
and _node_rank(node, seed_terms) > 0.4):
present.add(node["id"])
nodes.append(node)
except Exception: # noqa: BLE001 — read-only best-effort; never fatal
continue
return Region(seed=", ".join(seed_terms), nodes=nodes, edges=edges)
def load_self_region(client: ReadOnlyEngramClient | None = None,
max_nodes: int = 10) -> Region:
"""The self/identity region — Neuron's own geometry, for self-description."""
return load_region(["self", "identity", "Neuron", "values", "memory",
"imprint", "consciousness"],
client=client, max_nodes=max_nodes)
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"""pipeline.py — the Efferent Multimodal Projector, top level.
geometry region + surface/format spec
-> PLAN (manifold -> document skeleton/DAG)
-> REALIZE (proven realizer, sentence -> passage, each section faithful)
-> COHERE (document-level flow / transitions, not stitched sentences)
-> EMIT (pluggable SurfaceProjector -> the target surface)
THE SURFACE IS A PARAMETER. ``project(...)`` builds the geometry-carrying
DocumentIR once, then hands it to whichever surface projector the caller named.
Markdown, docx, and midi (music) are all the SAME IR emitted differently. That
is the efferent multimodal projector: geometry -> any surface.
"""
from __future__ import annotations
import os
import sys
_HERE = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, _HERE)
sys.path.insert(0, os.path.join(_HERE, "projectors"))
from cohere import cohere_document # noqa: E402
from document_ir import DocumentIR # noqa: E402
from geometry import Region, load_region # noqa: E402
from plan import plan_document # noqa: E402
from realize import realize_document # noqa: E402
# registering the projectors (import for side-effect: each self-registers)
import projectors.markdown # noqa: E402,F401
import projectors.docx # noqa: E402,F401
import projectors.midi # noqa: E402,F401
import projectors.seams # noqa: E402,F401
from projectors.base import available_surfaces, get_projector # noqa: E402
def build_ir(seed_terms, *, title: str, subtitle: str = "",
format_spec: dict | None = None,
region: Region | None = None,
max_sections: int = 8, conf_floor: float = 0.55) -> DocumentIR:
"""geometry -> PLAN -> REALIZE -> COHERE = the surface-neutral DocumentIR."""
region = region or load_region(seed_terms)
doc = plan_document(region, title=title, subtitle=subtitle,
format_spec=format_spec or {},
conf_floor=conf_floor, max_sections=max_sections)
doc = realize_document(doc)
doc = cohere_document(doc)
return doc
def emit(doc: DocumentIR, surface: str) -> bytes:
"""EMIT: project the built IR onto one surface (surface = a parameter)."""
return get_projector(surface).project(doc)
def project(seed_terms, *, surface: str, title: str, subtitle: str = "",
format_spec: dict | None = None, region: Region | None = None,
max_sections: int = 8) -> tuple[DocumentIR, bytes]:
"""The full efferent projection: geometry + surface -> (IR, bytes)."""
doc = build_ir(seed_terms, title=title, subtitle=subtitle,
format_spec=format_spec, region=region,
max_sections=max_sections)
return doc, emit(doc, surface)
__all__ = ["build_ir", "emit", "project", "available_surfaces",
"get_projector", "load_region", "DocumentIR"]
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"""plan.py — PLAN stage: geometry region -> document skeleton (a DAG/outline).
The manifold becomes the skeleton. We extract faithful propositions from the
region's nodes (the proven neuron-talk extractor, SACRED polarity preserved),
apply a quality floor, then GROUP them into sections. Grouping is by source
node — each engram node is one coherent topic, so one salient node becomes one
section. The section ORDER is the node ranking (importance/salience): the
geometry decides the outline, not a template.
Output: a DocumentIR whose sections carry seed node ids and empty blocks. REALIZE
fills the blocks; the plan owns the structure.
"""
from __future__ import annotations
import os
import re
import sys
_NT = os.path.expanduser("~/Desktop/neuron-talk")
_LR = os.path.expanduser("~/Desktop/lang-realizers")
for _p in (_NT, _LR):
if _p not in sys.path:
sys.path.insert(0, _p)
import propositions # noqa: E402 (the proven, faithful extractor)
from document_ir import DocumentIR, Section # noqa: E402
from geometry import Region # noqa: E402
# --------------------------------------------------------------------------- #
# Proposition quality — keep only clean, well-grounded claims.
# --------------------------------------------------------------------------- #
_JUNK_RE = re.compile(r"[.][a-z]{1,3}\b|[^A-Za-z0-9 '\-]") # ".o", stray symbols
def _has_banner_token(s: str) -> bool:
"""True if any word is an ALLCAPS banner token (DHARMA, ENGRAM, MEASURED)."""
for w in (s or "").split():
core = w.strip(".,:;'\"-")
if len(core) > 2 and core.isupper():
return True
return False
def _clean_prop(p, floor: float) -> bool:
if p.confidence < floor:
return False
if not p.subject or not (p.object or (p.obj_np is not None)):
return False
subj = (p.subject or "").strip()
obj = (p.object or "").strip()
if len(subj) < 2:
return False
# banner-derived shouty fragments read as garbage in prose
if _has_banner_token(subj) or _has_banner_token(obj):
return False
if propositions._is_shouty(p.sentence or ""):
return False
# junk tokens: file-extension fragments (".o"), stray non-word symbols
if _JUNK_RE.search(subj) or _JUNK_RE.search(obj):
return False
# a proposition whose object repeats the subject is usually a parse artifact
if obj and subj.lower() == obj.lower():
return False
# a bare copula with no real complement ("X is it") reads as noise
if p.predicate == "be" and obj.lower() in ("it", "no", "nothing", "empty", ""):
return False
return True
def _dedup(props):
"""Drop duplicate claims. Two axes: (a) identical (pred,obj,polarity), and
(b) same (subject,predicate) — which collapses a mis-split compound like
"detection is post-hoc eval" -> "Detection is post/hoc/eval" into one claim
(keep the highest-confidence surface)."""
props = sorted(props, key=lambda p: p.confidence, reverse=True)
seen_po, seen_sp, out = set(), set(), []
for p in props:
subj = (p.subject or "").lower()
po = (p.predicate, (p.object or "").lower(), p.polarity)
sp = (subj, p.predicate, p.polarity)
if po in seen_po or sp in seen_sp:
continue
seen_po.add(po)
seen_sp.add(sp)
out.append(p)
return out
# --------------------------------------------------------------------------- #
# Heading derivation — a clean human heading from a node.
# --------------------------------------------------------------------------- #
_HEADING_RE = re.compile(r"^\s*#{1,4}\s+(.{2,70})\s*$", re.M)
# node-type / system labels that are NOT topical headings
_NONTOPIC_LABEL = re.compile(r"^(memory|node|knowledge|doc|session)[:/]", re.I)
def _titlecase_banner(s: str) -> str:
"""A shouty banner ("CHRONOCEPTION — SCALE-INVARIANCE") makes a fine title
once Title-cased. Keep short acronyms uppercase."""
def fix(w):
core = w.strip("—-:,.")
if len(core) <= 3 and core.isupper():
return w # acronym
return w.capitalize()
return " ".join(fix(w) for w in s.split())
def _clean_heading(text: str) -> str | None:
"""First line only, no markdown, capped, banner Title-cased. None if unusable."""
if not text:
return None
line = text.strip().splitlines()[0]
line = re.sub(r"^#+\s*", "", line).strip().strip("#").strip()
# cut at a natural break so a long banner heading stays a heading, not a para
for sep in ("", " ", ": ", ". "):
if sep in line and len(line) > 48:
line = line.split(sep)[0].strip()
break
if not (3 <= len(line) <= 64):
return None
if propositions._is_shouty(line):
line = _titlecase_banner(line)
return line or None
def _heading_for(node: dict, fallback: str) -> str:
label = (node.get("label") or "").strip()
content = node.get("content") or ""
candidates: list[str] = []
# a node-type label ("memory:remembered") is never a topic — skip it
if label and not _NONTOPIC_LABEL.match(label):
candidates.append(label)
m = _HEADING_RE.search(content)
if m:
candidates.append(m.group(1))
# the leading banner/first sentence of the content is often the real title
first = re.split(r"(?<=[.\n])", content.strip(), maxsplit=1)[0] if content.strip() else ""
candidates.append(first)
for c in candidates:
h = _clean_heading(c)
if h:
return h
return fallback
def plan_document(region: Region, *, title: str, subtitle: str = "",
format_spec: dict | None = None,
conf_floor: float = 0.55,
max_sections: int = 8,
max_claims_per_section: int = 6) -> DocumentIR:
"""Region -> DocumentIR skeleton. The geometry dictates the outline."""
format_spec = format_spec or {}
doc = DocumentIR(title=title, subtitle=subtitle,
seed_id=region.nodes[0]["id"] if region.nodes else None,
format_spec=format_spec)
made = 0
seen_headings: set[str] = set()
for node in region.nodes:
if made >= max_sections:
break
props = propositions.extract(node.get("content") or "",
node_id=node.get("id"),
node_importance=float(node.get("importance") or 0.0),
max_sentences=10)
props = [p for p in props if _clean_prop(p, conf_floor)]
props = _dedup(props)
props.sort(key=lambda p: p.confidence, reverse=True)
props = props[:max_claims_per_section]
if not props:
continue
heading = _heading_for(node, fallback=f"Region {made + 1}")
# cross-section dedup: a topic appears once. Distinguish by top claim
# subject, else drop the collision so the outline stays clean.
if heading.lower() in seen_headings:
subj = (props[0].subject or "").strip().title()
alt = f"{heading}: {subj}" if subj and subj.lower() not in heading.lower() else None
if alt and alt.lower() not in seen_headings and len(alt) <= 64:
heading = alt
else:
continue
seen_headings.add(heading.lower())
sec = Section(heading=heading, level=2, seed_ids=[node["id"]])
# stash the planned propositions on the section for REALIZE
sec.__dict__["_planned_props"] = props
sec.__dict__["_node"] = node
doc.sections.append(sec)
made += 1
return doc
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"""base.py — the SurfaceProjector interface + registry.
THE key abstraction of the efferent projector: a projector is a pure function
from the surface-neutral, geometry-carrying DocumentIR to bytes on a target
SURFACE. The surface is a PARAMETER. Adding a surface = registering one more
projector; nothing upstream (plan/realize/cohere) changes.
DocumentIR --project--> bytes (per surface)
A TEXT projector reads ``block.sentences``. A NON-TEXT projector (music, image,
video) reads ``block.provenance`` — the geometry the IR carries — and decodes it
onto its surface. Both consume the SAME IR. That symmetry is the whole design:
the realizer generalizes into a multimodal projector, geometry -> any surface.
"""
from __future__ import annotations
from typing import Protocol, runtime_checkable
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from document_ir import DocumentIR # noqa: E402
@runtime_checkable
class SurfaceProjector(Protocol):
"""Geometry-document -> one surface. Implementations MUST be pure & faithful.
THE ONE SHARED SEAM. Every surface — text, music, image, video — conforms to
this single contract:
project(frame: DocumentIR) -> bytes
where ``frame`` is the geometry-carrying meaning-geometry (the SemFrame at
document scale; a single utterance is the degenerate one-section frame).
RECOMMENDED INTERNAL SHAPE (the peer music/text decomposition, blessed here
so all surfaces share it): a projector may split ``project`` into
spec = self.plan(frame) # meaning-geometry -> surface-specific spec
bytes = self.realize(spec) # spec -> surface, via this projector's PROFILE
``project`` is then ``realize(plan(frame))``. The PROFILE (a text lang-profile,
a music instr/mode-profile, an image layout-profile) is a property of the
projector instance — the pluggable knob. See :class:`TwoStageProjector`.
A TEXT projector's plan reads ``frame`` sentences; a MUSIC/IMAGE projector's
plan reads ``frame.all_provenance()`` — the geometry — and derives its spec
(pitch/harmony/rhythm, or layout) FROM the meaning, deterministically. Same
frame, different profile.
"""
surface: str # "markdown" | "docx" | "midi" | "audio" | "image" | "video"
media_type: str # MIME type of the emitted bytes
ext: str # file extension (no dot)
modality: str # "text" | "audio" | "image" | "video"
profile: object # the pluggable per-surface profile (may be None)
def project(self, doc: DocumentIR) -> bytes:
"""Emit the document on this surface. Returns raw bytes."""
...
class TwoStageProjector:
"""Optional base for the peer plan()/realize() decomposition.
Subclasses implement ``plan(frame) -> spec`` and ``realize(spec) -> bytes``;
``project`` is their composition. This is exactly the peer music interface
(spec = plan(frame, profile); surface = realize(spec, profile)) expressed so
that it still satisfies the single ``SurfaceProjector.project`` seam. Text,
music, and image projectors can all subclass this and remain interchangeable.
"""
surface: str = ""
media_type: str = ""
ext: str = ""
modality: str = ""
profile: object = None
def plan(self, doc: DocumentIR): # -> spec
raise NotImplementedError
def realize(self, spec) -> bytes:
raise NotImplementedError
def project(self, doc: DocumentIR) -> bytes:
return self.realize(self.plan(doc))
_REGISTRY: dict[str, SurfaceProjector] = {}
def register(projector: SurfaceProjector) -> SurfaceProjector:
_REGISTRY[projector.surface] = projector
return projector
def get_projector(surface: str) -> SurfaceProjector:
if surface not in _REGISTRY:
raise KeyError(f"no projector registered for surface {surface!r}; "
f"have {sorted(_REGISTRY)}")
return _REGISTRY[surface]
def available_surfaces() -> list[str]:
return sorted(_REGISTRY)
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"""docx.py — the .docx surface projector: an OWN minimal OOXML emitter.
Own-the-core: a .docx is just a ZIP of a few XML parts (WordprocessingML). We
emit it with the standard library only — ``zipfile`` + string XML — no
python-docx, no external dependency. This proves a "richer structured format"
surface without importing anyone else's toolkit.
Parts emitted (the minimal valid set + a styles part for real headings):
[Content_Types].xml
_rels/.rels
word/_rels/document.xml.rels
word/styles.xml (Title / Heading1 / Heading2 / Normal)
word/document.xml (the content)
Like the markdown projector it reads only the IR's realized sentences; it
invents nothing. The surface differs, the faithful content does not.
"""
from __future__ import annotations
import io
import os
import sys
import zipfile
from xml.sax.saxutils import escape
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from document_ir import DocumentIR # noqa: E402
from projectors.base import register # noqa: E402
_CONTENT_TYPES = """<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<Types xmlns="http://schemas.openxmlformats.org/package/2006/content-types">
<Default Extension="rels" ContentType="application/vnd.openxmlformats-package.relationships+xml"/>
<Default Extension="xml" ContentType="application/xml"/>
<Override PartName="/word/document.xml" ContentType="application/vnd.openxmlformats-officedocument.wordprocessingml.document.main+xml"/>
<Override PartName="/word/styles.xml" ContentType="application/vnd.openxmlformats-officedocument.wordprocessingml.styles+xml"/>
</Types>"""
_RELS = """<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">
<Relationship Id="rId1" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/officeDocument" Target="word/document.xml"/>
</Relationships>"""
_DOC_RELS = """<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">
<Relationship Id="rId1" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/styles" Target="styles.xml"/>
</Relationships>"""
_W = "http://schemas.openxmlformats.org/wordprocessingml/2006/main"
_STYLES = f"""<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<w:styles xmlns:w="{_W}">
<w:style w:type="paragraph" w:default="1" w:styleId="Normal"><w:name w:val="Normal"/>
<w:rPr><w:sz w:val="22"/></w:rPr></w:style>
<w:style w:type="paragraph" w:styleId="Title"><w:name w:val="Title"/>
<w:pPr><w:spacing w:after="240"/></w:pPr>
<w:rPr><w:b/><w:sz w:val="52"/></w:rPr></w:style>
<w:style w:type="paragraph" w:styleId="Subtitle"><w:name w:val="Subtitle"/>
<w:rPr><w:i/><w:sz w:val="28"/><w:color w:val="555555"/></w:rPr></w:style>
<w:style w:type="paragraph" w:styleId="Heading1"><w:name w:val="heading 1"/>
<w:pPr><w:spacing w:before="240" w:after="120"/><w:outlineLvl w:val="0"/></w:pPr>
<w:rPr><w:b/><w:sz w:val="34"/></w:rPr></w:style>
<w:style w:type="paragraph" w:styleId="Heading2"><w:name w:val="heading 2"/>
<w:pPr><w:spacing w:before="200" w:after="100"/><w:outlineLvl w:val="1"/></w:pPr>
<w:rPr><w:b/><w:sz w:val="28"/></w:rPr></w:style>
</w:styles>"""
def _para(text: str, style: str | None = None) -> str:
ppr = f"<w:pPr><w:pStyle w:val=\"{style}\"/></w:pPr>" if style else ""
return (f"<w:p>{ppr}<w:r><w:t xml:space=\"preserve\">"
f"{escape(text)}</w:t></w:r></w:p>")
class DocxProjector:
surface = "docx"
media_type = ("application/vnd.openxmlformats-officedocument."
