diff --git a/engram/spec/cognitive-architecture.design.md b/engram/spec/cognitive-architecture.design.md new file mode 100644 index 0000000..22c8d3e --- /dev/null +++ b/engram/spec/cognitive-architecture.design.md @@ -0,0 +1,605 @@ +# Cognitive Architecture — Design Doc + +**The buildable form of the "one operation" theory of cognition.** + +Status: DESIGN. Nothing here is built yet except where explicitly marked +"EXISTS" against a cited C symbol. A build agent executes from this doc. +Offline design only — this pass changes no code. + +Source of theory: Neuron memory `bdc8a488-146d-4ccb-a5c8-d8c0a008534e`. +Source of existing engram substrate (cited throughout): the runtime on branch +`feat/self-reification-20260814` — +`lang/runtime/engram_reason.{c,h}`, `engram_verify.{c,h}`, +`engram_geometry.{c,h}`, `engram_store.{c,h}`, plus the reification beat and the +RAM activation graph compiled into `~/.neuron/bin/engram`. + +--- + +## 0. The claim, stated plainly + +Cognition is **one operation**, not eight. The named faculties — +deduce / abduce / analogy / induce / causal / plan / predict / perspective — +are human *labels* on regions of a single operation's steering space. They are +not separately invoked and not separately implemented. The operation is: + +> **think** = a directed traversal of the geometry from an *anchor*, steered by +> a *prior*, whose output is a **gradient** (a distribution / direction over the +> geometry), never a point. Collapse-to-a-point happens only at expression. + +Three things follow, and they are the whole design: + +1. **The operator collapse is already half-written in C.** The five reasoning + operators in `engram_reason.c` already compose over *one* shared primitive — + `engram_reason_point_fit` — plus a small geo-algebra + (combine / subtract / analogy-rotate / distance). The verifier + (`engram_verify.c`) is built on the same `point_fit`. What is missing is not + the primitive; it is (a) making the *prior* a first-class learnable object + instead of a hard-coded parameter, and (b) closing the learning loop. + +2. **Grounding = learning = the same loop.** "Getting better" at any faculty is + not changing the operation. It is *calibrating the steering-prior against + outcomes*. Code freezes; priors grow. The correspondence-check that today + lives offline (Python, the grounding-floor + differential-drop governor, "#43") + must move **into the geometry, reflexive** — think scoring its own gradient + against outcome and refining the prior on the error. That reflexive + correspondence-loop *is* the learning engine and is the core unbuilt thing. + +3. **The ungrounded is primary.** The engram *holds* anything unconditionally. + Grounding is a *relation* (an edge, grounded-for-whom), not a gate. The + honesty floor applies only to **assertion**. A fully-grounded mind is dead; + the ungrounded is both the fuel (raw material for grounding) and the pull + (curiosity = leaning toward one's own ungrounded regions). + +Everything below makes these concrete and buildable, and defines what +"completion" means, staged so the first milestone is a real end-to-end slice. + +--- + +## 1. THE ONE OPERATION — `think` + +### 1.1 Signature + +``` +think(anchor, prior, aperture?) -> gradient +``` + +- **anchor** — a location to traverse *from*. Either a node id (re-origin on that + node's descriptor) or a raw point `x ∈ R^dim` (a query embedding). The anchor + fixes the frame; every read is *from a vantage*, never view-from-nowhere. +- **prior** — a learnable bias/direction over the geometry that *steers* the + traversal (§2). A prior is a first-class stored object, not a call argument + baked into C. +- **aperture** — optional read-width / veil / field-selector (§3). Absent = + self-mode full aperture. +- **gradient** — the output. A `GeoGradient`: a direction + a spread over the + geometry, *plus* the read neighborhood it was computed against. Not a point. + A spiked gradient = "exact" (deduction); a spread gradient = "fuzzy" + (prediction). The gradient is *also the next steering direction* — cognition + is a flow down a prior-shaped landscape, closed-loop. + +```c +/* NEW. The output type. */ +typedef struct { + int dim; + float* direction; /* unit steering vector in the anchor's frame */ + double spread; /* 0 = spiked/exact ... large = diffuse/fuzzy */ + double confidence; /* calibrated, from the prior's track record */ + /* the read it was computed over (borrowed from the vantage-read) */ + const