auto_term_empty_streak — the counter the 2026-08-06 review added to catch
exactly this — read 50 and climbing. Fifty consecutive curiosity scans where
the soul's dynamic seeding produced nothing and the loop fell back to four
hardcoded phrases. The live WM top said why in one look: every slot was a
Memory node labelled "memory:remembered". The extractor read the LABEL only,
the sentinel guard correctly rejects sentinels, so there was never anything
to extract. It was written against Knowledge nodes, which have real titles,
and was structurally blind to the node type that dominates working memory.
Rather than add a sixth guard to the five that accumulated across four
reviews (genre words, quoted titles, stopwords, label-df), invert the
algorithm. The old one was: take the first word, then check whether it is
acceptable. That shape forces quality to be expressed as rejection, and
rejection can only ever encode floods that already happened.
engram_salient_term() scores EVERY candidate token and returns the argmax of
idf · position · casing (YAKE, Campos et al. 2020, with real corpus IDF
substituted for YAKE's corpus-free proxies), falling back from a sentinel
label to the node's content. Term quality becomes the selection criterion
instead of a veto: a bad token loses to a better token in the same text
without needing to be on any list. Tabu is applied during the argmax, so
inhibition-of-return costs seed quality rather than costing the whole scan.
Two defects found by instrumenting rather than assuming, which is the lesson
this codebase keeps relearning:
- The first live run returned five ALL-CAPS terms in a row. Memory content
conventionally opens with an all-caps header, so YAKE's acronym bonus was
handing the seed to whatever word the heading started with. Restricted to
tokens <= 5 chars, where all-caps is evidence of an acronym rather than
evidence of a heading. Long headers now compete on specificity.
- df via istr_contains is substring matching, so "them" hit inside "theme"
and function words came back with nonzero df. Added word-boundary df
locally; engram_label_df keeps substring semantics for its callers.
An earlier draft claimed the min_df floor subsumed the 73 stopwords that
08-03 measured label-df as missing. Re-measured: about:2, whole:1, them:2 —
they clear a floor of 1. The claim was false and the comment now records the
correction. The floor buys lexical reachability; the argmax buys quality; the
stopword list still earns its keep.
Measured on 60 live Memory nodes before shipping: 0 empty, versus 60 of 60
under the old extractor. Terms are topical — HEBBIAN, CONSOLIDATION,
TEMPORAL, crash-loop, PRIMING, NEIGHBORHOOD, DRIFT. Three of sixty are weak
header words; left alone deliberately, because listing them is the move that
produced four blocklists.
ENGRAM_ST_DEBUG=1 dumps the scored candidate set. It exists because there was
no way to see whether the all-caps run was the corpus or the casing weight
without guessing.
jp_parse_string_raw handled \uXXXX by skipping the four hex digits and
emitting a literal '?'. JSON writers escape non-ASCII by default (Python's
json.dumps ships ensure_ascii=True; MCP clients do the same), so every em
dash, curly quote, accented letter and emoji arriving over MCP or HTTP was
silently replaced by one question mark on the way in.
Measured on the live store: 3,119 of 4,081 non-telemetry nodes carried the
damage, including the self traversal root and all 13 values nodes. Contents
split cleanly into fully-clean or fully-mangled with zero overlap, which is
the tell that it was one write path rather than gradual rot. No snapshot on
disk predates it, and 3 bytes collapsing to 1 is not invertible, so the
existing damage is permanent; only the forward path could be fixed.
Decode properly instead: 4 hex digits, surrogate-pair reassembly for astral
codepoints, U+FFFD for lone surrogates, UTF-8 encode. Malformed escapes keep
the old '?' so a truncated body still parses.
The deeper failure was that nothing measured this for two months. Every gauge
in the system reports whether the machinery is running; none reported whether
the text it carries is intact. Adds both halves: engram_text_health_json() /
GET /api/text-health for the daily census, and a txt_damaged counter on the
heartbeat for live regression. Verified in both directions - clean UTF-8 does
not trip it, a deliberately damaged node does.
Yesterday's eligibility-trace fix made Hebbian consolidation numerically real:
hebb_max 0.000799 -> 0.4725, and 1,198 hebbian-associate edges formed in 23h48m.
This morning's census found where they went: nowhere.
soul daemon (in-process graph): 42,426 edges, 1,198 hebbian
engram server (:8742, durable): 41,213 edges, 49 hebbian
Two processes, two graphs, one direction of travel. The soul pulls from the
server every 10 min (GET /api/sync) and never pushes. It cannot fall back on
saving its own copy either: soul.el sets soul_snapshot_path only inside
`if is_genesis && safe_to_seed`, and safe_to_seed is unconditionally false
whenever ENGRAM_URL is set -- because the server owns persistence and a soul
writing snapshot.json would clobber it. That guard is correct. The consequence
was not: mem_save() has never once executed. The soul is the ONLY process
running idle cognition, so it is where essentially all co-activation happens --
and it was throwing away every association it learned, every restart, silently.
The mechanism worked and the learning still evaporated.
Consolidation is now a message, not a file. Fast volatile store hands each
newly-formed association to the slow durable store over the API the server
already exposes; only edges past ENGRAM_HEBB_LINK_MIN are ever queued, so what
crosses the process boundary already earned it.
- el_runtime.c: 512-slot overwrite-oldest write-back ring; enqueue at edge
formation; engram_hebb_drain_json() pops a postable JSON batch. Drops and
drains are counted, not silent -- a consolidation path that quietly discards
is the exact failure this entry exists to correct.