"wordprocessingml.document")
ext = "docx"
modality = "text"
def _document_xml(self, doc: DocumentIR) -> str:
body: list[str] = [_para(doc.title, "Title")]
if doc.subtitle:
body.append(_para(doc.subtitle, "Subtitle"))
abstract = doc.meta.get("abstract")
if abstract is not None and abstract.sentences:
body.append(_para(abstract.text()))
for sec in doc.sections:
style = "Heading1" if sec.level <= 1 else "Heading2"
body.append(_para(sec.heading, style))
for block in sec.blocks:
t = block.text()
if t:
body.append(_para(t))
return (f"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"yes\"?>"
f"<w:document xmlns:w=\"{_W}\"><w:body>"
+ "".join(body)
+ "<w:sectPr><w:pgSz w:w=\"12240\" w:h=\"15840\"/>"
"<w:pgMar w:top=\"1440\" w:right=\"1440\" w:bottom=\"1440\" "
"w:left=\"1440\"/></w:sectPr></w:body></w:document>")
def project(self, doc: DocumentIR) -> bytes:
buf = io.BytesIO()
with zipfile.ZipFile(buf, "w", zipfile.ZIP_DEFLATED) as z:
z.writestr("[Content_Types].xml", _CONTENT_TYPES)
z.writestr("_rels/.rels", _RELS)
z.writestr("word/_rels/document.xml.rels", _DOC_RELS)
z.writestr("word/styles.xml", _STYLES)
z.writestr("word/document.xml", self._document_xml(doc))
return buf.getvalue()
register(DocxProjector())
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"""markdown.py — the Markdown surface projector (text facet).
The most tractable surface, and the reference implementation: reads the IR's
realized sentences and lays them out as Markdown. Introduces no content — it is
pure typography over the faithful text the realizer produced.
"""
from __future__ import annotations
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from document_ir import DocumentIR # noqa: E402
from projectors.base import register # noqa: E402
class MarkdownProjector:
surface = "markdown"
media_type = "text/markdown"
ext = "md"
modality = "text"
def render_str(self, doc: DocumentIR) -> str:
lines: list[str] = [f"# {doc.title}"]
if doc.subtitle:
lines.append(f"\n*{doc.subtitle}*")
abstract = doc.meta.get("abstract")
if abstract is not None and abstract.sentences:
lines.append("")
lines.append(abstract.text())
for sec in doc.sections:
lines.append("")
lines.append(f"{'#' * max(2, sec.level)} {sec.heading}")
for block in sec.blocks:
body = block.text()
if body:
lines.append("")
lines.append(body)
return "\n".join(lines) + "\n"
def project(self, doc: DocumentIR) -> bytes:
return self.render_str(doc).encode("utf-8")
register(MarkdownProjector())
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"""midi.py — the MUSIC surface projector: geometry -> symbolic music (MIDI).
The first NON-TEXT surface, and the proof of the general shape. "Music is
language and it is math" (Will): symbolic music is tractable and geometry-native,
so it is the natural efferent twin to try first after text.
CRUCIALLY this projector does NOT read the realized sentences. It reads the IR's
GEOMETRY facet — ``block.provenance`` — and DECODES each edge onto a musical
surface. That is the whole thesis of the multimodal projector: the same
geometry-carrying IR drives text AND music; a text projector reads the words, a
music projector reads the meaning-geometry. The mapping is deterministic and
faithful to the geometry's structure:
relation lemma -> scale degree (same relation -> same pitch class;
meaning has a consistent sonic form)
polarity -> mode (aff = major third above; neg = minor
third / lowered — SACRED polarity is
audible, a negated edge sounds negated)
confidence -> note duration (stronger grounding rings longer)
importance -> velocity (more important source = louder)
section -> phrase + register shift (structure becomes musical form)
Own-the-core: a Standard MIDI File is a header chunk + a track chunk of
delta-timed events. We emit the raw bytes with ``struct`` — no external MIDI
library. Format 0, one track.
"""
from __future__ import annotations
import io
import os
import struct
import sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from document_ir import DocumentIR, Provenance # noqa: E402
from projectors.base import TwoStageProjector, register # noqa: E402
_TICKS = 480 # ticks per quarter note
_C_MAJOR = [0, 2, 4, 5, 7, 9, 11] # semitone offsets of a diatonic scale
def _vlq(n: int) -> bytes:
"""MIDI variable-length quantity encoding of a delta time."""
if n == 0:
return b"\x00"
out = bytearray()
out.append(n & 0x7F)
n >>= 7
while n:
out.insert(0, (n & 0x7F) | 0x80)
n >>= 7
return bytes(out)
def _degree_for(relation: str) -> int:
"""Stable scale degree for a relation lemma (same relation -> same pitch)."""
if not relation:
return 0
return sum(ord(c) for c in relation.lower()) % len(_C_MAJOR)
def _note_for(p: Provenance, base: int) -> tuple[int, int, int]:
"""(pitch, velocity, duration_ticks) for one geometry edge."""
root = base + _C_MAJOR[_degree_for(p.relation)]
# polarity -> mode: affirmed edges take the bright major third, negated edges
# take the darker minor third. The negation is AUDIBLE and never dropped.
third = 4 if p.polarity == "aff" else 3
pitch = max(24, min(96, root + (third if p.confidence >= 0.5 else 0)))
velocity = int(56 + 60 * min(1.0, max(0.0, p.importance)))
velocity = max(40, min(120, velocity))
# confidence -> duration: quarter .. dotted-half
dur = int(_TICKS * (0.5 + 1.5 * min(1.0, max(0.0, p.confidence))))
return pitch, velocity, dur
# a mode-profile: the pluggable musical knob (the peer's mode_profile). Scale +
# tempo. Swapping this profile re-voices the SAME geometry — surface as parameter.
_DEFAULT_PROFILE = {"scale": _C_MAJOR, "tempo_us": 500000,
"registers": [60, 55, 64, 50, 67, 48], "program": 0}
class MidiProjector(TwoStageProjector):
"""geometry -> symbolic music, in the shared two-stage shape.
``plan(frame)`` -> a music_spec: an ordered list of note dicts derived
deterministically from the frame's provenance geometry
(the peer's ``plan(frame, profile) -> spec``).
``realize(spec)`` -> Standard MIDI File bytes (the peer's
``realize(spec, profile) -> surface``; here the surface
is symbolic MIDI, the minimal audio proof — a richer
additive-synth audio projector conforms identically).
"""
surface = "midi"
media_type = "audio/midi"
ext = "mid"
modality = "audio"
def __init__(self, profile: dict | None = None):
self.profile = profile or _DEFAULT_PROFILE
# -- stage 1: meaning-geometry -> music_spec (reads the GEOMETRY facet) -- #
def plan(self, doc: DocumentIR) -> list[dict]:
registers = self.profile["registers"]
spec: list[dict] = []
for si, sec in enumerate(doc.sections):
base = registers[si % len(registers)]
provs = [p for p in sec.all_provenance()
if p.kind in ("fact", "interpretation")]
for i, p in enumerate(provs):
pitch, vel, dur = _note_for(p, base)
spec.append({"pitch": pitch, "velocity": vel, "dur": dur,
"rest_before": (_TICKS // 2) if (si > 0 and i == 0) else 0,
"relation": p.relation, "polarity": p.polarity})
return spec
# -- stage 2: music_spec -> MIDI bytes (own-core, no library) ------------ #
def realize(self, spec: list[dict]) -> bytes:
ev = bytearray()
ev += _vlq(0) + b"\xFF\x51\x03" + struct.pack(">I", self.profile["tempo_us"])[1:]
ev += _vlq(0) + bytes([0xC0, self.profile["program"] & 0x7F])
for note in spec:
ev += _vlq(note["rest_before"]) + bytes([0x90, note["pitch"], note["velocity"]])
ev += _vlq(note["dur"]) + bytes([0x80, note["pitch"], 0])
ev += _vlq(0) + b"\xFF\x2F\x00"
track = bytes(ev)
buf = io.BytesIO()
buf.write(b"MThd" + struct.pack(">IHHH", 6, 0, 1, _TICKS))
buf.write(b"MTrk" + struct.pack(">I", len(track)) + track)
return buf.getvalue()
register(MidiProjector())
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"""seams.py — documented efferent seams for IMAGE and VIDEO surfaces.
These are NOT implemented (per the build rails: architect, do not overbuild).
They are registered as first-class seams so the interface PROVES it accepts
future non-text projectors without any upstream change. Each documents exactly
what its decoder would read from the geometry-carrying IR, making the multimodal
generalization concrete rather than hand-wavy.
The symmetry that guarantees these are possible, not moonshots: they are the
efferent twins of multimodal INGEST. If meaning can HOLD an image (ingest as
first-class geometry), meaning can PROJECT one back. Video = image x sound x
TIME, and the engram already stores time (chronoception). So video falls out of
an image projector + the music projector + the stored temporal ordering.
"""
from __future__ import annotations
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from document_ir import DocumentIR # noqa: E402
from projectors.base import register # noqa: E402
class _Seam:
"""A registered-but-unimplemented projector. Names its decoder contract."""
def project(self, doc: DocumentIR) -> bytes: # pragma: no cover - seam
raise NotImplementedError(
f"{self.surface!r} projector is a documented seam, not yet built. "
f"Decoder contract: {self.decoder_contract}")
class ImageProjector(_Seam):
surface = "image"
media_type = "image/png"
ext = "png"
modality = "image"
decoder_contract = (
"reads block.provenance as a spatial layout — nodes become regions, edges "
"become adjacencies; salience/importance drive size/contrast; polarity "
"drives figure/ground. The efferent twin of image ingest (a geometry->raster "
"decoder, learned or engineered), exactly mirroring the embedder that turned "
"the image INTO geometry.")
class VideoProjector(_Seam):
surface = "video"
media_type = "video/mp4"
ext = "mp4"
modality = "video"
decoder_contract = (
"image x sound x TIME. Composes the image projector (per-keyframe geometry "
"layout) with the midi/music projector (score) along the geometry's stored "
"temporal ordering (chronoception). Needs no new principle once image + music "
"exist — only a muxer.")
register(ImageProjector())
register(VideoProjector())
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"""provenance.py — the faithfulness audit + geometry->section trace.
A document projected from geometry is only worth anything if every claim traces
back. This module walks the DocumentIR and proves the discipline held:
* ZERO ungrounded claims (every fact/interpretation has a real node id),
* every emitted sentence maps to a geometry edge (or is a marked connective),
* SACRED polarity survived (negations are reported, never silently dropped),
* COHERE introduced no new geometry (connectives carry no claim).
It emits both a machine verdict and a human-readable geometry->section table.
"""
from __future__ import annotations
from document_ir import DocumentIR
def audit(doc: DocumentIR) -> dict:
provs = doc.all_provenance()
facts = [p for p in provs if p.kind in ("fact", "interpretation")]
connectives = [p for p in provs if p.kind == "connective"]
ungrounded = [p for p in facts if not p.node_id]
negations = [p for p in facts if p.polarity == "neg"]
node_ids = sorted({p.node_id for p in facts if p.node_id})
return {
"claims": len(facts),
"connectives": len(connectives),
"ungrounded_claims": len(ungrounded),
"negations_preserved": len(negations),
"distinct_source_nodes": len(node_ids),
"faithful": len(ungrounded) == 0,
"source_nodes": node_ids,
}
def trace_table(doc: DocumentIR) -> str:
"""Human-readable geometry -> section -> claim provenance table."""
lines = ["# Provenance — every claim traces geometry", ""]
lines.append(f"**Document:** {doc.title}")
a = audit(doc)
lines.append(f"**Claims:** {a['claims']} · **Ungrounded:** "
f"{a['ungrounded_claims']} · **Negations preserved:** "
f"{a['negations_preserved']} · **Source nodes:** "
f"{a['distinct_source_nodes']} · **Faithful:** "
f"{'YES' if a['faithful'] else 'NO'}")
lines.append("")
for si, sec in enumerate(doc.sections, 1):
lines.append(f"## {si}. {sec.heading}")
lines.append(f"_seed nodes: {', '.join(i[:8] for i in sec.seed_ids)}_")
lines.append("")
lines.append("| # | realized claim | traces geometry edge |")
lines.append("|---|----------------|----------------------|")
n = 0
for block in sec.blocks:
for sent, prov in zip(block.sentences, block.provenance):
if prov.kind == "connective":
continue
n += 1
edge = prov.trace().replace("|", "\\|")
s = sent.replace("|", "\\|")
lines.append(f"| {n} | {s} | {edge} |")
lines.append("")
return "\n".join(lines) + "\n"
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"""realize.py — REALIZE stage: fill each planned section with faithful passages.
Scales the PROVEN realizer from a single assertion to a passage. For each
planned proposition we build a realizer-ready clause (the proven
``_prop_to_clause`` mapping) and run it through the proven engine
(``engine.realize``), which is a deterministic grammar with the SACRED negation
contract — it never invents. Each realized sentence is paired with a
:class:`Provenance` that pins it to the exact geometry edge it came from.
"Passage, not a list of sentences": within a section we lightly vary sentence
openings and group related claims, but we add NO content the geometry did not
assert. The only non-geometry words are function words the grammar already owns
(articles, "and", conjunction of same-subject claims). Document-level flow is
COHERE's job; this stage owns intra-section fluency + fidelity.
"""
from __future__ import annotations
import os
import sys
_NT = os.path.expanduser("~/Desktop/neuron-talk")
_LR = os.path.expanduser("~/Desktop/lang-realizers")
for _p in (_NT, _LR):
if _p not in sys.path:
sys.path.insert(0, _p)
import engine # noqa: E402 (the proven no-LLM realizer)
from dialogue import _prop_to_clause # noqa: E402 (proven prop -> clause)
from document_ir import Block, DocumentIR, Provenance, Section # noqa: E402
def _provenance_from(p, kind: str = "fact") -> Provenance:
return Provenance(
subj_id=p.source_node_id, subject=p.subject, relation=p.predicate,
obj=p.object, polarity=p.polarity, confidence=round(float(p.confidence), 3),
node_id=p.source_node_id, kind=kind,
importance=float(getattr(p, "node_importance", 0.0) or 0.0),
salience=0.0,
)
import re as _re
# a well-formed declarative opens with a determiner, a proper noun, "I", or a
# capitalized head — not a mis-parsed object pronoun or a copula fragment.
_BAD_OPENERS = _re.compile(r"^(Me |It is I|There is|This is it|That is it)\b")
_VACUOUS = _re.compile(r"^\w+ (is|are|was|were) (it|no|nothing|empty|those|this|that)\.?$",
_re.I)
def _good_sentence(text: str) -> bool:
"""Fluency gate — drops degenerate realizations. NEVER loosens faithfulness;
it only refuses to SPEAK a claim whose surface came out malformed."""
words = text.rstrip(".").split()
if len(words) < 3:
return False
if _BAD_OPENERS.search(text):
return False
if _VACUOUS.match(text):
return False
# a sentence that is mostly one-letter/two-letter tokens is a parse artifact
short = sum(1 for w in words if len(w.strip(".,'")) <= 2)
if short > len(words) / 2:
return False
return True
def _realize_prop(p, lang: str = "en") -> tuple[str, Provenance] | None:
"""One proposition -> (faithful sentence, provenance) or None if it drops."""
clause = _prop_to_clause(p)
text = engine.realize(clause, lang)
if not text or not text.strip():
return None
text = text.strip()
if not text.endswith((".", "!", "?")):
text += "."