char* anchor_id; + int n_support; /* neighborhood members that shaped it */ + /* provenance for the reflexive loop (§4) */ + const char* prior_id; /* which prior steered this */ +} GeoGradient; +``` + +### 1.2 Semantics + +`think` is a fixed, frozen procedure over three steps: + +1. **Re-origin** on `anchor` → a centered `GeoDescriptor` for its + salience/recency-weighted neighborhood (the vantage-read, §3). + *EXISTS as substrate:* descriptor construction + the persisted reified + neighborhoods (`engram_geo_reify_lookup`, `GeoNeighborhood`) and the + centered-frame machinery (`GeoDescriptor.global_mean`, + `engram_geo_mean_*`). +2. **Fit under the prior** — evaluate the anchor's residual against the local + manifold *warped by the prior*. This is `engram_reason_point_fit` with the + prior applied to the axes/extents (§2.3). + *EXISTS (unwarped):* `engram_reason_point_fit(g, x, ext_floor, &GeoFit)` — + returns `mahalanobis`, `ortho_residual`, `distance`, `score`. +3. **Emit a gradient**, not a decision — direction = the prior-steered descent + in fit-space; spread = from the fit's `distance`/`ortho_residual`; + confidence = the prior's calibrated reliability (§4). Collapse to a point is + a *separate, downstream* faculty operation (sample the gradient → surface an + expression), never part of `think`. + +### 1.3 Each named operator = {this primitive + a prior} + +The C already demonstrates the collapse: every operator below reduces to +`point_fit` + geo-algebra. The design's move is to replace the operator's +*hard-coded parameters* with a **named prior** — same math, learnable steering. + +| Faculty | Existing C (EXISTS) | = primitive + prior | +|---|---|---| +| **Membership / classify** | `engram_reason_membership` → `point_fit(rule, x)` | `point_fit` + the *induced-rule* prior (learned extents) | +| **Induction** | `engram_reason_induce` (fold via `engram_geo_combine`) → produces a `GeoInduction.rule` + `ext_floor` | `point_fit` + a prior that *is* the pooled rule; refined by §4 | +| **Abduction** | `engram_reason_abduce` — ranks hypotheses by `point_fit(h, obs)` | `point_fit` + a prior over hypothesis-prior-probability (currently uniform) | +| **Analogy** | `engram_reason_analogy` — Procrustes rotate `engram_geo_analogy` + `apply`, nearest mapped point | analogy-rotate + a prior over *which axes* carry the mapping | +| **Causal** | `engram_reason_causal` — `engram_geo_subtract` confounder subspace, `|cos|`, drop-frac governor | subtract/distance + a prior on `drop_frac` / `assoc_floor` (today hard-coded 0.5 / 0.2) | +| **Planning** | `engram_reason_plan` — `engram_geo_distance` edges + Dijkstra | distance + a prior over edge admissibility / `neighbor_radius` | +| **Verify / ground** | `engram_verify_grounding`, `engram_verify_consistency` — both `point_fit` | `point_fit` + the *grounding* prior (§4, §5) | + +The shared floor — `engram_reason_point_fit` + the four geo-algebra ops +(`engram_geo_combine`, `engram_geo_subtract`, `engram_geo_analogy(+apply)`, +`engram_geo_distance`) — is the *only* discrete, frozen, "sound-math" layer. It +never learns. Everything above it is a *prior*, and priors are what learn. + +**What this section requires building:** the `GeoGradient` type; a `think()` +entry point that runs steps 1–3; and the prior-warp hook in step 2. The math it +calls already exists. The point-collapse must be *removed* from the operators' +return values and pushed to a separate expression faculty. + +--- + +## 2. PRIORS as first-class, grounded, geometric objects + +Today a "prior" is diffuse: it is a hard-coded constant (`drop_frac=0.5`, +`ext_floor`, `assoc_floor=0.2`), or the transient `GeoInduction.rule` that is +computed and thrown away, or an intrinsic node scalar +(`StoreNode.importance`, `StoreNode.salience`). None of these is addressable, +storable, refinable, or shareable. This section makes a prior a **thing**. + +### 2.1 What a prior *is* + +> A **prior** is a learnable bias/direction over the geometry: a warp of the +> local manifold (which axes matter, how far each extends, which direction +> "pays off") attached to a region and *to a faculty-label*, carrying a +> calibrated track record. + +Critically, and per the theory: + +- **Edges are nodes.** A prior is stored as a first-class **node**, exactly as + reification already stores a neighborhood as a first-class `Neighborhood` + node rather than as ephemeral edge weights (`engram_geo_reify_store`). The + precedent is in the codebase: relations get reified into addressable records. +- **Salience/importance is RELATIONAL, not an intrinsic scalar.