el/engram/spec/cognitive-architecture.design.md

# 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.