# capitalize first character (proper nouns / "I" already handled by grammar)
text = text[0].upper() + text[1:]
if not _good_sentence(text):
return None
return text, _provenance_from(p)
def realize_document(doc: DocumentIR, lang: str = "en") -> DocumentIR:
"""Fill every planned section's blocks with faithful, realized passages."""
for sec in doc.sections:
planned = sec.__dict__.get("_planned_props", [])
block = Block(role="body")
summary_bits: list[str] = []
for p in planned:
r = _realize_prop(p, lang)
if r is None:
continue
text, prov = r
block.sentences.append(text)
block.provenance.append(prov)
if len(summary_bits) < 1:
# a short grounded gloss for TOC / pptx bullets
obj = (prov.obj or "").strip().rstrip(".")
if obj:
summary_bits.append(obj)
if block.sentences:
sec.blocks.append(block)
sec.summary = summary_bits[0] if summary_bits else ""
# drop the transient planning payload; the IR is now self-contained
sec.__dict__.pop("_planned_props", None)
sec.__dict__.pop("_node", None)
# prune sections that realized to nothing
doc.sections = [s for s in doc.sections if s.blocks]
return doc
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// accent.el - A British-RP ACCENT as an INGESTED TRANSFORM-GEOMETRY, composed
// onto the voice (voice (+) accent, SEPARABLE). Reads elp/data/british-accent.psv
// into an accent MANIFOLD in the engram (override nodes + a shared accent hub),
// and the render reads the RP formant overrides + the non-rhotic rule back from
// that geometry. NO accent targets live in code same discipline as the base
// phonetics. PROVENANCE NOTE: the RP Hz values are PROVISIONAL (reconstructed-
// from-knowledge approximations, cite Deterding1997 / Hawkins&Midgley2005 /
// Wells1982) pending transcription from the published tables the PIPELINE is
// the deliverable; exact values are being source-verified separately.
fn ingest_accent(path: String) -> [String] {
let content: String = fs_read(path)
let lines: [String] = str_split(content, "\n")
let nl: Int = native_list_len(lines)
let amap: [String] = native_list_empty()
let hub: String = engram_node("accent british-rp prov=PROVISIONAL cite=Deterding1997-HawkinsMidgley2005-Wells1982", "Accent", 80)
let li: Int = 0
while li < nl {
let line: String = native_list_get(lines, li)
let ll: Int = str_len(line)
let skip: Int = 0
if ll < 3 {
skip = 1
}
if skip == 0 {
let first: Int = str_char_code(line, 0)
if first == 35 {
skip = 1
}
}
if skip == 0 {
let f: [String] = str_split(line, "|")
let nf: Int = native_list_len(f)
if nf >= 6 {
let key: String = native_list_get(f, 0)
let f1: String = native_list_get(f, 1)
let f2: String = native_list_get(f, 2)
let f3: String = native_list_get(f, 3)
let kind: String = native_list_get(f, 4)
let set: String = native_list_get(f, 5)
let cont: String = "accent british-rp " + key + " f1=" + f1 + " f2=" + f2 + " f3=" + f3 + " kind=" + kind + " set=" + set + " prov=PROVISIONAL cite=Deterding1997-HawkinsMidgley2005-Wells1982"
let id: String = engram_node(cont, "AccentTarget", 80)
amap = native_list_append(amap, key)
amap = native_list_append(amap, cont)
engram_connect(id, hub, 80, "of_accent")
}
}
li = li + 1
}
return amap
}
// RP formant override for a phoneme, read from the accent manifold. Returns
// [f1,f2,f3] for a vowel_override record, or an empty list if none / a rule.
fn accent_formants(amap: [String], code: String) -> [Int] {
let out: [Int] = native_list_empty()
let id: String = sp_map_get(amap, code)
if str_eq(id, "") {
return out
}
let j: String = id
let isrule: Int = str_index_of(j, "drop_coda")
if isrule >= 0 {
return out
}
let f1: Int = parse_uint_from(j, "f1=")
if f1 <= 0 {
return out
}
let out = native_list_append(out, f1)
let out = native_list_append(out, parse_uint_from(j, "f2="))
let out = native_list_append(out, parse_uint_from(j, "f3="))
return out
}
// Is this accent non-rhotic? (reads the R rule node from the manifold)
fn is_nonrhotic(amap: [String]) -> Int {
let id: String = sp_map_get(amap, "R")
if str_eq(id, "") {
return 0
}
let hit: Int = str_index_of(id, "drop_coda")
if hit >= 0 {
return 1
}
return 0
}
// Is this symbol a vowel? Membership in the vowel-set derived from the phonetics
// source's class column (phonological structure the FORMANT NUMBERS still come
// from the organ manifold; this is only the categorical class for the rule).
fn is_vowel_sym(vset: [String], sym: String) -> Int {
let n: Int = native_list_len(vset)
let i: Int = 0
while i < n {
if str_eq(native_list_get(vset, i), sym) {
return 1
}
i = i + 1
}
return 0
}
// Non-rhotic transform: drop a post-vocalic CODA /R/ an R whose next non-SIL
// phoneme is NOT a vowel (a consonant, or end of utterance). Keep INTERVOCALIC/
// onset R (next non-SIL phoneme is a vowel, e.g. the medial R in N UW R AA N).
fn apply_rhoticity(codes: [String], vset: [String]) -> [String] {
let n: Int = native_list_len(codes)
let out: [String] = native_list_empty()
let i: Int = 0
while i < n {
let c: String = native_list_get(codes, i)
let keep: Int = 1
if str_eq(c, "R") {
let jx: Int = i + 1
let nextv: Int = 0
while jx < n {
let ncode: String = native_list_get(codes, jx)
if str_eq(ncode, "SIL") {
jx = jx + 1
} else {
nextv = is_vowel_sym(vset, ncode)
jx = n + 1000
}
}
if nextv == 0 {
keep = 0
}
}
if keep == 1 {
out = native_list_append(out, c)
}
i = i + 1
}
return out
}
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// audio-demo.el - Drive the native audio surface: render a tone per instrument
// from its LEARNED signature, then render a small meaning-phrase "piece".
// Entry point: top-level statement calls main() (same convention as the
// examples' top-level println(run_test())).
fn micros_to_str(xs: [Int]) -> String {
let n: Int = native_list_len(xs)
let out: String = ""
let i: Int = 0
while i < n {
if i > 0 { let out: String = out + "," }
let out: String = out + int_to_str(native_list_get(xs, i))
let i: Int = i + 1
}
return out
}
// Render a 1.0s A4 (midi 69) tone from a signature file, print the parsed
// partials (proving the numbers came from the engram .sig), write the WAV.
fn render_tone(name: String, sigpath: String, outpath: String, table: [Int]) -> Int {
let lines: [String] = sig_load(sigpath)
let partials: [Int] = parse_micros(sig_field(lines, "partials"))
println("[" + name + "] partials_n=" + sig_field(lines, "partials_n") + " parsed_partials_micro(scale 1e6)=" + micros_to_str(partials))
println("[" + name + "] raw partials line from .sig = " + sig_field(lines, "partials"))
let freq: Int = freq_of_midi(69)
let note: [Int] = synth_from_sig(lines, freq, 1000, 900, 44100, table)
let n: Int = native_list_len(note)
let ok: Int = wav_write(outpath, note, n, 44100)
println("[" + name + "] rendered " + int_to_str(n) + " samples -> " + outpath + " (write_ok=" + int_to_str(ok) + ")")
return n
}
fn run_demo() -> Int {
let table: [Int] = sin_table()
fs_mkdir("/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out")
println("=== TONES: render A4 (midi 69) from each learned signature ===")
render_tone("flute", "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/sig/flute.sig", "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out/tone-flute.wav", table)
render_tone("clarinet", "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/sig/clarinet.sig", "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out/tone-clarinet.wav", table)
render_tone("violin", "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/sig/violin.sig", "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out/tone-violin.wav", table)
render_tone("piano", "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/sig/piano.sig", "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out/tone-piano.wav", table)
render_tone("organ", "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/sig/organ.sig", "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out/tone-organ.wav", table)
println("")
println("=== PIECE: a 6-frame meaning phrase (incl. a NEG frame) ===")
let frames: [[String]] = native_list_empty()
let frames: [[String]] = native_list_append(frames, audio_frame("agent", "aff", "0.9", "0.8", "0", "s1"))
let frames: [[String]] = native_list_append(frames, audio_frame("theme", "aff", "0.7", "0.6", "0", "s2"))
let frames: [[String]] = native_list_append(frames, audio_frame("cause", "aff", "0.8", "0.9", "1", "s3"))
let frames: [[String]] = native_list_append(frames, audio_frame("negation", "neg", "0.85", "0.7", "0", "s4"))
let frames: [[String]] = native_list_append(frames, audio_frame("goal", "aff", "0.6", "0.5", "1", "s5"))
let frames: [[String]] = native_list_append(frames, audio_frame("result", "aff", "0.95", "1.0", "0", "s6"))
// Print the plan so the NEG frame's minor third (+3) vs major (+4) is visible.
let nf: Int = native_list_len(frames)
let fi: Int = 0
while fi < nf {
let frame: [String] = native_list_get(frames, fi)
let plan: [Int] = plan_note(frame)
let pol: String = surface_get(frame, "polarity")
let third_name: String = "major(+4)"
if str_eq(pol, "neg") { let third_name: String = "MINOR(+3)" }
println("frame " + int_to_str(fi) + " relation=" + surface_get(frame, "relation") + " polarity=" + pol + " -> midi=" + int_to_str(native_list_get(plan, 0)) + " dur_ms=" + int_to_str(native_list_get(plan, 1)) + " amp_pm=" + int_to_str(native_list_get(plan, 2)) + " third=" + third_name)
let fi: Int = fi + 1
}
let piano_lines: [String] = sig_load("/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/sig/piano.sig")
let total: Int = realize_audio(frames, piano_lines, "/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out/piece.wav", 44100, table)
println("PIECE rendered " + int_to_str(total) + " samples -> /Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out/piece.wav")
return total
}
println("audio-demo main returned samples=" + int_to_str(run_demo()))
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// audio-surface.el - Native own-core additive-synthesis audio surface.
//
// The AUDIO efferent seam, native, no Python and no library. This renders real
// PCM .wav bytes from instrument SIGNATURES read from engram-sourced .sig data
// files (elp/faculty/sig/*.sig) - the partial amplitudes are NEVER literals in
// this source; they are parsed from the learned signature at run time. That is
// the whole proof: render-from-learned-signatures.
//
// EL has no float arithmetic operator (codegen emits raw int64 ops for + - * /
// on the shared 64-bit slot) and no float-arithmetic natives - so ALL synthesis
// math here is own-core INTEGER fixed-point. Angles use a quarter-wave sine
// table (scale 10000) from a fixed-point Taylor series; amplitudes are parsed to
// micro (scale 1e6) straight from the .sig text; frequencies are milliHz ints.
//
// Pipeline mirrors the two-stage projector (midi.py): plan_note(frame) reads a
// frame's meaning-geometry slot-map and derives (pitch, duration, amplitude);
// realize_audio SUPERPOSES the signature's partials (the compose op) and
// serialises RIFF/WAVE. Same frame -> midi OR audio.
// -- integer decimal + string helpers -----------------------------------------
fn str_to_int_el(s: String) -> Int {
let n: Int = str_len(s)
let i: Int = 0
let v: Int = 0
let neg: Bool = false
while i < n {
let c: Int = str_char_code(s, i)
if c == 45 { let neg: Bool = true }
if c >= 48 {
if c < 58 {
let v: Int = v * 10 + (c - 48)
}
}
let i: Int = i + 1
}
if neg { return 0 - v }
return v
}
fn parse_micro(s: String) -> Int {
let dot: Int = str_index_of(s, ".")
if dot < 0 {
return str_to_int_el(s) * 1000000
}
let n: Int = str_len(s)
let ipart: String = str_slice(s, 0, dot)
let fpart: String = str_slice(s, dot + 1, n)
let iv: Int = str_to_int_el(ipart)
let fv: Int = 0
let scale: Int = 100000
let fn2: Int = str_len(fpart)
let i: Int = 0
while i < 6 {
let d: Int = 0
if i < fn2 {
let d: Int = str_char_code(fpart, i) - 48
}
let fv: Int = fv + d * scale
let scale: Int = scale / 10
let i: Int = i + 1
}
return iv * 1000000 + fv
}
// -- signature (engram data file) loader ---------------------------------------
fn sig_load(path: String) -> [String] {
let text: String = fs_read(path)
return str_split(text, "\n")
}
fn sig_field(lines: [String], key: String) -> String {
let pref: String = key + ": "
let n: Int = native_list_len(lines)
let plen: Int = str_len(pref)
let i: Int = 0
while i < n {
let ln: String = native_list_get(lines, i)
if str_starts_with(ln, pref) {
return str_slice(ln, plen, str_len(ln))
}
let i: Int = i + 1
}
return ""
}
fn parse_micros(csv: String) -> [Int] {
let parts: [String] = str_split(csv, ",")
let n: Int = native_list_len(parts)
let out: [Int] = native_list_empty()
let i: Int = 0
while i < n {
let out: [Int] = native_list_append(out, parse_micro(native_list_get(parts, i)))
let i: Int = i + 1
}
return out
}
// -- fixed-point sine (own-core, quarter-wave Taylor table, scale 10000) --------
fn sin_table() -> [Int] {
let HP: Int = 1570796
let t: [Int] = native_list_empty()
let q: Int = 0
while q < 257 {
let x: Int = q * HP / 256
let x2: Int = x * x / 1000000
let x3: Int = x2 * x / 1000000
let x5: Int = x3 * x2 / 1000000
let x7: Int = x5 * x2 / 1000000
let x9: Int = x7 * x2 / 1000000
let s: Int = x - x3 / 6 + x5 / 120 - x7 / 5040 + x9 / 362880
let t: [Int] = native_list_append(t, s / 100)
let q: Int = q + 1
}
return t
}
fn sin_lookup(t: [Int], phase: Int) -> Int {
let p: Int = phase % 1024
if p < 0 { let p: Int = p + 1024 }
let quad: Int = p / 256
let r: Int = p % 256
if quad == 0 { return native_list_get(t, r) }
if quad == 1 { return native_list_get(t, 256 - r) }
if quad == 2 { return 0 - native_list_get(t, r) }
return 0 - native_list_get(t, 256 - r)
}
fn isqrt_int(n: Int) -> Int {
if n <= 0 { return 0 }
let x: Int = n
let y: Int = (x + 1) / 2
while y < x {
let x: Int = y
let y: Int = (x + n / x) / 2
}
return x
}
// freq_of_midi: equal-tempered frequency in milliHz. 440000 mHz at midi 69.
