Merge pull request 'nsbx: one-command dev onboarding (branch + worktree + isolated engram)' (#99) from feat/nsbx-dev-env into dev

engram/spec/cognitive-architecture.design.md

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+# 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  = <held claim/prediction node>
+  to_id    = <evidence node / outcome node>
+  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.