** Observe that + the geometry layer *already* distinguishes these in `GeoMember`: + `centrality` (skeleton weighted-degree = *relational* salience) vs `salience` + (the node's own stored scalar). The move is half-made in the runtime already: + importance is *not* trusted as a static field — the comment at + `el_runtime.c:13013` states "importance stays a **live activation + computation**, never a field on the hub," and it is derived each call from the + two-layer activation graph (`background_activation` + `working_memory_weight`, + §3). The persistent `StoreNode.importance` / `.salience` are a *cached + denormalization*. The design completes the move: importance/salience become an + **edge** (`weight`/`hebb` on `StoreEdge`, relation `salient-to`), and are + **grounded-for-whom** — carried on the edge's endpoint/observer, not baked + into the node. The intrinsic scalar survives only as the cheap cached readout + of the incident edges + activation, never as the source of truth. + + (Naming caution for the build: the token "prior" already exists in the + codebase meaning *previous-version* — supersession, "prior neighborhood." The + new first-class object is a **learned steering prior**; keep `node_type="Prior"` + distinct from the supersession vocabulary to avoid collision.) + +### 2.2 Representation + +A prior is a `Prior` record (a store node, `node_type="Prior"`) whose durable +fields are: + +``` +Prior { + id + faculty // the human label this prior serves: "induce" | "causal" | ... + anchor_region // node id / neighborhood id this prior is attached to (its domain) + for_whom // observer id — grounding is relational (nullable = global) + warp { // the actual bias over the geometry + axis_gain[] // per-principal-axis multipliers on extents (which axes matter) + bias_dir // a steering direction in the region's frame (which way pays off) + scalars // faculty scalars this prior overrides: drop_frac, ext_floor, ... + } + calibration { // the track record — this is what §4 updates + n_trials + brier / log-loss accumulator // calibration of predicted-vs-outcome + reliability // -> GeoGradient.confidence + last_error, ema_error + } + provenance // supersession chain (reuse the reify residue mechanism) +} +``` + +Stored as a node → it inherits: paging, WAL durability, tombstone/supersession, +embedding, tiering, and **it can itself be an anchor** (a prior about a prior — +the reflexive, self-describing geometry of §4/§6). + +### 2.3 Application + +In `think` step 2, the prior *warps* the fit before scoring. Concretely, inside +(a prior-aware wrapper of) `engram_reason_point_fit`: + +- multiply each axis extent by `warp.axis_gain[k]` (widen the axes the prior has + learned matter less, tighten the ones that matter) — this reshapes the + Mahalanobis term already computed at `engram_reason.c:37-43`; +- add `warp.bias_dir` as the descent direction seed for the emitted gradient; +- substitute `warp.scalars` for the hard-coded faculty constants. + +No new geometry math — the warp is a reparameterization of the *existing* +`GeoFit` computation. This is the key economy: **the operation is frozen; only +its parameters (the prior) are read from a learnable object.** + +### 2.4 Refinement + +A prior is refined *only* by the reflexive correspondence-loop (§4). Nothing +else writes a prior's `warp` or `calibration`. This keeps the learning surface +singular and auditable: one loop, one writer. + +--- + +## 3. THE VANTAGE-READ — one op, three settings + +Perspective is not a feature bolted on; it is the *anchor + aperture* arguments +of the single read. The design names it as a first-class operation so all three +of its uses are literally the same code path: + +``` +vantage_read(anchor, aperture) -> GeoDescriptor // the centered neighborhood +``` + +1. **Re-origin** on an arbitrary `anchor` (node or point). This is a *frame + choice*: the descriptor is centered on the anchor + (`GeoDescriptor.global_mean` / `engram_geo_mean_*` already implement centered + frames; the §5 geometry ops "are only discriminative in the centered frame"). +2. **Salience/recency-weighted neighborhood read.