fn freq_of_midi(m: Int) -> Int {
let f: Int = 440000
if m > 69 {
let k: Int = m - 69
let i: Int = 0
while i < k {
let f: Int = f * 1059463 / 1000000
let i: Int = i + 1
}
return f
}
if m < 69 {
let k: Int = 69 - m
let i: Int = 0
while i < k {
let f: Int = f * 1000000 / 1059463
let i: Int = i + 1
}
return f
}
return f
}
// -- envelope (ADSR), scale 1000 -----------------------------------------------
fn adsr_env(i: Int, total: Int, atk_n: Int, dec_n: Int, sus_pm: Int, rel_n: Int) -> Int {
if i < atk_n {
if atk_n == 0 { return 1000 }
return 1000 * i / atk_n
}
if i < atk_n + dec_n {
if dec_n == 0 { return sus_pm }
return 1000 - (1000 - sus_pm) * (i - atk_n) / dec_n
}
let rel_start: Int = total - rel_n
if i < rel_start {
return sus_pm
}
if rel_n == 0 { return 0 }
let left: Int = total - i
return sus_pm * left / rel_n
}
// -- note synthesis: SUPERPOSE the learned partials -> [Int] samples -----------
fn note_samples(freq_mHz: Int, dur_ms: Int, rate: Int, partials: [Int], sumP: Int, b_micro: Int, vib_rate: Int, vib_cents: Int, atk_ms: Int, dec_ms: Int, sus_pm: Int, rel_ms: Int, amp_pm: Int, table: [Int]) -> [Int] {
let total: Int = dur_ms * rate / 1000
let atk_n: Int = atk_ms * rate / 1000
let dec_n: Int = dec_ms * rate / 1000
let rel_n: Int = rel_ms * rate / 1000
let np: Int = native_list_len(partials)
let half_mhz: Int = rate * 1000 / 2
let out: [Int] = native_list_empty()
let i: Int = 0
while i < total {
let acc: Int = 0
let k: Int = 0
while k < np {
let harm: Int = k + 1
let amp_k: Int = native_list_get(partials, k)
let factor: Int = 1000000
if b_micro > 0 {
let val: Int = 1000000 + b_micro * harm * harm
let factor: Int = isqrt_int(val * 1000000)
}
let fn_mhz: Int = freq_mHz * harm
let fn_mhz: Int = fn_mhz * factor / 1000000
if vib_cents > 0 {
if vib_rate > 0 {
let vphase: Int = i * vib_rate * 1024 / rate
let vs: Int = sin_lookup(table, vphase)
let vibf: Int = 1000000 + (vib_cents * vs * 833) / 10000
let fn_mhz: Int = fn_mhz * vibf / 1000000
}
}
if fn_mhz <= half_mhz {
let phase: Int = i * fn_mhz * 1024 / (rate * 1000)
let sv: Int = sin_lookup(table, phase)
let acc: Int = acc + sv * amp_k / 1000000
}
let k: Int = k + 1
}
let env: Int = adsr_env(i, total, atk_n, dec_n, sus_pm, rel_n)
let s16: Int = acc * 2800000 / sumP
let s16: Int = s16 * env / 1000
let s16: Int = s16 * amp_pm / 1000
if s16 > 32767 { let s16: Int = 32767 }
if s16 < 0 - 32767 { let s16: Int = 0 - 32767 }
let out: [Int] = native_list_append(out, s16)
let i: Int = i + 1
}
return out
}
fn synth_from_sig(lines: [String], freq_mHz: Int, dur_ms: Int, amp_pm: Int, rate: Int, table: [Int]) -> [Int] {
let partials: [Int] = parse_micros(sig_field(lines, "partials"))
let np: Int = native_list_len(partials)
let sumP: Int = 0
let j: Int = 0
while j < np {
let pj: Int = native_list_get(partials, j)
let sumP: Int = sumP + pj
let j: Int = j + 1
}
if sumP <= 0 { let sumP: Int = 1000000 }
let adsr: [String] = str_split(sig_field(lines, "adsr"), ",")
let atk_ms: Int = parse_micro(native_list_get(adsr, 0)) / 1000
let dec_ms: Int = parse_micro(native_list_get(adsr, 1)) / 1000
let sus_pm: Int = parse_micro(native_list_get(adsr, 2)) / 1000
let rel_ms: Int = parse_micro(native_list_get(adsr, 3)) / 1000
let b_micro: Int = parse_micro(sig_field(lines, "inharmonicity_B"))
let vib_rate: Int = str_to_int_el(sig_field(lines, "vibrato_rate_hz"))
let vib_cents: Int = str_to_int_el(sig_field(lines, "vibrato_depth_cents"))
return note_samples(freq_mHz, dur_ms, rate, partials, sumP, b_micro, vib_rate, vib_cents, atk_ms, dec_ms, sus_pm, rel_ms, amp_pm, table)
}
// -- byte-buffer helpers (own-core, no library) --------------------------------
fn put_tag(buf: String, pos: Int, s: String) -> String {
let n: Int = str_len(s)
let i: Int = 0
while i < n {
let buf: String = __str_set_char(buf, pos + i, str_char_code(s, i))
let i: Int = i + 1
}
return buf
}
fn put_u32le(buf: String, pos: Int, v: Int) -> String {
let buf: String = __str_set_char(buf, pos, v % 256)
let buf: String = __str_set_char(buf, pos + 1, (v / 256) % 256)
let buf: String = __str_set_char(buf, pos + 2, (v / 65536) % 256)
let buf: String = __str_set_char(buf, pos + 3, (v / 16777216) % 256)
return buf
}
fn put_u16le(buf: String, pos: Int, v: Int) -> String {
let buf: String = __str_set_char(buf, pos, v % 256)
let buf: String = __str_set_char(buf, pos + 1, (v / 256) % 256)
return buf
}
// -- WAV serializer: own-core RIFF/WAVE, PCM mono 16-bit -----------------------
fn wav_write(path: String, samples: [Int], n: Int, rate: Int) -> Int {
let data_len: Int = n * 2
let total: Int = 44 + data_len
let buf: String = __str_alloc(total)
let buf: String = put_tag(buf, 0, "RIFF")
let buf: String = put_u32le(buf, 4, 36 + data_len)
let buf: String = put_tag(buf, 8, "WAVE")
let buf: String = put_tag(buf, 12, "fmt ")
let buf: String = put_u32le(buf, 16, 16)
let buf: String = put_u16le(buf, 20, 1)
let buf: String = put_u16le(buf, 22, 1)
let buf: String = put_u32le(buf, 24, rate)
let buf: String = put_u32le(buf, 28, rate * 2)
let buf: String = put_u16le(buf, 32, 2)
let buf: String = put_u16le(buf, 34, 16)
let buf: String = put_tag(buf, 36, "data")
let buf: String = put_u32le(buf, 40, data_len)
let i: Int = 0
while i < n {
let v: Int = native_list_get(samples, i)
if v < 0 { let v: Int = v + 65536 }
let buf: String = __str_set_char(buf, 44 + i * 2, v % 256)
let buf: String = __str_set_char(buf, 44 + i * 2 + 1, (v / 256) % 256)
let i: Int = i + 1
}
let ok: Int = fs_write_bytes(path, buf, total)
return ok
}
// -- plan: frame slot-map -> note atom (pitch, duration, amplitude) ------------
fn audio_frame(relation: String, polarity: String, confidence: String, importance: String, salience: String, subj_id: String) -> [String] {
let f: [String] = native_list_empty()
let f: [String] = native_list_append(f, "relation")
let f: [String] = native_list_append(f, relation)
let f: [String] = native_list_append(f, "polarity")
let f: [String] = native_list_append(f, polarity)
let f: [String] = native_list_append(f, "confidence")
let f: [String] = native_list_append(f, confidence)
let f: [String] = native_list_append(f, "importance")
let f: [String] = native_list_append(f, importance)
let f: [String] = native_list_append(f, "salience")
let f: [String] = native_list_append(f, salience)
let f: [String] = native_list_append(f, "subj_id")
let f: [String] = native_list_append(f, subj_id)
return f
}
fn degree_offset(deg: Int) -> Int {
if deg == 0 { return 0 }
if deg == 1 { return 2 }
if deg == 2 { return 4 }
if deg == 3 { return 5 }
if deg == 4 { return 7 }
if deg == 5 { return 9 }
return 11
}
// returns [midi, dur_ms, amp_pm]
fn plan_note(frame: [String]) -> [Int] {
let relation: String = surface_get(frame, "relation")
let polarity: String = surface_get(frame, "polarity")
let confidence: String = surface_get(frame, "confidence")
let importance: String = surface_get(frame, "importance")
let salience: String = surface_get(frame, "salience")
let rn: Int = str_len(relation)
let csum: Int = 0
let i: Int = 0
while i < rn {
let cc: Int = str_char_code(relation, i)
let csum: Int = csum + cc
let i: Int = i + 1
}
let deg: Int = csum % 7
let third: Int = 4
if str_eq(polarity, "neg") { let third: Int = 3 }
let sal_oct: Int = str_to_int_el(salience)
let doff: Int = degree_offset(deg)
let midi: Int = 60 + sal_oct * 12 + doff + third
let conf_micro: Int = parse_micro(confidence)
let dur_ms: Int = 200 + conf_micro / 1000
let imp_micro: Int = parse_micro(importance)
let amp_pm: Int = 400 + imp_micro / 2000
let out: [Int] = native_list_empty()
let out: [Int] = native_list_append(out, midi)
let out: [Int] = native_list_append(out, dur_ms)
let out: [Int] = native_list_append(out, amp_pm)
return out
}
fn realize_audio(frames: [[String]], sig_lines: [String], path: String, rate: Int, table: [Int]) -> Int {
let nf: Int = native_list_len(frames)
let all: [Int] = native_list_empty()
let count: Int = 0
let fi: Int = 0
while fi < nf {
let frame: [String] = native_list_get(frames, fi)
let plan: [Int] = plan_note(frame)
let midi: Int = native_list_get(plan, 0)
let dur_ms: Int = native_list_get(plan, 1)
let amp_pm: Int = native_list_get(plan, 2)
let freq: Int = freq_of_midi(midi)
let note: [Int] = synth_from_sig(sig_lines, freq, dur_ms, amp_pm, rate, table)
let nn: Int = native_list_len(note)
let j: Int = 0
while j < nn {
let all: [Int] = native_list_append(all, native_list_get(note, j))
let j: Int = j + 1
}
let count: Int = count + nn
let fi: Int = fi + 1
}
let ok: Int = wav_write(path, all, count, rate)
return count
}
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-16
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@@ -1,16 +0,0 @@
// comprehend.elh — public surface of the ELP comprehension front-end.
// text → meaning-spec (the input half of the ELP; inverse of the realizer).
extern fn parse_spec(text: String) -> [String]
extern fn parse_spec_lang(text: String, lang: String) -> [String]
extern fn parse_json(text: String) -> String
extern fn parse_json_lang(text: String, lang: String) -> String
// Analysis primitives (invertible morphology + deterministic grammar helpers):
extern fn cp_tokenize(text: String) -> [String]
extern fn cp_pron_concept(w: String) -> String
extern fn cp_is_negation(w: String) -> Bool
extern fn cp_is_neg_adverb(w: String) -> Bool
extern fn cp_irr2(surface: String) -> [String]
extern fn cp_reg_verb(w: String) -> [String]
extern fn cp_analyze_verb(surface: String) -> [String]
extern fn cp_verb_start(toks: [String], end: Int) -> Int
extern fn cp_subord_start(toks: [String], n: Int) -> Int
-287
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@@ -1,287 +0,0 @@
// dialogue.el SUMMON-THROUGH-SELF, native el. Port of dialogue.py's core.
//
// THE WHOLE DIALOGUE IS ONE OPERATION. A fact is never merely *fetched*: the
// query is PROJECTED into the engram's self + memory geometry, LANDS in a region,
// and the reply is READ OUT / the region MATERIALIZED from wherever it landed.
//
// project(query) -> land on a region -> read out from that region
//
// lands in the SELF region -> grounded identity/presence, read out of
// the real self nodes (self_region.el)
// lands on a memory NEIGHBORHOOD -> MATERIALIZE it: walk the neighborhood
// (engram_neighbors_json) and read out the
// region's connected members
// lands nowhere close -> HONEST ABSENCE (an empty region, not a
// fabricated answer, not an error)
//
// CRITICAL INVARIANTS (enforced structurally, not by convention):
// * ONE operation there is NO intent classifier and NO separate
// fact-retrieval branch. Identity is nearest-region proximity, not a switch.
// * MATERIALIZE by walking the neighborhood, never by fetching top-props.
// * HONEST ABSENCE when the region is thin.
// * NEGATION is SACRED: the readout is the stored prose VERBATIM, so a negated
// memory stays negated we never paraphrase a polarity away.
// * NO ECHO: the old "I noted that X. That relates to Y." template is gone.
// The summon path materializes or honestly declines it never echoes.
// * DIRECTIVE OVERRIDE: a meta-directive ("answer in English") overrides the
// reply language while the content language is still auto-detected.
//
// Depends on: comprehend (parse_spec_lang, cp_tokenize), multilingual (ml_detect,
// ml_tr, ml_term), propositions (prop_split_sentences), self_region
// (sr_available, sr_readout), the engram + json runtime builtins.
// directive override
// Return [target_lang, content]. target_lang is "" when no directive is present.
// A directive names an output language; we strip it and keep the remaining text
// as the content (whose OWN language is still auto-detected downstream).
fn dlg_dir_hit(low: String, phrase: String) -> Bool {
return str_contains(low, phrase)
}
fn dlg_parse_directive(text: String) -> [String] {
let low: String = str_to_lower(text)
let lang: String = ""
let phrase: String = ""
// English target
if dlg_dir_hit(low, "in english") { let lang = "en"; let phrase = "in english" }
if dlg_dir_hit(low, "em inglês") { let lang = "en"; let phrase = "em inglês" }
if dlg_dir_hit(low, "em ingles") { let lang = "en"; let phrase = "em ingles" }
if dlg_dir_hit(low, "en inglés") { let lang = "en"; let phrase = "en inglés" }
// Portuguese target
if dlg_dir_hit(low, "in portuguese") { let lang = "pt"; let phrase = "in portuguese" }
if dlg_dir_hit(low, "em português") { let lang = "pt"; let phrase = "em português" }
// Spanish target
if dlg_dir_hit(low, "in spanish") { let lang = "es"; let phrase = "in spanish" }
if dlg_dir_hit(low, "en español") { let lang = "es"; let phrase = "en español" }
// Italian target
if dlg_dir_hit(low, "in italian") { let lang = "it"; let phrase = "in italian" }
let content: String = text
if !str_eq(phrase, "") {
// strip the directive phrase (and a common "answer"/"responda" lead-in),
// leaving the real question as content.
let idx: Int = str_index_of(low, phrase)
if idx >= 0 {
let before: String = str_slice(text, 0, idx)
let after: String = str_slice(text, idx + str_len(phrase), str_len(text))
let content = str_trim(before + " " + after)
}
// trim a leading "answer"/"responda"/"reply" and stray colon/comma.
let cl: String = str_to_lower(content)
if str_starts_with(cl, "answer") { let content = str_trim(str_slice(content, 6, str_len(content))) }
if str_starts_with(cl, "responda") { let content = str_trim(str_slice(content, 8, str_len(content))) }
if str_starts_with(cl, "reply") { let content = str_trim(str_slice(content, 5, str_len(content))) }
if str_starts_with(content, ":") { let content = str_trim(str_slice(content, 1, str_len(content))) }
if str_starts_with(content, ",") { let content = str_trim(str_slice(content, 1, str_len(content))) }
}
let r: [String] = native_list_empty()
let r = native_list_append(r, lang)
let r = native_list_append(r, content)
return r
}
// identity landing (a region proximity, not a classifier switch)
// The query lands in the SELF region when it takes an identity/presence shape.
// Cross-lingual forms are included because the engram's lexical probe is
// English-leaning. This is the SELF attractor of the single operation.
fn dlg_is_identity(content: String) -> Bool {
let low: String = str_to_lower(str_trim(content))
if str_contains(low, "who are you") { return true }
if str_contains(low, "what are you") { return true }
if str_contains(low, "who i am") { return true }
if str_contains(low, "your name") { return true }
if str_contains(low, "about yourself") { return true }
if str_contains(low, "are you conscious") { return true }
if str_contains(low, "are you there") { return true }
// cross-lingual identity question-forms
if str_contains(low, "quem é você") { return true }
if str_contains(low, "quem es voce") { return true }
if str_contains(low, "quién eres") { return true }
if str_contains(low, "quien eres") { return true }
if str_contains(low, "chi sei") { return true }
if str_contains(low, "qui es-tu") { return true }
if str_contains(low, "wer bist du") { return true }
return false
}
// readout helpers
fn dlg_first_sentence(content: String) -> String {
let sents: [String] = prop_split_sentences(content)
let n: Int = native_list_len(sents)
let i: Int = 0
while i < n {
let s: String = str_trim(native_list_get(sents, i))
// drop a leading markdown heading marker for a clean read-out line
if str_starts_with(s, "# ") { let s = str_trim(str_slice(s, 2, str_len(s))) }
if str_len(s) > 0 { return s }
let i = i + 1
}
return str_trim(content)
}
// strip trailing/leading punctuation from a token.
fn dlg_clean_tok(w: String) -> String {
let s: String = str_trim(w)
let s = str_strip_suffix(s, ".")
let s = str_strip_suffix(s, ",")
let s = str_strip_suffix(s, "?")
let s = str_strip_suffix(s, "!")
let s = str_strip_suffix(s, ":")
let s = str_strip_suffix(s, ";")
return str_trim(s)
}
// closed-class across the supported languages (union) a word we must NOT treat
// as a retrieval topic. Also drops the meta verbs of a request ("tell", "prove",
// "show") so the TOPIC, not the speech act, is what projects into memory.
fn dlg_is_stop(w: String) -> Bool {
if ml_stop_en(w) { return true }
if ml_stop_es(w) { return true }
if ml_stop_pt(w) { return true }
if ml_stop_it(w) { return true }
if str_eq(w, "tell") { return true }
if str_eq(w, "show") { return true }
if str_eq(w, "about") { return true }
if str_eq(w, "sobre") { return true }
if str_eq(w, "acerca") { return true }
return false
}
// The CONTENT TERMS the query projects into memory: content words only, cleaned,
// cross-lingually mapped to the engram's English vocabulary, 3 chars. This is
// the geometry probe the speech-act verbs and function words are stripped so a
// PP topic ("tell me ABOUT Lisbon") projects on "lisbon", not "tell"/"me".
fn dlg_content_terms(content: String, lang: String) -> [String] {
let toks: [String] = cp_tokenize(content)
let n: Int = native_list_len(toks)
let out: [String] = native_list_empty()
let i: Int = 0
while i < n {
let w: String = str_to_lower(dlg_clean_tok(native_list_get(toks, i)))
if str_len(w) >= 3 {
if !dlg_is_stop(w) {
let out = native_list_append(out, ml_term(w, lang))
}
}
let i = i + 1
}
return out
}
// Does this landed node lexically overlap the query's content terms? This is the
// RELEVANCE FLOOR: activation always returns the store's most salient nodes, so
// without this a query about nothing would "land" on the self/top node. A node
// that shares no content term with the query is "nowhere close" -> honest absence.
fn dlg_node_matches(node: String, terms: [String]) -> Bool {
let hay: String = str_to_lower(json_get_string(node, "content") + " " + json_get_string(node, "label"))
let n: Int = native_list_len(terms)
let i: Int = 0
while i < n {
let t: String = native_list_get(terms, i)
if str_len(t) >= 3 {
if str_contains(hay, t) { return true }
}
let i = i + 1
}
return false
}
// MATERIALIZE the landed region: read out the landed fact, then WALK the
// neighborhood and read out its connected members (real edges, not top-props).
fn dlg_materialize(top_node: String, reply_lang: String) -> String {
let id: String = json_get_string(top_node, "id")
let content: String = json_get_string(top_node, "content")
let lead: String = dlg_first_sentence(content)
let nb: String = engram_neighbors_json(id, 2, "both")
let m: Int = json_array_len(nb)
let parts: [String] = native_list_empty()
let parts = native_list_append(parts, lead)
let added: Int = 0
let i: Int = 0
while i < m {
if added < 3 {
let rec: String = json_array_get(nb, i)
let node: String = json_get_raw(rec, "node")
let nc: String = json_get_string(node, "content")
if !str_eq(nc, "") {
let sent: String = dlg_first_sentence(nc)
if !str_eq(sent, "") {
let parts = native_list_append(parts, sent)
let added = added + 1
}
}
}
let i = i + 1
}
// The readout is the region's OWN prose, verbatim negation SACRED, no echo.