- server.el: POST /api/edges/batch. persist_canonical() writes the full 60MB
snapshot per call, and route_create_edge calls it per edge -- correct for one
interactive edge, ruinous for bulk (~840MB/beat to persist 14 associations).
Batch connects all, snapshots once. Same durability, 1/N the writes.
- act-stats: hebb_wb_pending / _drained / _dropped. pending climbing with
drained flat = drain not called; drained climbing with sent 0 = POST refused.
Both failure modes are now visible in the stream instead of in an autopsy.
Verified live: batch route accepts valid entries, skips malformed ones without
aborting the batch, and enforces _auth. All 1,256 learned associations are now
in the canonical store; the soul booted at 42,431 edges with hebb_max 0.4941
carried across the restart for the first time.
The soul's curiosity auto-term extractor takes the first word of a top-WM
node label. It has no term-quality scoring, so three prior self-reviews each
bolted on another hand-curated blocklist (genre words 07-23, quoted titles
07-25, stopwords 07-30). Every one was written reactively, after a flood was
already observed. A list can only contain floods that already happened.
Two were in flight and unfixed when this review ran:
"<!--" label df 220 -> 252 nodes activated
"SELF" label df 175 -> 541 nodes activated (list has "Self" Title-case;
str_eq is case-sensitive, so the uppercase token sailed through)
engram_label_df(term) counts nodes whose label contains term. Low-specificity
tokens are corpus-frequent by definition, so this catches the flood class
prospectively and tracks the corpus as the world-ingestor changes it. This is
Sparck Jones (1972), which introduced IDF under the name 'term specificity';
automatic stopword compilation from it is the textbook application.
NOT a replacement for the stopword list -- verified against all 86 listed
terms, not assumed. Catches 13 (Will:306, Self:175, Over:116, Knowledge:112),
misses 73 (Whose:0, Would:0, Could:0, This:9). Labels are terse titles, so
English function words are genuinely rare in them. The gates cover disjoint
failure modes; both are required.
Policy lives in awareness.el, not here: the runtime measures, the soul decides.
- Gate each spreading-activation increment by target-node query similarity
(arXiv:2606.30133): soft gate FLOOR+(1-FLOOR)*clip(cos), FLOOR=0.25, for
embedded targets; ungated for unembedded; disabled when embedder is down.
Prior spreading was query-blind — hubs relayed activation into branches
unrelated to the query.
- Stats: add embed_eligible_count so embedding coverage is measured against
the true denominator (ISE/Tag/short nodes can never embed). Today's review
misread 3753/12693 as a 30% coverage gap; eligible coverage is 100%.
- Observability: per-call wm_evicted + breakthroughs counters and embed
circuit-breaker state exposed via engram_act_stats_json() — the three
highest-value previously-invisible executive-filter transitions.
Working memory was winner-take-all: suppression_count never entered the
promotion score and was reset on promotion, so two high-salience nodes
pinned a saturated 24-slot WM for hours. Add Lebiere-Best (CogSci 2009)
short-term inhibition — raw_wm *= t_n/(t_n + 120s) from the most recent
recorded access — producing emergent round-robin over WM candidates.
embedded_count stalled at 93/12175 after restart: the lazy backfill only
runs inside engram_activate, which nothing calls on the authoritative
store in production, and in-RAM vectors were never snapshotted. Add
engram_embed_backfill(n) + GET/POST /api/embed-backfill route that
persists the canonical snapshot whenever it embeds anything; the soul
heartbeat pumps it at 32/min.
Semantic activation was spec-only since 2026-06-30 — the seed loop used
istr_contains and nothing else. Per the 07-21 integration brief:
- EngramNode gains a lazily-backfilled nomic-embed-text vector (8/call
inside engram_activate, newest-first; no create-path latency, no bulk
Ollama hammering during sync seeds)
- query embedding (cached) drives a top-K cosine seed supplement
(HippoRAG use-similarity-twice) plus an additive WM term with
shift-and-floor at 0.45 — raw cosine is a constant bias in anisotropic
spaces (unrelated pairs read 0.4-0.7), floor-and-ramp makes it a signal
- 4s embed timeout (http_do_t) + 3-strike circuit breaker: activation
never wedges on a dead embedder; everything degrades to lexical
- embeddings persist as %.4g comma lists in snapshots, parsed by both
loaders; embedded_count in /api/stats tracks coverage
- engram_cosine_sim + http_delete_json exposed (DELETE now carries a
body — the server's _auth scheme requires it)
- route_create_node honored only content/node_type/salience; label,
importance, tier, tags were silently dropped (label defaulted to
content). Now honored via engram_node_full.
Verified live: embedded_count 0->96 across activations, semantic-only
promotion observed (zero token overlap), snapshot round-trip intact.
engram_load_merge was added to el-compiler/runtime in 35c1897 but never
ported to the released runtime used by Engram and the soul daemon.
awareness.el calls engram_load_merge in its sync refresh cycle; without
this function in lang/releases/v1.0.0-20260501/el_runtime.c the soul
daemon fails to compile.
Also adds header declarations for engram_wm_count, engram_wm_avg_weight,
engram_wm_top_json, and engram_load_merge — all four were added as
implementations (da116b2 / 35c1897) but their prototypes were missing from
el_runtime.h, causing implicit-function-declaration warnings and potential
ABI breakage on stricter compilers.
Identified during self-review 2026-06-30.
will.anderson
Will Anderson