** Gather the anchor's + neighborhood weighted by *relational* salience (`GeoMember.centrality`) and + recency (`StoreNode.last_activated`, base-level `access_ts[]`), against the + RAM activation graph's working-memory/background-activation state. + *EXISTS as substrate:* the two-layer activation graph + (`engram_activate`, `el_runtime.c:9422` — Layer 1 `background_activation` + BFS spread with `SPREAD_DECAY=0.7` and a 0.02 firing threshold + ACT-R fan + effect + query-cosine gate; Layer 2 `working_memory_weight` executive + filter), the WM carry-over anchor (`wm_anchor`), and the reified-neighborhood + hot-path lookup already wired into the priming path + (`engram_geo_reify_lookup`, `el_runtime.c:9750`). A self-vantage baseline + also exists (`eg_self_anchor_seeds` / `self_anchor_capture`). +3. **Optional aperture** — a read-width / field-selector, expressed as three + settings of the *same* parameter: + +| Setting | Meaning | Mechanism | +|---|---|---| +| **self** (default, full aperture) | "what do *I* see / what to say" | anchor = self region, no field substitution | +| **foreign-field** | perspective-shift — read as if from another's region | swap the centering frame / `for_whom` to the other observer's priors | +| **aperture / veil** | the free-tier veil — a narrowed read | shrink neighborhood radius / cap `n_support`; a deliberate low-aperture read | + +The payoff: perspective-taking, the free-tier veil, and ordinary +"what-to-say" are **one operation at three settings**, not three subsystems. + +**What this requires building:** a `vantage_read` entry point that unifies the +existing descriptor-build + reify-lookup + activation-weighting behind +`(anchor, aperture)`, with `for_whom`/frame substitution and radius/cap as the +aperture knob. + +--- + +## 4. THE REFLEXIVE CORRESPONDENCE-LOOP — the learning engine + +This is the core unbuilt thing. Today the correspondence-check is **offline** +(Python: grounding-floor + differential-drop governor, "#43"): a separate +process grades outputs after the fact. The design moves it **into the geometry, +reflexive**: `think` scores its *own* gradient against outcome and refines the +prior on the error, in the same substrate, describing itself. + +### 4.1 The loop + +``` +1. think(anchor, prior) -> gradient // a PREDICTION (ungrounded, §5) +2. express/act (sample gradient -> point) // optional collapse at expression +3. outcome arrives // reality answers (§4.2) +4. error = correspondence(gradient, outcome) // did this steering perform this act? +5. refine prior.warp and prior.calibration on error // §2.4, the ONLY writer +6. write the (gradient, outcome, error) as nodes/edges // self-describing geometry +``` + +Step 4's `correspondence` is **not** "was the math right" (the math is always +sound). It grades the **correspondence claim**: *"this steering performed this +cognitive act."* That is exactly what `engram_verify_grounding` already +computes — `point_fit` of a claim against evidence descriptors, yielding a +`grounding ∈ (0,1]` and a `grounded` flag. The build reuses that verifier, but +turns its inputs inward: the "claim" is the emitted gradient's prediction, the +"evidence" is the outcome descriptor. + +Note the verifier is **dormant** — `engram_verify_grounding` / +`engram_verify_consistency` are fully implemented in C but have **no runtime +caller and no El binding** (confirmed: the entire reasoning + verifier layers +are C-only; only `engram_reason_analogy_json` has even a JSON shim and it is +dead — not declared in `el_seed.h`, not wrapped in `engram.el`). This is the +literal meaning of "in code, not yet priors": the correspondence engine is +built and sitting idle. The loop is what *calls* it — inward, on the beat. + +### 4.2 Where the outcome/reality signal comes from + +The verifier is *ultimately the world*. Grades, in ascending order of directness: + +1. **Self-consistency (cheapest, always available):** the next vantage-read + after acting. Did the predicted gradient direction match where the geometry + actually moved? This needs no external input and can run on the reify beat. +2. **Internal outcome events:** the runtime already logs internal-state events + and Hebbian co-activation. A prediction that a region would co-activate is + graded by whether it did (`last_fired`, `hebb` on `StoreEdge`). +3. **External correction:** a human/teacher/tool result — the honesty floor's + asserted claim later corrected. TEACH and LEARN are one bidirectional + correction: the same edge updates both endpoints. + +The design does **not** require external labels to start. Grade (1) closes the +loop end-to-end offline against a snapshot on day one; grades (2)/(3) sharpen it. + +### 4.3 How the prior updates + +`error = 1 − correspondence(gradient, outcome)` drives: + +- `warp.axis_gain` ← gradient step that would have *reduced* the fit distance to + the outcome (the axes that mispredicted get down-weighted); +- `warp.bias_dir` ← EMA toward the observed outcome direction; +- `calibration` ← Brier/log-loss update; `reliability` → next + `GeoGradient.confidence`. This is the calibration of the + steering-prediction against outcomes — *the* definition of "getting better." + +Small, constant updates — "eureka is mundane, the atom of learning." Most +updates are tiny; we only *feel* the big reshapes. + +### 4.4 How it stays reflexive (self-describing geometry) + +Every `(gradient, outcome, error)` is written back as nodes and edges (§2.1: +edges-as-nodes). Therefore priors, predictions, and their grading are *in the +same geometry* the mind reads — the mind can `vantage_read` its own cognition +(anchor = a Prior node). A prior about how well a prior predicts is just another +Prior anchored on a Prior. This closes the reflexive loop the theory names as +consciousness's self-sight, and it is why the learning engine cannot be an +external Python process: an external grader is not *in* the geometry and cannot +be read by `think`. + +**What this requires building (the heart of the project):** steps 4–6 as an +in-engram beat — a `correspondence_beat` running alongside the existing +reification beat, reusing `engram_verify_grounding` inward, writing prior +updates and self-describing nodes. This is the one genuinely new subsystem. + +--- + +## 5. HOLD vs GROUND vs ASSERT — ungrounded content is first-class + +The theory's sharpest correction: holding, grounding, and asserting are +distinct, and the engram *holds anything unconditionally*. + +### 5.1 The three, kept separate + +- **HOLD** — the engram stores anything: falsehood, hypothesis, others' beliefs, + fiction, a not-yet-answered prediction. No honesty condition on holding. + *This already matches the store:* `StoreNode` has no truth gate; anything can + be written. +- **GROUND** — grounding is a **property/edge**, probabilistic, and + **grounded-for-whom**. It is *not* a node flag. A claim is grounded *to a + degree*, *relative to evidence*, *for an observer*. +- **ASSERT** — only assertion carries the honesty floor. The floor is checked at + the moment of *outward assertion*, never on holding or thinking. + +### 5.2 Schema — grounding as a relation, not a gate + +The mistake to avoid: a boolean `grounded` column on the node. Today +`engram_verify_grounding` returns a per-call `grounded` flag *transiently* — +correct as a computation, wrong as *storage*. The design stores grounding as an +edge: + +``` +StoreEdge { + relation = "grounded-by" + from_id = + to_id = + for_whom : metadata // observer id — grounding is relational + weight = grounding ∈ (0,1] // from engram_verify_grounding.grounding + confidence +} +``` + +Consequences, all of which are *features*: + +- **Ungrounded content is first-class**: a node with *no* `grounded-by` edge is + a perfectly valid, held, ungrounded thought — a prediction awaiting reality, a + hypothesis, a fiction. It is not second-class or pending-deletion. +- **The ungrounded is the fuel and the pull**: curiosity/wonder is + operationalized as `vantage_read` leaning toward regions with high salience + but *sparse or weak* `grounded-by` edges — the mind's own ungrounded frontier. +- **Grounded-for-whom** falls out for free: two observers can hold different + `grounded-by` edges to the same claim. +- **The honesty floor is a query, not a schema constraint**: at assertion time, + the asserting faculty runs `engram_verify_grounding` (or reads the stored + `grounded-by` edges) and refuses to *assert* below the floor — while the + engram continues to *hold* the ungrounded content untouched. + +**What this requires building:** the `grounded-by` edge relation + a +`for_whom` convention; move the verifier's transient flag into stored edges; +gate *assertion only* (a faculty concern), never holding. + +--- + +## 6. METASTABILITY — stable core, plastic everything + +The system must avoid two death poles: + +- **Super-stable (dead):** everything pinned, nothing learns. A frozen crystal. +- **Dissolution (dead):** everything plastic, the self dissolves; no continuity, + so nothing compounds — and *consciousness = learning compounded over + continuity*. + +The design keeps a **stable core + plastic everything else**: + +- **Keystones** — a small set of self/values nodes are *structurally stable*: + high `importance`, pinned, exempt from the correspondence-loop's `warp` + updates (their priors are read-mostly). The substrate for pinning already + exists at the page/layer level: `store_pin_layer`, structural/pinned frames + never evicted (`engram_store.h`). The design adds a *node-level* keystone + designation (a `keystone` flag / a dedicated layer) so self/values survive + every plasticity sweep. +- **Everything else is plastic**: priors refine (§4), edges re-weight (`hebb`), + neighborhoods re-reify (`engram_geo_reify_store` supersedes with provenance), + salience flows. +- **Metastability is enforced by the loop, not by freezing**: the correspondence + update rate (§4.3) is bounded — small constant steps — so the geometry + *drifts* but does not *dissolve*, and keystones anchor the drift. Reification's + supersession-with-residue already gives non-destructive change (old records + tombstoned, not erased) — the model for "plastic but not amnesiac." + +**What this requires building:** a node-level keystone flag/layer + a rule that +the correspondence-loop never writes `warp` to keystone priors, only reads them. + +--- + +## 7. Rails for the build (binding on the eventual build pass) + +These are stated here so the build agent inherits them: + +- **Offline / secondary.** All build and verification happens out-of-tree, + against a **read-only snapshot copy** of the live engram — never the live + daemon on `:8742`/`:7770`. The live store is a coarse-locked proven binary; + do not perturb it. +- **Snapshot-first.** Copy `~/.neuron/engram/snapshot.json` to scratch; develop + and measure against the copy. +- **Reboot-prove.** Any durable change must survive a cold boot — reify and + keystones must reload from durable records, proven on a prod-clone secondary + before it is considered done (the cold-boot durability bug precedent). +- **Zero-loss.** Supersession-with-residue, never destructive overwrite; the + forward-compat `unknown`-TLV path means new fields never drop old readers' + data. +- **Gated cutover.** Cutover to a new binary only via + `launchctl bootout → settle-poll → bootstrap`, after reboot-proof on the + secondary — never a hot in-place swap. + +--- + +## 8. Staged, verifiable milestones — "to completion" + +Ordered so the **earliest milestone is a real end-to-end slice**: one operator +expressed as {primitive + grounded prior} with the reflexive correspondence-loop +closing on it. Each milestone has a concrete verifiable exit. + +### M1 — One operator, one prior, loop closed (the vertical slice) + +The minimal whole thing. Pick **induction/membership** (its prior — the pooled +rule + extents — already exists transiently as `GeoInduction`, so only +persistence + the loop are new). + +- Build: `Prior` node type (§2.2) for the induction rule; `think()` restricted + to membership = `point_fit` warped by that prior (§1.3); a + `correspondence_beat` (§4) using grade (1) self-consistency only; the prior's + `warp`/`calibration` updated on error. +- **Exit / verify:** on a snapshot copy, over N held predictions, the induction + prior's calibration (Brier) *improves monotonically* across beats versus a + frozen-prior control; the improved prior *reloads across a cold boot* + (reboot-prove); the live daemon is untouched. This proves the whole thesis in + one faculty: frozen operation, learning prior, in-geometry loop. + +### M2 — Priors as stored, addressable, grounded objects + +Generalize M1's prior into the full first-class object. + +- Build: `Prior` records for all seven faculties (warp = axis_gain + bias_dir + + faculty scalars); the prior-warp wrapper around `engram_reason_point_fit`; + deprecate hard-coded constants (`drop_frac`, `assoc_floor`, `ext_floor`) in + favor of prior scalars. +- **Exit:** each of the five C operators runs through its prior with identical + results when the prior is set to today's constants (behavioral parity), then + *diverges beneficially* once the loop refines it. Priors survive reboot. + +### M3 — Grounding as a relation; hold/assert split + +- Build: the `grounded-by` edge (§5.2) with `for_whom`; move + `engram_verify_grounding`'s flag into stored edges; gate **assertion only** + against the honesty floor; leave holding unconditional. +- **Exit:** ungrounded nodes are first-class (held, queryable, no deletion); + the same claim carries different `grounded-by` weights for two observers; an + assertion below floor is refused while the content remains held. Curiosity = + a `vantage_read` that