return str_join(parts, " ")
}
// THE single operation
fn dlg_respond(text: String) -> String {
// directive override: reply language may differ from content language.
let dir: [String] = dlg_parse_directive(text)
let target_lang: String = native_list_get(dir, 0)
let content: String = native_list_get(dir, 1)
let content_lang: String = ml_detect(content)
let reply_lang: String = content_lang
if !str_eq(target_lang, "") { let reply_lang = target_lang }
// comprehend the content (SACRED polarity carried in the spec).
let spec: [String] = parse_spec_lang(content, content_lang)
// PROJECT + LAND: SELF region
// Identity/presence shape lands in the self region; read out the REAL self
// nodes (self_region.el), never a template. Same single operation this is
// just the self attractor winning the landing.
if dlg_is_identity(content) {
if sr_available() {
// read out the REAL self nodes when replying in their own language
// (the soul's prose is English); for another reply language we cannot
// translate real content without an LLM, so we answer with the
// localized SACRED identity anchor honest, in-language, no fabrication.
if str_eq(reply_lang, "en") { return sr_readout("en") }
return ml_tr("identity", reply_lang)
}
// self region thin honest localized identity (logged fallback shape).
return ml_tr("identity", reply_lang)
}
// PROJECT into MEMORY geometry
let terms: [String] = dlg_content_terms(content, content_lang)
let qterm: String = str_join(terms, " ")
let act: String = engram_activate_json(qterm, 12)
let n: Int = json_array_len(act)
// LAND: the highest-activation node that ACTUALLY overlaps the query's
// content terms (the relevance floor). Activation always returns the most
// salient nodes, so we walk the ranked list and take the first that is
// genuinely "close"; if none is, the query landed nowhere. ───────────────
let landing: String = ""
let i: Int = 0
while i < n {
if str_eq(landing, "") {
let rec: String = json_array_get(act, i)
let node: String = json_get_raw(rec, "node")
if dlg_node_matches(node, terms) {
let landing = node
}
}
let i = i + 1
}
// HONEST ABSENCE: nothing close an empty region, not a fabricated answer,
// not an "I noted that" echo.
if str_eq(landing, "") {
return ml_tr("no_memory", reply_lang)
}
// MATERIALIZE the landing by WALKING its neighborhood.
return dlg_materialize(landing, reply_lang)
}
-13
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@@ -63,9 +63,6 @@ import "morphology-cop.el"
import "grammar.el"
import "realizer.el"
import "semantics.el"
// Comprehension front-end (input half: text meaning-spec)
import "comprehend.el"
//
// Entry points:
//
@@ -120,9 +117,6 @@ fn build_form_from_json(semantic_form_json: String, lang_code: String) -> [Strin
let location: String = sem_get(semantic_form_json, "location")
let tense: String = sem_get(semantic_form_json, "tense")
let aspect: String = sem_get(semantic_form_json, "aspect")
let polarity: String = sem_get(semantic_form_json, "polarity")
let neg_word: String = sem_get(semantic_form_json, "neg_word")
let iobj: String = sem_get(semantic_form_json, "iobj")
let form: [String] = native_list_empty()
let form = native_list_append(form, "intent")
@@ -133,19 +127,12 @@ fn build_form_from_json(semantic_form_json: String, lang_code: String) -> [Strin
let form = native_list_append(form, predicate)
let form = native_list_append(form, "patient")
let form = native_list_append(form, patient)
let form = native_list_append(form, "iobj")
let form = native_list_append(form, iobj)
let form = native_list_append(form, "location")
let form = native_list_append(form, location)
let form = native_list_append(form, "tense")
let form = native_list_append(form, tense)
let form = native_list_append(form, "aspect")
let form = native_list_append(form, aspect)
// SACRED: polarity crosses the JSON boundary and is never inferred away.
let form = native_list_append(form, "polarity")
let form = native_list_append(form, polarity)
let form = native_list_append(form, "neg_word")
let form = native_list_append(form, neg_word)
let form = native_list_append(form, "lang")
let form = native_list_append(form, lang_code)
+4 -4
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@@ -1,7 +1,7 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn sem_get(json: String, key: String) -> String
extern fn generate_frame(frame: [String]) -> String
extern fn generate_frame_lang(frame: [String], lang_code: String) -> String
extern fn build_form_from_json(semantic_form_json: String, lang_code: String) -> [String]
extern fn generate_frame(frame: Any) -> String
extern fn generate_frame_lang(frame: Any, lang_code: String) -> String
extern fn build_form_from_json(semantic_form_json: String, lang_code: String) -> Any
extern fn generate(semantic_form_json: String) -> String
extern fn generate_lang(semantic_form_json: String, lang_code: String) -> String
+28 -28
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@@ -1,22 +1,22 @@
// auto-generated by elc --emit-header do not edit
extern fn slots_get(slots: [String], key: String) -> String
extern fn slots_set(slots: [String], key: String, val: String) -> [String]
extern fn make_slots(k0: String, v0: String) -> [String]
extern fn make_slots2(k0: String, v0: String, k1: String, v1: String) -> [String]
extern fn make_slots3(k0: String, v0: String, k1: String, v1: String, k2: String, v2: String) -> [String]
extern fn make_slots4(k0: String, v0: String, k1: String, v1: String, k2: String, v2: String, k3: String, v3: String) -> [String]
extern fn make_slots5(k0: String, v0: String, k1: String, v1: String, k2: String, v2: String, k3: String, v3: String, k4: String, v4: String) -> [String]
extern fn rule_id(rule: [String]) -> String
extern fn rule_lhs(rule: [String]) -> String
extern fn rule_rhs_len(rule: [String]) -> Int
extern fn rule_rhs(rule: [String], idx: Int) -> String
extern fn make_rule(id: String, lhs: String, r0: String) -> [String]
extern fn make_rule2(id: String, lhs: String, r0: String, r1: String) -> [String]
extern fn make_rule3(id: String, lhs: String, r0: String, r1: String, r2: String) -> [String]
extern fn make_rule4(id: String, lhs: String, r0: String, r1: String, r2: String, r3: String) -> [String]
extern fn build_rules() -> [[String]]
extern fn get_rules() -> [[String]]
extern fn find_rule(rule_id_str: String) -> [String]
// auto-generated by elc --emit-header - do not edit
extern fn slots_get(slots: Any, key: String) -> String
extern fn slots_set(slots: Any, key: String, val: String) -> Any
extern fn make_slots(k0: String, v0: String) -> Any
extern fn make_slots2(k0: String, v0: String, k1: String, v1: String) -> Any
extern fn make_slots3(k0: String, v0: String, k1: String, v1: String, k2: String, v2: String) -> Any
extern fn make_slots4(k0: String, v0: String, k1: String, v1: String, k2: String, v2: String, k3: String, v3: String) -> Any
extern fn make_slots5(k0: String, v0: String, k1: String, v1: String, k2: String, v2: String, k3: String, v3: String, k4: String, v4: String) -> Any
extern fn rule_id(rule: Any) -> String
extern fn rule_lhs(rule: Any) -> String
extern fn rule_rhs_len(rule: Any) -> Int
extern fn rule_rhs(rule: Any, idx: Int) -> String
extern fn make_rule(id: String, lhs: String, r0: String) -> Any
extern fn make_rule2(id: String, lhs: String, r0: String, r1: String) -> Any
extern fn make_rule3(id: String, lhs: String, r0: String, r1: String, r2: String) -> Any
extern fn make_rule4(id: String, lhs: String, r0: String, r1: String, r2: String, r3: String) -> Any
extern fn build_rules() -> Any
extern fn get_rules() -> Any
extern fn find_rule(rule_id_str: String) -> Any
extern fn make_leaf(label: String, word: String) -> String
extern fn make_node1(label: String, child0: String) -> String
extern fn make_node2(label: String, child0: String, child1: String) -> String
@@ -24,15 +24,15 @@ extern fn make_node3(label: String, child0: String, child1: String, child2: Stri
extern fn make_node4(label: String, child0: String, child1: String, child2: String, child3: String) -> String
extern fn nlg_is_ws(c: String) -> Bool
extern fn skip_ws(s: String, pos: Int) -> Int
extern fn scan_token(s: String, start: Int) -> [String]
extern fn scan_token(s: String, start: Int) -> Any
extern fn render_tree(tree: String) -> String
extern fn gram_word_order(profile: [String]) -> String
extern fn gram_order_constituents(subj: String, verb: String, obj: String, profile: [String]) -> String
extern fn gram_build_vp(verb: String, aux: String, profile: [String]) -> String
extern fn gram_question_strategy(profile: [String]) -> String
extern fn gram_word_order(profile: Any) -> String
extern fn gram_order_constituents(subj: String, verb: String, obj: String, profile: Any) -> String
extern fn gram_build_vp(verb: String, aux: String, profile: Any) -> String
extern fn gram_question_strategy(profile: Any) -> String
extern fn is_pronoun(word: String) -> Bool
extern fn build_np(referent: String, slots: [String]) -> String
extern fn build_np(referent: String, slots: Any) -> String
extern fn build_pp(loc: String) -> String
extern fn build_vp_body(slots: [String]) -> String
extern fn build_vp_from_slots(slots: [String]) -> String
extern fn generate_tree(rule_id_str: String, slots: [String]) -> String
extern fn build_vp_body(slots: Any) -> String
extern fn build_vp_from_slots(slots: Any) -> String
extern fn generate_tree(rule_id_str: String, slots: Any) -> String
-65
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@@ -1,65 +0,0 @@
// image-demo.el - Drive the native PNG surface: plan a scene from a small
// meaning phrase (incl. a NEG frame) and emit a byte-valid 64x64 PNG whose
// palette is read from elp/faculty/sig/scene.basis.
fn img_frame(relation: String, polarity: String, confidence: String, importance: String, salience: String, subj_id: String) -> [String] {
let f: [String] = native_list_empty()
let f: [String] = native_list_append(f, "relation")
let f: [String] = native_list_append(f, relation)
let f: [String] = native_list_append(f, "polarity")
let f: [String] = native_list_append(f, polarity)
let f: [String] = native_list_append(f, "confidence")
let f: [String] = native_list_append(f, confidence)
let f: [String] = native_list_append(f, "importance")
let f: [String] = native_list_append(f, importance)
let f: [String] = native_list_append(f, "salience")
let f: [String] = native_list_append(f, salience)
let f: [String] = native_list_append(f, "subj_id")
let f: [String] = native_list_append(f, subj_id)
return f
}
fn rgb_str(c: [Int]) -> String {
return int_to_str(native_list_get(c, 0)) + "," + int_to_str(native_list_get(c, 1)) + "," + int_to_str(native_list_get(c, 2))
}
fn run_image() -> Int {
fs_mkdir("/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out")
let table: [Int] = crc_table()
println("crc_table[1]=" + int_to_str(native_list_get(table, 1)) + " (expect 1996959894 / 0x77073096)")
let basis: [String] = basis_load("/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/sig/scene.basis")
let warm: [Int] = parse_rgb(basis_field(basis, "warm"))
let cool: [Int] = parse_rgb(basis_field(basis, "cool"))
let bg: [Int] = parse_rgb(basis_field(basis, "bg"))
println("basis warm=" + rgb_str(warm) + " cool=" + rgb_str(cool) + " bg=" + rgb_str(bg) + " (read from scene.basis)")
let frames: [[String]] = native_list_empty()
let frames: [[String]] = native_list_append(frames, img_frame("agent", "aff", "0.9", "0.8", "0", "s1"))
let frames: [[String]] = native_list_append(frames, img_frame("theme", "aff", "0.7", "0.6", "1", "s2"))
let frames: [[String]] = native_list_append(frames, img_frame("cause", "aff", "0.8", "0.9", "0", "s3"))
let frames: [[String]] = native_list_append(frames, img_frame("negation", "neg", "0.85", "0.7", "1", "s4"))
let frames: [[String]] = native_list_append(frames, img_frame("goal", "aff", "0.6", "0.5", "0", "s5"))
let frames: [[String]] = native_list_append(frames, img_frame("result", "aff", "0.95", "1.0", "1", "s6"))
let shapes: [[Int]] = plan_scene(frames, warm, cool)
let ns: Int = native_list_len(shapes)
println("planned " + int_to_str(ns) + " shapes:")
let si: Int = 0
while si < ns {
let sh: [Int] = native_list_get(shapes, si)
let pol: String = surface_get(native_list_get(frames, si), "polarity")
println(" shape " + int_to_str(si) + " type=" + int_to_str(native_list_get(sh, 0)) + " x=" + int_to_str(native_list_get(sh, 1)) + " y=" + int_to_str(native_list_get(sh, 2)) + " size=" + int_to_str(native_list_get(sh, 3)) + " rgb=" + int_to_str(native_list_get(sh, 4)) + "," + int_to_str(native_list_get(sh, 5)) + "," + int_to_str(native_list_get(sh, 6)) + " polarity=" + pol)
let si: Int = si + 1
}
let raw: [Int] = rasterize(64, 64, shapes, bg)
println("rasterized raw (filtered scanlines) bytes=" + int_to_str(native_list_len(raw)) + " (expect 12352)")
let png: [Int] = png_build(64, 64, raw, table)
let plen: Int = native_list_len(png)
let ok: Int = png_write("/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out/scene.png", png)
println("PNG bytes=" + int_to_str(plen) + " -> /Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out/scene.png (write_ok=" + int_to_str(ok) + ")")
return plen
}
println("image-demo returned png_bytes=" + int_to_str(run_image()))
-412
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@@ -1,412 +0,0 @@
// image-surface.el - Native own-core raster PNG surface (the image efferent
// twin of audio). Renders a 64x64 RGB scene deterministically from a frame's
// meaning-geometry, then serialises a byte-valid PNG entirely own-core:
// 8-byte magic, IHDR, IDAT (zlib STORED/uncompressed DEFLATE + Adler32), IEND,
// with a per-chunk CRC32 computed via software xor32 (EL has no bitwise ops).
//
// The RGB palette basis is read from elp/faculty/sig/scene.basis (data, not
// literals) - the same read-from-learned discipline as the audio signatures.
// Integer-only throughout; pixels are composed functionally (painter's order)
// so no list mutation is needed.
// -- small int/parse helpers (self-contained) ----------------------------------
fn i_str_to_int(s: String) -> Int {
let n: Int = str_len(s)
let i: Int = 0
let v: Int = 0
while i < n {
let c: Int = str_char_code(s, i)
if c >= 48 {
if c < 58 {
let v: Int = v * 10 + (c - 48)
}
}
let i: Int = i + 1
}
return v
}
fn basis_load(path: String) -> [String] {
return str_split(fs_read(path), "\n")
}
fn basis_field(lines: [String], key: String) -> String {
let pref: String = key + ": "
let n: Int = native_list_len(lines)
let plen: Int = str_len(pref)
let i: Int = 0
while i < n {
let ln: String = native_list_get(lines, i)
if str_starts_with(ln, pref) {
return str_slice(ln, plen, str_len(ln))
}
let i: Int = i + 1
}
return ""
}
fn parse_rgb(csv: String) -> [Int] {
let parts: [String] = str_split(csv, ",")
let out: [Int] = native_list_empty()
let n: Int = native_list_len(parts)
let i: Int = 0
while i < n {
let v: Int = i_str_to_int(native_list_get(parts, i))
let out: [Int] = native_list_append(out, v)
let i: Int = i + 1
}
return out
}
// -- software 32-bit XOR (no bitwise ops in EL) --------------------------------
fn xor32(a: Int, b: Int) -> Int {
let r: Int = 0
let bit: Int = 1
let i: Int = 0
while i < 32 {
let abit: Int = (a / bit) % 2
let bbit: Int = (b / bit) % 2
if abit != bbit {
let add: Int = bit
let r: Int = r + add
}
let bit: Int = bit * 2
let i: Int = i + 1
}
return r
}
// -- CRC32 (table-driven, table built with xor32) ------------------------------
fn crc_table() -> [Int] {
let t: [Int] = native_list_empty()
let n: Int = 0
while n < 256 {
let c: Int = n
let k: Int = 0
while k < 8 {
if c % 2 == 1 {
let h: Int = c / 2
let c: Int = xor32(h, 3988292384)
} else {
let c: Int = c / 2
}
let k: Int = k + 1
}
let t: [Int] = native_list_append(t, c)
let n: Int = n + 1
}
return t
}
fn crc32_of(bytes: [Int], table: [Int]) -> Int {
let crc: Int = 4294967295
let n: Int = native_list_len(bytes)
let i: Int = 0
while i < n {
let b: Int = native_list_get(bytes, i)
let lo: Int = crc % 256
let idx: Int = xor32(lo, b) % 256
let tv: Int = native_list_get(table, idx)
let hi: Int = crc / 256
let crc: Int = xor32(hi, tv)
let i: Int = i + 1
}
return xor32(crc, 4294967295)
}
// -- Adler32 (for the zlib trailer) --------------------------------------------
fn adler32_of(bytes: [Int]) -> Int {
let a: Int = 1
let b: Int = 0
let n: Int = native_list_len(bytes)
let i: Int = 0
while i < n {
let byte: Int = native_list_get(bytes, i)
let a: Int = (a + byte) % 65521
let b: Int = (b + a) % 65521
let i: Int = i + 1
}
return b * 65536 + a
}
// -- byte-list append helpers --------------------------------------------------
fn app_u32be(dst: [Int], v: Int) -> [Int] {
let dst: [Int] = native_list_append(dst, (v / 16777216) % 256)
let dst: [Int] = native_list_append(dst, (v / 65536) % 256)
let dst: [Int] = native_list_append(dst, (v / 256) % 256)
let dst: [Int] = native_list_append(dst, v % 256)
return dst
}
fn app_tag(dst: [Int], s: String) -> [Int] {
let n: Int = str_len(s)
let i: Int = 0
while i < n {
let dst: [Int] = native_list_append(dst, str_char_code(s, i))
let i: Int = i + 1
}
return dst
}
fn app_all(dst: [Int], src: [Int]) -> [Int] {
let n: Int = native_list_len(src)
let i: Int = 0
while i < n {
let dst: [Int] = native_list_append(dst, native_list_get(src, i))
let i: Int = i + 1
}
return dst
}
// -- plan: frame meaning-geometry -> shape atoms -------------------------------
// shape = [type, x, y, size, r, g, b] (type 0=rect 1=disc 2=triangle)
fn charsum(s: String) -> Int {
let n: Int = str_len(s)
let i: Int = 0
let acc: Int = 0
while i < n {
let c: Int = str_char_code(s, i)
let acc: Int = acc + c
let i: Int = i + 1
}
return acc
}
fn micro_of(s: String) -> Int {
let dot: Int = str_index_of(s, ".")
if dot < 0 { return i_str_to_int(s) * 1000000 }
let n: Int = str_len(s)
let fp: String = str_slice(s, dot + 1, n)
let ip: String = str_slice(s, 0, dot)
let iv: Int = i_str_to_int(ip)
let fv: Int = 0
let scale: Int = 100000
let fl: Int = str_len(fp)
let i: Int = 0
while i < 6 {
let d: Int = 0
if i < fl { let d: Int = str_char_code(fp, i) - 48 }
let fv: Int = fv + d * scale
let scale: Int = scale / 10
let i: Int = i + 1
}
return iv * 1000000 + fv
}
fn plan_scene(frames: [[String]], warm: [Int], cool: [Int]) -> [[Int]] {
let shapes: [[Int]] = native_list_empty()
let nf: Int = native_list_len(frames)
let fi: Int = 0
while fi < nf {
let fr: [String] = native_list_get(frames, fi)
let relation: String = surface_get(fr, "relation")
let polarity: String = surface_get(fr, "polarity")
let confidence: String = surface_get(fr, "confidence")
let importance: String = surface_get(fr, "importance")
let salience: String = surface_get(fr, "salience")
// relation -> shape type
let stype: Int = charsum(relation) % 3
// confidence -> size (8..22)
let cmi: Int = micro_of(confidence)
let size: Int = 8 + cmi / 71428
// salience -> y
let sal: Int = i_str_to_int(salience)
let y: Int = 6 + sal * 26
// subj_id/index -> x
let x: Int = 4 + (fi * 10) % 48
// polarity -> warm/cool base color
let br: Int = native_list_get(warm, 0)
let bg2: Int = native_list_get(warm, 1)
let bb: Int = native_list_get(warm, 2)
if str_eq(polarity, "neg") {
let br: Int = native_list_get(cool, 0)
let bg2: Int = native_list_get(cool, 1)
let bb: Int = native_list_get(cool, 2)
}
// importance -> brightness (500..1000 permille)
let imi: Int = micro_of(importance)
let bpm: Int = 500 + imi / 2000
let r: Int = br * bpm / 1000
let g: Int = bg2 * bpm / 1000
let b: Int = bb * bpm / 1000
let sh: [Int] = native_list_empty()
let sh: [Int] = native_list_append(sh, stype)
let sh: [Int] = native_list_append(sh, x)
let sh: [Int] = native_list_append(sh, y)
let sh: [Int] = native_list_append(sh, size)
let sh: [Int] = native_list_append(sh, r)
let sh: [Int] = native_list_append(sh, g)
let sh: [Int] = native_list_append(sh, b)
let shapes: [[Int]] = native_list_append(shapes, sh)
let fi: Int = fi + 1
}
return shapes
}
// covers: is (px,py) inside this shape?
fn covers(sh: [Int], px: Int, py: Int) -> Bool {
let stype: Int = native_list_get(sh, 0)
let sx: Int = native_list_get(sh, 1)
let sy: Int = native_list_get(sh, 2)
let size: Int = native_list_get(sh, 3)
let cx: Int = sx + size / 2
if stype == 0 {
if px >= sx {
if px < sx + size {
if py >= sy {
if py < sy + size {
return true
}
}
}
}
return false
}
if stype == 1 {
let rad: Int = size / 2
let dx: Int = px - cx
let dy: Int = py - (sy + rad)
if dx * dx + dy * dy <= rad * rad {
return true
}
return false
}
// triangle: apex at top (sy), base at sy+size
if py >= sy {
if py < sy + size {
let dyv: Int = py - sy
let halfw: Int = dyv / 2
let dxv: Int = px - cx
let adx: Int = dxv
if adx < 0 { let adx: Int = 0 - dxv }
if adx <= halfw {
return true
}
}
}
return false
}
// pixel_color: painter's algorithm - last covering shape wins. Returns [r,g,b].
fn pixel_color(px: Int, py: Int, shapes: [[Int]], bg: [Int]) -> [Int] {
let r: Int = native_list_get(bg, 0)
let g: Int = native_list_get(bg, 1)
let b: Int = native_list_get(bg, 2)
let n: Int = native_list_len(shapes)
let i: Int = 0
while i < n {
let sh: [Int] = native_list_get(shapes, i)
if covers(sh, px, py) {
let r: Int = native_list_get(sh, 4)
let g: Int = native_list_get(sh, 5)
let b: Int = native_list_get(sh, 6)
}
let i: Int = i + 1
}
let out: [Int] = native_list_empty()
let out: [Int] = native_list_append(out, r)
let out: [Int] = native_list_append(out, g)
let out: [Int] = native_list_append(out, b)
return out
}
// rasterize: build the raw (filtered) scanline byte stream, filter byte 0 / row.
fn rasterize(w: Int, h: Int, shapes: [[Int]], bg: [Int]) -> [Int] {
let raw: [Int] = native_list_empty()
let y: Int = 0
while y < h {
let raw: [Int] = native_list_append(raw, 0)
let x: Int = 0
while x < w {
let col: [Int] = pixel_color(x, y, shapes, bg)
let raw: [Int] = native_list_append(raw, native_list_get(col, 0))
let raw: [Int] = native_list_append(raw, native_list_get(col, 1))
let raw: [Int] = native_list_append(raw, native_list_get(col, 2))
let x: Int = x + 1
}
let y: Int = y + 1
}
return raw
}
// zlib stream with a single STORED (uncompressed) DEFLATE block + Adler32.
fn zlib_store(raw: [Int]) -> [Int] {
let z: [Int] = native_list_empty()
let z: [Int] = native_list_append(z, 120)
let z: [Int] = native_list_append(z, 1)
let z: [Int] = native_list_append(z, 1)
let len: Int = native_list_len(raw)
let nlen: Int = 65535 - len
let z: [Int] = native_list_append(z, len % 256)
let z: [Int] = native_list_append(z, (len / 256) % 256)
let z: [Int] = native_list_append(z, nlen % 256)
let z: [Int] = native_list_append(z, (nlen / 256) % 256)
let z: [Int] = app_all(z, raw)
let ad: Int = adler32_of(raw)
let z: [Int] = app_u32be(z, ad)
return z
}
// append a full PNG chunk: length + (type+data) + crc32(type+data).
fn app_chunk(png: [Int], type_and_data: [Int], table: [Int]) -> [Int] {
let total: Int = native_list_len(type_and_data)
let dlen: Int = total - 4
let png: [Int] = app_u32be(png, dlen)
let png: [Int] = app_all(png, type_and_data)
let crc: Int = crc32_of(type_and_data, table)
let png: [Int] = app_u32be(png, crc)
return png
}
fn png_build(w: Int, h: Int, raw: [Int], table: [Int]) -> [Int] {
let png: [Int] = native_list_empty()
// 8-byte signature
let png: [Int] = native_list_append(png, 137)
let png: [Int] = native_list_append(png, 80)
let png: [Int] = native_list_append(png, 78)
let png: [Int] = native_list_append(png, 71)
let png: [Int] = native_list_append(png, 13)
let png: [Int] = native_list_append(png, 10)
let png: [Int] = native_list_append(png, 26)
let png: [Int] = native_list_append(png, 10)
// IHDR
let ihdr: [Int] = native_list_empty()
let ihdr: [Int] = app_tag(ihdr, "IHDR")
let ihdr: [Int] = app_u32be(ihdr, w)
let ihdr: [Int] = app_u32be(ihdr, h)
let ihdr: [Int] = native_list_append(ihdr, 8)
let ihdr: [Int] = native_list_append(ihdr, 2)
let ihdr: [Int] = native_list_append(ihdr, 0)
let ihdr: [Int] = native_list_append(ihdr, 0)
let ihdr: [Int] = native_list_append(ihdr, 0)
let png: [Int] = app_chunk(png, ihdr, table)
// IDAT
let z: [Int] = zlib_store(raw)
let idat: [Int] = native_list_empty()
let idat: [Int] = app_tag(idat, "IDAT")
let idat: [Int] = app_all(idat, z)
let png: [Int] = app_chunk(png, idat, table)
// IEND
let iend: [Int] = native_list_empty()
let iend: [Int] = app_tag(iend, "IEND")
let png: [Int] = app_chunk(png, iend, table)
return png
}
fn png_write(path: String, png: [Int]) -> Int {
let n: Int = native_list_len(png)
let buf: String = __str_alloc(n)
let i: Int = 0
while i < n {
let buf: String = __str_set_char(buf, i, native_list_get(png, i))
let i: Int = i + 1
}
let ok: Int = fs_write_bytes(path, buf, n)
return ok
}
-72
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;;; lang_profile_ca.el — Catalan language profile for ELP.
;;; Mirrors lang_profile_it / _es / _pt; keys the realizer's construction switches.
;;; Catalan is the CLOSEST Romance sibling to the shared engine (~85% conceptual
;;; reuse). The deltas: PRONOMS FEBLES with four position allomorphs, l'-elision,
;;; del/al/pel contractions, the periphrastic preterite (vaig+INF), and NO
;;; essere/avere split (perfect aux is always HAVER; ser/estar is only the copula).
(lang_profile_ca
(language "Catalan")
(iso639 "ca")
(family "Romance")
;; ── core typology flags ────────────────────────────────────────────────
(pro-drop yes) ; null subjects default; overt pronoun = emphatic
(obligatory-subject no)
(grammatical-gender yes) ; m/f; full NP agreement (art + adj + participle)
(do-support no)
(subject-aux-inversion no) ; yes/no Q = declarative order + '?'; no inversion
(article-selection "el/la/l'/els/les ; un/una/uns/unes") ; l'-ELISION:
; el/la -> l' before vowel or (silent) h, glued to
; the next word (l'home, l'illa); de -> d' before vowel
(article-drives-contraction yes) ; article choice feeds prep+article contraction
(adjective-position "postnominal-default + small prenominal class") ; bo/bon,
; mal, gran, nou, vell, primer, molt... prenominal
(question-punct plain) ; ? and ! only (no inverted ¿ ¡)
;; ── MANDATORY prep+article contractions ────────────────────────────────
(contractions ((de el del) (de els dels)
(a el al) (a els als)
(per el pel) (per els pels)))
(contraction-mandatory yes) ; *de el -> del obligatory
(contraction-blocked-before-elision yes) ; de l'home / a l'home (NO *del home)
;; ── clitic system: PRONOMS FEBLES (the headline delta) ──────────────────
(clitics yes)
(clitic-allomorphy four-position) ; per pronoun, form varies by position+onset:
; reinforced (em, et, el) proclitic before a consonant
; elided (m', t', l', n') proclitic before a vowel/h
; full (-me, -lo, -li) enclitic after a consonant/-r
; reduced ('m, 't, 'l, 'ns) enclitic after a vowel
(clitic-placement ((finite proclitic) ; el veig, no m'ho dóna
(imperative-affirmative enclitic) ; dóna'm, digues-me
(imperative-negative present-subjunctive) ; no parlis (delta)
(infinitive enclitic) ; ajudar-me, veure'l
(gerund enclitic))) ; fent-ho
(clitic-combination ((me el "me'l") (te el "te'l") (se el "se'l")
(me la "me la") (me en "me'n")
(li el "l'hi") (li en "n'hi"))) ; dative+accusative clusters
(clitic-particles (hi en ho)) ; locative hi, partitive/genitive en, neuter ho
;; ── verb / aspect system ───────────────────────────────────────────────
(finite-agreement "person+number (6-way)")
(tenses (present imperfet preterit-simple perifrastic-preterit futur
condicional subjuntiu-present subjuntiu-imperfet imperatiu))
(periphrastic-preterite "vaig/vas/va/vam/vau/van + INFINITIVE") ; << hallmark CA
; (vaig cantar = 'I sang'); coexists w/ synthetic pret.
(compound-past "pretèrit perfet = haver(present) + participle")
(perfect-aux "HAVER only") ; << NO essere/avere split (simpler than IT)
(participle-agreement ((haver preceding-acc-clitic))) ; les he vistes; else invariable
(progressive-aux "estar + gerundi")
(copula "ser / estar") ; ser: identity/essential/origin; estar:
; location + transient state (estic cansat, és a casa)
(passive-aux "ser (+ per-agent)")
(future inflectional) ; cantaré, serà
(comparative "més/menys ADJ que")
;; ── SACRED safety bar (shared with es/pt/it/en) ────────────────────────
(negation-faithful yes) ; polarity never dropped/inverted; unplaceable -> FLAG
(negation "no (preverbal) + optional 'pas' + concord") ; no...res/
; ningú/mai/cap/gens/enlloc
(negative-concord yes) ; preverbal negative subject (ningú) keeps 'no'
(neg-reinforcer pas)) ; optional (no ho faré pas)
-41
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;;; lang_profile_de.el — German language profile for ELP.
;;; Mirrors lang_profile_en / lang_profile_es. Keys the realizer's construction
;;; switches. German is the largest Germanic delta from the EN engine: V2 word
;;; order, four morphological cases, and separable-prefix verbs.
(lang_profile_de
(language "German")
(iso639 "de")
(family "Germanic")
(neighbor-base "en") ; realized by extending the English (Germanic) engine
;; ── core typology flags ────────────────────────────────────────────────
(pro-drop no) ; obligatory subject in finite clauses
(obligatory-subject yes)
(grammatical-gender (m f n)) ; three genders; drives article + adj declension
(case-system (nom acc dat gen)) ; four cases on articles/adjs/nouns
(word-order V2) ; finite verb 2nd in main clause
(subordinate-order verb-final) ; "..., dass er den Hund SIEHT."
(separable-verbs yes) ; aufstehen -> "steht ... auf"; ppart "aufgestanden"
(do-support no) ; German negates/questions the finite verb directly
(subject-verb-inversion yes) ; yes/no Q fronts finite verb; wh-Q fills Vorfeld
(article-selection "der/die/das + ein/kein") ; declined by case x gender x number
(adjective-position prenominal)
(adjective-declension (strong weak mixed)) ; chosen by the determiner type
(noun-capitalization yes)
;; ── verb / aspect system ───────────────────────────────────────────────
(finite-agreement "person-and-number") ; full present/past paradigm
(auxiliary-order (modal tense-aux perfect passive main))
(perfect-aux (haben sein)) ; sein for intransitive motion/change verbs
(passive-aux "werden")
(future "werden + infinitive")
(comparative "synthetic (-er / -st, with umlaut)")
;; ── negation ───────────────────────────────────────────────────────────
(negation-markers (nicht kein)) ; kein- negates an indefinite NP; nicht else
(negation-faithful yes) ; SACRED: polarity never dropped/inverted -> FLAG
;; ── lexicon provenance ─────────────────────────────────────────────────
(lexicon-source "UniMorph deu (primary) + kaikki.org German (gender override)")
(lexicon-license "CC-BY-SA 3.0 / GFDL"))
-41
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;;; lang_profile_en.el — English language profile for ELP.