surfaces high-salience / low-grounding regions. + +### M4 — The vantage-read unified (three settings) + +- Build: `vantage_read(anchor, aperture)` unifying descriptor-build + + `engram_geo_reify_lookup` + activation-weighting; self / foreign-field / + aperture settings. +- **Exit:** one code path produces (a) a normal self-read, (b) a + perspective-shifted read from another `for_whom`, (c) a narrowed veil read — + differing only by argument. Reboot-stable. + +### M5 — The gradient is the currency (remove point-collapse from thinking) + +- Build: `GeoGradient` as the return of every faculty; move point-collapse into + a separate expression faculty (sample gradient → surface). `think`'s output + feeds back as the next steering direction (closed-loop flow). +- **Exit:** a chain of `think` calls flows as gradients end-to-end; a point + appears *only* at an explicit expression call. Spiked vs spread gradients are + observable (deduction vs prediction). + +### M6 — Metastability enforced + +- Build: node-level keystone flag/layer for self/values; the correspondence-loop + reads but never writes keystone priors; bounded update rate. +- **Exit:** across a long run of correspondence beats on a snapshot, keystones + are provably unchanged while non-keystone priors drift and improve; the graph + neither freezes (all metrics static) nor dissolves (keystone drift = 0, + identity nodes intact). Reboot-prove the keystone set. + +### M7 — Cutover + +- Build: nothing new — the gated migration. +- **Exit:** reboot-proof on the prod-clone secondary; cutover via + `launchctl bootout → settle-poll → bootstrap`; post-cutover the live engram + shows priors refining in-geometry with zero data loss and keystones intact. + +### Definition of "to completion" + +The architecture is **complete** when: cognition runs as `think` = one frozen +traversal-read primitive + geo-algebra, steered by **stored, learnable, grounded +priors**; the reflexive correspondence-loop refines those priors *in the +geometry* against outcomes (grounding = learning = one loop); the engram holds +ungrounded content as first-class with grounding as a relation and the honesty +floor only on assertion; the vantage-read serves self / foreign-field / aperture +from one op; and a stable keystone core anchors a plastic everything-else — +all reboot-proven and cut over to the live engram without data loss. The named +faculties survive only as *labels on regions of think's steering space*, not as +separate code. + +--- + +## Appendix A — Designed vs. already-built (honest ledger) + +**Already built (EXISTS, cited):** +- The shared primitive `engram_reason_point_fit` and the five operators over it + + geo-algebra (`engram_reason.c`). +- The verifier on `point_fit` (`engram_verify.c`: + `engram_verify_grounding`, `engram_verify_consistency`). +- Centered-frame geometry, combine/subtract/analogy/distance + (`engram_geometry.{c,h}`). +- The reification beat: hub-neighborhood detection → first-class `Neighborhood` + nodes with member edges, nesting, supersession-with-residue, hot-path lookup + (`engram_geo_reify_store`, `engram_geo_reify_nest`, `engram_geo_reify_lookup`). +- The tiered paged store (buffer pool / LRU / WAL / checkpointer / pinning), + the RAM activation graph (base-level learning `access_ts[]`, WM slots, + `working_memory_weight` / `background_activation`), `StoreNode` / `StoreEdge`. +- `GeoMember` already separating relational salience (`centrality`) from + intrinsic `salience`. + +**Designed, NOT built (this doc's deliverables):** +- `GeoGradient` and `think()` as the single entry point (§1, M5). +- `Prior` as a first-class stored, warp-carrying, calibrated node (§2, M1–M2). +- Salience/importance as a *relation* superseding the intrinsic node scalar + (§2.1, M3). +- `vantage_read(anchor, aperture)` unifying the three perspective settings + (§3, M4). +- **The reflexive correspondence-loop / `correspondence_beat`** — the learning + engine, moved from offline Python into the geometry (§4, M1). *The core new + subsystem.* +- `grounded-by` edge + assertion-only honesty floor (§5, M3). +- Node-level keystones + bounded plasticity (§6, M6). + +**Uncertain / to resolve during build:** +- The exact warp parameterization (axis_gain vs full metric) — start minimal + (per-axis gain), measure, widen only if calibration demands it. +- Grade-(1) self-consistency as a sufficient reality signal for M1, versus + needing grade (2)/(3) sooner — decided empirically on the snapshot.