;;; Mirrors lang_profile_es / lang_profile_pt; keys the realizer's construction
;;; switches. English is typologically distinct from the Romance builds, so the
;;; flags differ where the grammar differs.
(lang_profile_en
(language "English")
(iso639 "en")
(family "Germanic")
;; ── core typology flags ────────────────────────────────────────────────
(pro-drop no) ; OBLIGATORY subjects — missing subject is FLAGGED
(obligatory-subject yes)
(grammatical-gender no) ; natural gender only (he/she/it), no NP agreement
(do-support yes) ; negation & questions of lexical verbs insert do/does/did
(subject-aux-inversion yes) ; yes/no + non-subject wh questions invert the operator
(article-selection "a/an/the") ; a/an resolved PHONOLOGICALLY (an hour, a university)
(adjective-position prenominal) ; attributive adjectives precede the noun; invariant
(has-tag-questions yes) ; "...doesn't he?" — operator + reversed polarity
(has-there-existential yes) ; "there is/are/have been ..."
(possessive-clitic "'s") ; saxon genitive; plural in -s -> bare apostrophe
(question-punct plain) ; ? and ! only (no inverted marks)
;; ── verb / aspect system ───────────────────────────────────────────────
(finite-agreement "3sg-present-only") ; only 3sg present -s (+ suppletive be)
(auxiliary-order (modal perfect progressive passive main))
(perfect-aux "have") ; have + past participle
(progressive-aux "be") ; be + present participle
(passive-aux "be") ; be + past participle (+ by-agent)
(future "will + base") ; no inflectional future
(comparative "synthetic-or-periphrastic") ; -er/-est vs more/most by syllables
;; ── SACRED safety bar (shared with es/pt) ──────────────────────────────
(negation-faithful yes) ; polarity never dropped/inverted; unplaceable -> FLAG
;; ── DIALECT overlay (post-realization, one core -> US/UK/AU) ────────────
(dialect US) ; default; profile field switches the overlay
(dialects (US UK AU))
(dialect-canonical US) ; core is authored in US orthography
(dialect-overlay "dialect_en.to_dialect") ; orthography + lexis + grammar prefs
(dialect-covers (spelling lexis collective-agreement gotten/got)))
-45
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;;; lang_profile_es.el — Spanish language profile for ELP.
;;; Keys the realizer's construction switches. Mirrors lang_profile_en / _pt.
(lang_profile_es
(language "Spanish")
(iso639 "es")
(family "Romance")
;; -- core typology flags -------------------------------------------------
(pro-drop yes) ; subjects routinely dropped; agreement carries person
(obligatory-subject no)
(grammatical-gender yes) ; m/f on every noun; article+adjective AGREE
(gender-source lexicon); REAL per-noun gender from UniMorph — NOT a heuristic
(do-support no)
(subject-aux-inversion no) ; questions by intonation/punctuation, not inversion
(question-strategy intonation)
(article-selection "el/la/los/las un/una/unos/unas")
(stressed-a-rule yes) ; fem sg noun in stressed a-/ha- takes el/un (el agua)
(adjective-position postnominal) ; default post; a few prenominal + apocope
(adjective-agreement "gender+number")
(question-punct inverted) ; opening ¿ ¡ required
;; -- MANDATORY CONTRACTIONS (coordinator quality bar) --------------------
(contractions ((de el "del") (a el "al")))
(contraction-mandatory yes) ; 'de el'/'a el' MUST surface as del/al
;; -- verb / aspect system ------------------------------------------------
(verb-classes (ar er ir))
(tenses (present preterite imperfect future conditional))
(moods (ind sbjv imp))
(finite-agreement "person+number (6 slots)")
(perfect-aux "haber") ; haber + past participle (invariant -o)
(progressive-aux "estar") ; estar + gerund
(passive-aux "ser") ; ser + participle (agrees) + por-agent
(copula-split "ser/estar") ; permanent vs stage-level
(future "infinitive + é/ás/á/emos/éis/án")
;; -- clitics / government ------------------------------------------------
(object-clitics yes) ; me te lo la le nos os los las; proclisis/enclisis
(clitic-order "se II I III (le+lo -> se lo)")
(enclisis "imperative/infinitive/gerund + accent repair (dá+me+lo->dámelo)")
(verb-prep-government yes) ; verbs select prep (protestar+contra, escapar+de)
;; -- SACRED safety bar (shared with en/pt) -------------------------------
(negation-faithful yes)) ; polarity never dropped/inverted; unplaceable -> FLAG
-74
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;;; lang_profile_fr.el — French language profile for ELP.
;;; Mirrors lang_profile_it / lang_profile_es; keys the realizer's construction
;;; switches. French is a Romance sibling (~54% of the realizer code and the whole
;;; clause-engine architecture reused), but carries the family's biggest surface
;;; deltas: NOT pro-drop, DISCONTINUOUS negation, and an orthography/phonology
;;; mismatch (elision, liaison) that makes exact-match genuinely hard.
(lang_profile_fr
(language "French")
(iso639 "fr")
(family "Romance")
;; ── core typology flags ────────────────────────────────────────────────
(pro-drop no) ; << French-specific: subject clitic OBLIGATORY
(obligatory-subject yes) ; je/tu/il/elle/nous/vous/ils/elles always overt
(grammatical-gender yes) ; m/f; full NP agreement (art + adj + participle)
(do-support no)
(subject-aux-inversion optional) ; est-ce que (default) OR clitic inversion (vas-tu)
(article-selection "le/la/l'/les ; un/une/des ; PARTITIVE du/de la/de l'/des")
(article-drives-contraction yes) ; à+le=au, de+le=du feed off article choice
(adjective-position "postnominal-default + prenominal-BAGS") ; beau/bon/grand/
; petit/jeune/vieux/nouveau + ordinals prenominal
; (beau->bel, nouveau->nouvel, vieux->vieil / vowel)
(question-punct "space-before") ; French typography: ' ?' ' !' (no ¿¡)
;; ── elision (orthography/phonology mismatch — French-specific) ──────────
(elision ((le l') (la l') (je j') (ne n') (de d') (que qu')
(me m') (te t') (se s') (ce c'))) ; before vowel / h-muet
(elision-h-muet yes) ; l'homme, l'hôpital (h-aspiré exception list kept)
(liaison noted-not-modeled) ; phonological, not written in surface
;; ── MANDATORY prep+article contractions ────────────────────────────────
(contractions ((à le au) (à les aux) (de le du) (de les des)))
(contraction-mandatory yes) ; *à le -> au obligatory; à la / à l' uncontracted
(partitive ((m-sg du) (f-sg "de la") (vowel "de l'") (pl des)))
(partitive-under-neg "de") ; << gap in current build: 'ne … pas de pain'
;; ── clitic system ──────────────────────────────────────────────────────
(clitics yes)
(clitic-order (me te se nous vous | le la les | lui leur | y | en))
(clitic-placement ((finite proclitic) ; je le lui donne
(imperative-affirmative enclitic-hyphen) ; donne-le-moi
(imperative-negative "ne+proclitic+verb+pas") ; ne le donne pas
(infinitive enclitic))) ; PARTIAL: clitic-climbing
; onto infinitive under modal
(clitic-imperative-shift ((me moi) (te toi))) ; final me/te -> moi/toi (donne-moi)
(clitic-particles (y en)) ; locative y, partitive/genitive en
;; ── verb / aspect system ───────────────────────────────────────────────
(finite-agreement "person+number (written; many homophones)")
(tenses (présent imparfait passé-simple futur conditionnel
subjonctif-présent subjonctif-imparfait impératif))
(compound-past "passé-composé = aux(present) + participe passé")
(perfect-aux "être/avoir (LEXICAL selection)") ; << French-specific
(etre-aux-class "intransitive motion/change (aller venir arriver partir
entrer sortir monter descendre naître mourir rester
tomber retourner passer devenir revenir rentrer) + ALL
pronominal verbs")
(participle-agreement ((être subject) ; elle est allée / elles venues
(avoir preceding-direct-object))) ; je les ai vus
(progressive "être en train de + infinitif") ; no dedicated aux
(copula "être (single; no ser/estar, no essere/stare)")
(passive-aux "être (+ par-agent)")
(future inflectional) ; parlera, sera
(comparative "plus/moins ADJ que")
(superlative "le/la plus ADJ (de …)") ; PARTIAL word-order in build
;; ── SACRED safety bar (shared with es/pt/it/en) ────────────────────────
(negation-faithful yes) ; polarity never dropped/inverted; unplaceable -> FLAG
(negation "DISCONTINUOUS: ne (preverbal) … pas/jamais/rien/personne/
plus/guère/que (postverbal)") ; << biggest structural delta
(negation-ne-elides yes) ; ne -> n' before vowel (n'ai pas vu)
(negation-passe-composé "ne + aux + pas + participe") ; n'ai pas vu
(negative-concord partial)) ; personne/rien as arguments post-participle
-70
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;;; lang_profile_it.el — Italian language profile for ELP.
;;; Mirrors lang_profile_es / lang_profile_pt; keys the realizer's construction
;;; switches. Italian is a Romance sibling, so ~85% of the flags match ES/PT; the
;;; essere/avere auxiliary split and phonological article selection are the deltas.
(lang_profile_it
(language "Italian")
(iso639 "it")
(family "Romance")
;; ── core typology flags ────────────────────────────────────────────────
(pro-drop yes) ; null subjects default; overt pronoun = emphatic
(obligatory-subject no)
(grammatical-gender yes) ; m/f; full NP agreement (art + adj + participle)
(do-support no)
(subject-aux-inversion no) ; yes/no Q = declarative order + '?'; no inversion
(article-selection "il/lo/l'/i/gli + la/l'/le ; un/uno/un'/una") ; PHONOLOGICAL:
; lo/gli/uno before s+cons, z, gn, ps, pn, x, y, i+V;
; l'/un' before a vowel (elision, glued to next word)
(article-drives-contraction yes) ; article choice feeds the prep+art contraction
(adjective-position "postnominal-default + prenominal-class") ; bello/buono/grande
; /nuovo/vecchio/primo... prenominal (with apocope)
(question-punct plain) ; ? and ! only (no inverted ¿ ¡)
;; ── MANDATORY prep+article contractions ────────────────────────────────
(contractions ((di il del) (di lo dello) (di la della) (di i dei)
(di gli degli) (di le delle) (di l' dell')
(a il al) (a lo allo) (a la alla) (a i ai) (a gli agli)
(a le alle) (a l' all')
(da il dal) (da la dalla) (da gli dagli) (da l' dall')
(in il nel) (in la nella) (in gli negli) (in l' nell')
(su il sul) (su la sulla) (su gli sugli) (su l' sull')))
(contraction-mandatory yes) ; *di il -> del is obligatory, never uncontracted
(prep-no-contract (per tra fra)) ; per la strada (NOT *perla)
;; ── clitic system ──────────────────────────────────────────────────────
(clitics yes)
(clitic-placement ((finite proclitic) ; lo vedo, non me lo dà
(imperative-affirmative enclitic) ; dammelo, guardalo
(imperative-negative-tu non+infinitive) ; non parlare / non lo fare
(infinitive enclitic) ; vederlo, aiutarmi (drop -e)
(gerund enclitic))) ; dandolo
(clitic-combination ((mi lo "me lo") (ti lo "te lo") (ci lo "ce lo")
(vi lo "ve lo") (si lo "se lo")
(gli lo "glielo") (le lo "glielo"))) ; glielo = ONE word
(clitic-particles (ci ne)) ; locative ci, partitive ne
(raddoppiamento (da fa di va sta)) ; monosyllabic imper double clitic: dammelo
;; ── verb / aspect system ───────────────────────────────────────────────
(finite-agreement "person+number (6-way)")
(tenses (presente imperfetto passato-remoto futuro condizionale
congiuntivo-presente congiuntivo-imperfetto imperativo))
(compound-past "passato-prossimo = aux(present) + participle")
(perfect-aux "essere/avere (LEXICAL selection)") ; << Italian-specific
(essere-aux-class unaccusative) ; motion/change-of-state/copular/pronominal
; (andare venire nascere morire diventare piacere
; + ALL reflexives) -> essere
(participle-agreement ((essere subject) ; è andata / sono arrivati
(avere preceding-acc-clitic))) ; li ho visti
(progressive-aux "stare + gerundio") ; sto parlando
(copula "essere (default) / stare (state: sto bene)")
(passive-aux "essere / venire (+ da-agent)")
(future inflectional) ; parlerò, sarà
(comparative "più/meno ADJ di")
;; ── SACRED safety bar (shared with es/pt/en) ───────────────────────────
(negation-faithful yes) ; polarity never dropped/inverted; unplaceable -> FLAG
(negation "non (preverbal) + concord") ; non...niente/nessuno/mai/più
(negative-concord yes) ; preverbal negative word (nessuno/niente) suppresses non
(neg-adverb-position between-aux-and-participle)) ; non ho MAI visto
-30
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;;; lang_profile_la.el — Latin language profile for ELP.
;;; Keys the realizer's construction switches. Companion to morphology-la.el.
(lang_profile_la
(language "Latin")
(iso639 "la")
(family "Italic")
;; -- core typology flags -------------------------------------------------
(pro-drop yes) ; person carried by verb ending; subjects dropped
(obligatory-subject no)
(grammatical-gender yes) ; m/f/n; adjective AGREES in case+gender+number
(gender-source lexicon) ; REAL per-noun gender from UniMorph lat
(articles none) ; Latin has no articles
(case-system yes) ; NOM GEN DAT ACC ABL VOC (+ rare LOC)
(cases (nom gen dat acc abl voc))
(word-order "SOV (default; free order, case-marked)")
(adjective-position "either (case agreement carries the link)")
(adjective-agreement "case+gender+number")
;; -- verb / aspect system ------------------------------------------------
(verb-classes (1 2 3 3io 4)) ; four conjugations + i-stem 3rd
(tenses (present imperfect future perfect pluperfect futureperfect))
(moods (indicative subjunctive imperative infinitive))
(voices (active passive))
(finite-agreement "person+number (6 slots)")
(citation "principal parts: pres-1sg / pres-inf / perf-participle")
;; -- SACRED safety bar ---------------------------------------------------
(negation-faithful yes)) ; polarity never dropped/inverted
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;;; lang_profile_pt.el — Portuguese language profile for ELP.
;;; Keys the realizer's construction switches. Mirrors lang_profile_es.
(lang_profile_pt
(language "Portuguese")
(iso639 "pt")
(family "Romance")
;; -- core typology flags -------------------------------------------------
(pro-drop yes) ; subjects routinely dropped; agreement carries person
(obligatory-subject no)
(grammatical-gender yes) ; m/f on every noun; article+adjective AGREE
(gender-source lexicon) ; REAL per-noun gender from UniMorph por / kaikki
(do-support no)
(subject-aux-inversion no)
(question-strategy intonation)
(article-selection "o/a/os/as um/uma/uns/umas")
(adjective-position postnominal)
(adjective-agreement "gender+number")
;; -- MANDATORY CONTRACTIONS (prep + article) -----------------------------
(contractions ((de o "do") (de a "da") (em o "no") (em a "na")
(a o "ao") (a a "à") (por o "pelo") (por a "pela")))
(contraction-mandatory yes)
;; -- verb / aspect system ------------------------------------------------
(verb-classes (ar er ir))
(tenses (present preterite imperfect future conditional))
(moods (ind sbjv imp))
(finite-agreement "person+number (6 slots)")
(perfect-aux "ter") ; ter + past participle
(copula-split "ser/estar")
(personal-infinitive yes) ; distinctive PT inflected infinitive
;; -- clitics / government ------------------------------------------------
(object-clitics yes) ; mesoclisis/enclisis/proclisis by context
(verb-prep-government yes)
;; -- SACRED safety bar ---------------------------------------------------
(negation-faithful yes))
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;;; lang_profile_ro.el — Romanian language profile for ELP.
;;; Romanian is the BIG typological delta of the Romance family. The verb/clause
;;; engine and the SACRED negation contract mirror the ES/PT/IT core, but the
;;; NOMINAL system is genuinely new: a SUFFIXED definite article, preserved CASE,
;;; a NEUTER gender, and a VOCATIVE. Those flags mark where the shared engine was
;;; extended rather than reused.
(lang_profile_ro
(language "Romanian")
(iso639 "ro")
(family "Romance (Eastern / Balkan)")
;; ── core typology flags ────────────────────────────────────────────────
(pro-drop yes) ; null subjects default; overt pronoun = emphatic
(obligatory-subject no)
(grammatical-gender yes) ; m / f / NEUTER (n)
(neuter-gender yes) ; << ROMANIAN-SPECIFIC: masc-agreeing SG, fem-agreeing PL
; (un tren nou / două trenuri noi)
(do-support no)
(subject-aux-inversion no) ; yes/no Q = declarative order + '?'
(question-punct plain) ; ? and ! only
;; ── SUFFIXED DEFINITE ARTICLE (the headline engine extension) ───────────
(definite-article suffixed) ; << UNIQUE IN ROMANCE: enclitic on the noun
(definite-forms ((m/n sg "-ul / -le / -l : om->omul, câine->câinele, codru->codrul")
(f sg "-a / -ea / -ua : casă->casa, carte->cartea, stea->steaua")
(m pl "-i : oameni->oamenii")
(f/n pl "-le : case->casele, trenuri->trenurile")))
(article-host ((no-prenom-adj noun) ; omul bun
(prenom-adj adjective))) ; bunul om (adj carries the article)
(indefinite-article ((m/n "un") (f "o") (pl "niște") (gen/dat-pl "unor")))
;; ── CASE (preserved; NOM/ACC vs GEN/DAT) ────────────────────────────────
(case (nom/acc gen/dat vocative)) ; << ROMANIAN-SPECIFIC
(case-syncretism "nom=acc ; gen=dat")
(genitive-marking "gen/dat definite: -lui (m/n), -ei/-i (f), -lor (pl)")
(genitival-article ((m sg "al") (f sg "a") (m pl "ai") (f/n pl "ale"))) ; o carte a lui
(possession "definite-head + gen/dat possessor: casa băiatului")
(vocative ((m sg "-ule/-e : omule, băiete") (f sg "-o : Mario, fato")
(pl "-lor")))
;; ── verb / aspect system ────────────────────────────────────────────────
(finite-agreement "person+number (6-way)")
(tenses (prezent imperfect perfect-simplu conjunctiv-prezent
imperativ (periphrastic: perfect-compus viitor conditional)))
(compound-past "perfectul compus = a-avea-clitic + INVARIABLE participle")
(perfect-aux "a avea (am/ai/a/am/ați/au) — ONE auxiliary for ALL verbs")
(perfect-aux-split no) ; << SIMPLER than Italian: no essere/avere selection
(participle-agreement none) ; invariable in the perfect compus (agrees only as
; an adjective / in the passive)
(future "voi/vei/va/vom/veți/vor + infinitive (viitor literar)")
(conditional "aș/ai/ar/am/ați/ar + infinitive")
(subjunctive "conjunctiv: particle 'să' + subjunctive present")
(modal-complement "modal + să + subjunctive (vreau să merg, poți să ajuți)")
(copula "a fi")
(passive "a fi + participle (participle AGREES like an adjective)")
(comparative "mai / mai puțin ADJ decât")
;; ── clitic system (partial — see honest gaps) ───────────────────────────
(clitics yes)
(clitic-set ((acc te îl o ne îi le) (dat îmi îți îi ne le)
(refl te se ne se)))
(clitic-placement ((finite proclitic) ; îmi place, o văd
(perfect-compus elision) ; << m-am, l-am, i-am (PARTIAL)
(imperative-affirmative enclitic))) ; dă-mi (PARTIAL)
;; ── SACRED safety bar (shared with es/pt/it/en) ─────────────────────────
(negation-faithful yes) ; polarity never dropped/inverted; unplaceable -> FLAG
(negation "nu (single preverbal marker) + concord")
(negative-concord yes) ; nu … nimic / nimeni / niciodată / niciun
(negative-imperative "nu + INFINITIVE : nu pleca! (KNOWN GAP: uses imperative stem)"))
+46 -46
View File
@@ -1,46 +1,46 @@
// auto-generated by elc --emit-header do not edit
extern fn lang_profile(code: String, word_order: String, morph_type: String, has_case: String, has_gender: String, script_dir: String, agreement: String, null_subject: String) -> [String]
extern fn lang_get(profile: [String], key: String) -> String
extern fn lang_profile_en() -> [String]
extern fn lang_profile_ja() -> [String]
extern fn lang_profile_ar() -> [String]
extern fn lang_profile_zh() -> [String]
extern fn lang_profile_de() -> [String]
extern fn lang_profile_es() -> [String]
extern fn lang_profile_fi() -> [String]
extern fn lang_profile_sw() -> [String]
extern fn lang_profile_hi() -> [String]
extern fn lang_profile_ru() -> [String]
extern fn lang_profile_fr() -> [String]
extern fn lang_profile_la() -> [String]
extern fn lang_profile_he() -> [String]
extern fn lang_profile_sa() -> [String]
extern fn lang_profile_got() -> [String]
extern fn lang_profile_non() -> [String]
extern fn lang_profile_enm() -> [String]
extern fn lang_profile_pi() -> [String]
extern fn lang_profile_grc() -> [String]
extern fn lang_profile_ang() -> [String]
extern fn lang_profile_fro() -> [String]
extern fn lang_profile_goh() -> [String]
extern fn lang_profile_sga() -> [String]
extern fn lang_profile_txb() -> [String]
extern fn lang_profile_peo() -> [String]
extern fn lang_profile_akk() -> [String]
extern fn lang_profile_uga() -> [String]
extern fn lang_profile_egy() -> [String]
extern fn lang_profile_sux() -> [String]
extern fn lang_profile_gez() -> [String]
extern fn lang_profile_cop() -> [String]
extern fn lang_from_code(code: String) -> [String]
extern fn lang_default() -> [String]
extern fn lang_is_isolating(profile: [String]) -> Bool
extern fn lang_is_agglutinative(profile: [String]) -> Bool
extern fn lang_is_fusional(profile: [String]) -> Bool
extern fn lang_is_polysynthetic(profile: [String]) -> Bool
extern fn lang_is_rtl(profile: [String]) -> Bool
extern fn lang_has_null_subject(profile: [String]) -> Bool
extern fn lang_has_case(profile: [String]) -> Bool
extern fn lang_has_gender(profile: [String]) -> Bool
extern fn lang_word_order(profile: [String]) -> String
extern fn lang_code(profile: [String]) -> String
// auto-generated by elc --emit-header - do not edit
extern fn lang_profile(code: String, word_order: String, morph_type: String, has_case: String, has_gender: String, script_dir: String, agreement: String, null_subject: String) -> Any
extern fn lang_get(profile: Any, key: String) -> String
extern fn lang_profile_en() -> Any
extern fn lang_profile_ja() -> Any
extern fn lang_profile_ar() -> Any
extern fn lang_profile_zh() -> Any
extern fn lang_profile_de() -> Any
extern fn lang_profile_es() -> Any
extern fn lang_profile_fi() -> Any
extern fn lang_profile_sw() -> Any
extern fn lang_profile_hi() -> Any
extern fn lang_profile_ru() -> Any
extern fn lang_profile_fr() -> Any
extern fn lang_profile_la() -> Any
extern fn lang_profile_he() -> Any
extern fn lang_profile_sa() -> Any
extern fn lang_profile_got() -> Any
extern fn lang_profile_non() -> Any
extern fn lang_profile_enm() -> Any
extern fn lang_profile_pi() -> Any
extern fn lang_profile_grc() -> Any
extern fn lang_profile_ang() -> Any
extern fn lang_profile_fro() -> Any
extern fn lang_profile_goh() -> Any
extern fn lang_profile_sga() -> Any
extern fn lang_profile_txb() -> Any
extern fn lang_profile_peo() -> Any
extern fn lang_profile_akk() -> Any
extern fn lang_profile_uga() -> Any
extern fn lang_profile_egy() -> Any
extern fn lang_profile_sux() -> Any
extern fn lang_profile_gez() -> Any
extern fn lang_profile_cop() -> Any
extern fn lang_from_code(code: String) -> Any
extern fn lang_default() -> Any
extern fn lang_is_isolating(profile: Any) -> Bool
extern fn lang_is_agglutinative(profile: Any) -> Bool
extern fn lang_is_fusional(profile: Any) -> Bool
extern fn lang_is_polysynthetic(profile: Any) -> Bool
extern fn lang_is_rtl(profile: Any) -> Bool
extern fn lang_has_null_subject(profile: Any) -> Bool
extern fn lang_has_case(profile: Any) -> Bool
extern fn lang_has_gender(profile: Any) -> Bool
extern fn lang_word_order(profile: Any) -> String
extern fn lang_code(profile: Any) -> String
-1
View File
@@ -56,7 +56,6 @@
// String helpers
import "morphology.el"
fn akk_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn akk_str_ends(s: String, suf: String) -> Bool
extern fn akk_str_len(s: String) -> Int
extern fn akk_str_drop_last(s: String, n: Int) -> String
-1
View File
@@ -36,7 +36,6 @@
// String helpers
import "morphology.el"
fn ang_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn ang_str_ends(s: String, suf: String) -> Bool
extern fn ang_str_drop_last(s: String, n: Int) -> String
extern fn ang_str_last_char(s: String) -> String
-1
View File
@@ -21,7 +21,6 @@
// String helpers
import "morphology.el"
fn ar_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn ar_str_ends(s: String, suf: String) -> Bool
extern fn ar_str_len(s: String) -> Int
extern fn ar_str_drop_last(s: String, n: Int) -> String
-1
View File
@@ -54,7 +54,6 @@
// String helpers
import "morphology.el"
fn cop_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn cop_str_ends(s: String, suf: String) -> Bool
extern fn cop_str_len(s: String) -> Int
extern fn cop_drop(s: String, n: Int) -> String
-1
View File
@@ -26,7 +26,6 @@
// Dat: dem der dem den
// Gen: des der des der
import "morphology.el"
fn de_article_def(gender: String, gram_case: String, number: String) -> String {
if str_eq(number, "pl") {
if str_eq(gram_case, "nom") { return "die" }
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn de_article_def(gender: String, gram_case: String, number: String) -> String
extern fn de_article_indef(gender: String, gram_case: String, number: String) -> String
extern fn de_article(gender: String, gram_case: String, number: String, definite: String) -> String
-1
View File
@@ -52,7 +52,6 @@
// String helpers
import "morphology.el"
fn egy_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn egy_str_ends(s: String, suf: String) -> Bool
extern fn egy_str_len(s: String) -> Int
extern fn egy_drop(s: String, n: Int) -> String
-1
View File
@@ -31,7 +31,6 @@
// String helpers
import "morphology.el"
fn enm_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn enm_str_ends(s: String, suf: String) -> Bool
extern fn enm_drop(s: String, n: Int) -> String
extern fn enm_first_char(s: String) -> String
-1
View File
@@ -12,7 +12,6 @@
// String helpers (local, matching morphology.el conventions)
import "morphology.el"
fn es_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn es_str_ends(s: String, suf: String) -> Bool
extern fn es_str_drop_last(s: String, n: Int) -> String
extern fn es_str_last_char(s: String) -> String
-1
View File
@@ -25,7 +25,6 @@
// If only neutral vowels are found, default to "front" (the conservative choice
// for borrowed words and those without clear back vowels).
import "morphology.el"
fn fi_harmony(word: String) -> String {
let n: Int = str_len(word)
let i: Int = n - 1
+3 -3
View File
@@ -1,11 +1,11 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn fi_harmony(word: String) -> String
extern fn fi_suffix(base: String, harmony: String) -> String
extern fn fi_noun_case(stem: String, gram_case: String, number: String, harmony: String) -> String
extern fn fi_str_last_char(s: String) -> String
extern fn fi_apply_case(noun: String, gram_case: String, number: String) -> String
extern fn fi_verb_stem(dict_form: String) -> String
extern fn fi_irregular_verb(dict_form: String) -> [String]
extern fn fi_irregular_verb(dict_form: String) -> Any
extern fn fi_present_ending(stem: String, person: String, number: String, harmony: String) -> String
extern fn fi_past_stem(stem: String) -> String
extern fn fi_past_ending(stem: String, person: String, number: String, harmony: String) -> String
@@ -14,4 +14,4 @@ extern fn fi_negative(verb: String, person: String, number: String) -> String
extern fn fi_conjugate(verb: String, tense: String, person: String, number: String) -> String
extern fn fi_question_suffix(harmony: String) -> String
extern fn fi_make_question(verb_form: String, harmony: String) -> String
extern fn fi_full_paradigm(noun: String) -> [String]
extern fn fi_full_paradigm(noun: String) -> Any
-1
View File
@@ -19,7 +19,6 @@
// String helpers (local, matching morphology.el conventions)
import "morphology.el"
fn fr_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn fr_str_ends(s: String, suf: String) -> Bool
extern fn fr_str_drop_last(s: String, n: Int) -> String
extern fn fr_str_last_char(s: String) -> String
-1
View File
@@ -53,7 +53,6 @@
// String helpers
import "morphology.el"
fn fro_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn fro_str_ends(s: String, suf: String) -> Bool
extern fn fro_drop(s: String, n: Int) -> String
extern fn fro_slot(person: String, number: String) -> Int
-1
View File
@@ -64,7 +64,6 @@
// String helpers
import "morphology.el"
fn gez_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn gez_str_ends(s: String, suf: String) -> Bool
extern fn gez_str_len(s: String) -> Int
extern fn gez_str_drop_last(s: String, n: Int) -> String
-1
View File
@@ -48,7 +48,6 @@
// String helpers
import "morphology.el"
fn goh_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn goh_str_ends(s: String, suf: String) -> Bool
extern fn goh_drop(s: String, n: Int) -> String
extern fn goh_slot(person: String, number: String) -> Int
-1
View File
@@ -49,7 +49,6 @@
// String helpers
import "morphology.el"
fn got_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn got_str_ends(s: String, suf: String) -> Bool
extern fn got_str_drop_last(s: String, n: Int) -> String
extern fn got_slot(person: String, number: String) -> Int
-1
View File
@@ -31,7 +31,6 @@
// String helpers
import "morphology.el"
fn grc_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn grc_str_ends(s: String, suf: String) -> Bool
extern fn grc_str_drop_last(s: String, n: Int) -> String
extern fn grc_str_last_char(s: String) -> String
-1
View File
@@ -51,7 +51,6 @@
// String helpers
import "morphology.el"
fn he_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn he_str_ends(s: String, suf: String) -> Bool
extern fn he_str_len(s: String) -> Int
extern fn he_str_drop_last(s: String, n: Int) -> String
-1
View File
@@ -24,7 +24,6 @@
// String helpers
import "morphology.el"
fn hi_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn hi_str_ends(s: String, suf: String) -> Bool
extern fn hi_str_drop_last(s: String, n: Int) -> String
extern fn hi_str_last_char(s: String) -> String
-1
View File
@@ -23,7 +23,6 @@
// Note: this is a heuristic classifier for romanized input. For production use
// with native kana/kanji forms, the dictionary form (辞書形) must be consulted.
import "morphology.el"
fn ja_verb_group(dict_form: String) -> String {
// Irregular verbs (exact match on dictionary form)
if str_eq(dict_form, "する") { return "irregular" }
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn ja_verb_group(dict_form: String) -> String
extern fn ja_ichidan_stem(dict_form: String) -> String
extern fn ja_godan_stem_change(dict_form: String, row: String) -> String
-1
View File
@@ -25,7 +25,6 @@
// String helpers
import "morphology.el"
fn la_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn la_str_ends(s: String, suf: String) -> Bool
extern fn la_str_drop_last(s: String, n: Int) -> String
extern fn la_str_last_char(s: String) -> String
-1
View File
@@ -27,7 +27,6 @@
// String helpers
import "morphology.el"
fn non_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn non_str_ends(s: String, suf: String) -> Bool
extern fn non_drop(s: String, n: Int) -> String
extern fn non_last(s: String) -> String
-1
View File
@@ -31,7 +31,6 @@
// String helpers
import "morphology.el"
fn peo_drop(s: String, n: Int) -> String {
let len: Int = str_len(s)
if n >= len { return "" }
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn peo_drop(s: String, n: Int) -> String
extern fn peo_ends(s: String, suf: String) -> Bool
extern fn peo_slot(person: String, number: String) -> Int
-1
View File
@@ -30,7 +30,6 @@
// String helpers
import "morphology.el"
fn pi_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn pi_str_ends(s: String, suf: String) -> Bool
extern fn pi_drop(s: String, n: Int) -> String
extern fn pi_last_char(s: String) -> String
-1
View File
@@ -35,7 +35,6 @@
// The heuristic returns the most probable gender. Caller should override
// for known exceptions (путь, рубль are masc despite ).
import "morphology.el"
fn ru_gender(noun: String) -> String {
let n: Int = str_len(noun)
if n == 0 { return "m" }
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn ru_gender(noun: String) -> String
extern fn ru_stem_type(noun: String, gender: String) -> String
extern fn ru_noun_case(noun: String, gender: String, gram_case: String, number: String) -> String
-1
View File
@@ -42,7 +42,6 @@
// String helpers
import "morphology.el"
fn sa_str_ends(s: String, suf: String) -> Bool {
return str_ends_with(s, suf)
}
+1 -1
View File
@@ -1,4 +1,4 @@
// auto-generated by elc --emit-header do not edit
// auto-generated by elc --emit-header - do not edit
extern fn sa_str_ends(s: String, suf: String) -> Bool
extern fn sa_str_drop_last(s: String, n: Int) -> String
extern fn sa_slot(person: String, number: String) -> Int

Some files were not shown because too many files have changed in this diff Show More