core/docs/analysis/phase2-general-comprehension-organ-scope-2026-06-05.md

Phase 2 — The General Comprehension Organ (scope, not build)

Status: SCOPE — no code. This is the scope-before-build for the make-or-break phase of the AGI-candidacy roadmap (AGI-candidacy-autonomous-improvement-roadmap-2026-06-05.md). Phase 1 (MEASURE — the cross-domain capability index) is landed (#575); this document scopes Phase 2 (COMPREHEND) so the first increment can be built TDD without falling into the per-domain-matcher overfit trap that would fake the capability number.

Reviewer note: I am the eyes on the implementation; the architectural decision in §4 is presented with a recommendation for design review. Nothing here is committed beyond this document.

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1. Why this phase is the gate

The roadmap's loop is COMPREHEND → REALIZE → REASON/GROUND/RECALL → RESPOND (assert/estimate/refuse) → PROPOSE → HITL → ACCUMULATE → measurably more capable → repeat. Every later phase consumes the output of COMPREHEND. If comprehension is narrow, the whole organism is narrow no matter how good the reasoners are. This is exactly what the GSM8K serving numbers were telling us: ~92% refused is not a reasoning failure, it is a comprehension failure — the engine could not turn the prose into a structure the reasoner could touch.

The one-line gap: CORE can reason over structured input in several domains, and it can articulate a response from field resonance, but it has no organ that turns arbitrary natural language into reasoning-ready structure. The reasoning side and the articulation side never connect.

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2. The honest substrate map

2a. The articulation side (chat) — shallow comprehension, aimed at responding

Module	What it actually does
generate/proposition.py :: PropositionGraph	"prompt and field form a relation blade; a frame is selected by exact CGA inner product against that relation; vocabulary points instantiate the frame slots." Frame-fill from field resonance.
generate/graph_planner.py	PropositionGraph → ArticulationTarget (topological walk → ordered articulation steps). This is the output path.
generate/intent.py, generate/realizer.py	intent classification + deterministic surface realization.

This path is general over input but shallow: it selects a response frame by field resonance and fills slots from vocabulary. It is built to respond, not to understand-for-reasoning. Its PropositionGraph is not a refusal-first, reasoning-ready meaning structure — and it shares a name with the logic-side proposition representation (§2b), which we must not conflate.

2b. The reasoning side — strong reasoners, but they consume structured input

Lane / module	Input it consumes	Meaning structure
evals/deductive_logic (ROBDD)	already-formal facts + rules + query (JSON)	proof_chain proposition repr (ADR-0201/0202), canonicalizer, proof-graph-builder (ADR-0204), modus-ponens (ADR-0205)
evals/relational_metric (generate/relational_field_reader)	narrow templated text	tiny quantitative grammar: fact / more_than / fewer_than / sum_of
evals/dimensional	structured cases	unit/dimension analysis
GSM8K (generate/derivation, generate/binding_graph)	math word problems → MathProblemGraph	binding-graph (ADR-0132): the canonized NL↔reasoning interlingua

2c. The binding-graph interlingua — neutral, but arithmetic-shaped

generate/binding_graph/model.py (ADR-0132) is described as "the typed compiler boundary between natural language and symbolic reasoning," and INV-26 keeps it neutral (it imports no engine/benchmark/domain code). That discipline is exactly right. But:

• Its closed vocabularies are arithmetic: SEMANTIC_ROLES = {entity, quantity, rate, duration, count, total, difference, ratio, unknown}; QUESTION_FORMS = {count, rate, total, difference, ratio, identity}.
• Its only relational node is BoundEquation (lhs := rhs, quantitative) and a single-symbol BoundConstraint (predicate string). There is no general n-ary relation / predicate node, no class-membership, no quantifier.
• Its only producer is generate/binding_graph/adapter.py, which translates an already-structured MathProblemGraph — not raw NL — into the graph.

So the "interlingua" exists and is well-disciplined, but today it is the arithmetic word-problem interlingua, fed by the math reader, never by a general parser.

2d. The precise gap (the "missing middle")

            arbitrary NL prose
                   │
                   ▼
        ┌──────────────────────┐
        │  ???  GENERAL         │   ← Phase 2: this organ does not exist
        │  COMPREHENSION ORGAN  │
        └──────────────────────┘
                   │  (general meaning structure)
        ┌──────────┴───────────┐
        ▼          ▼           ▼
   binding-graph  proof_chain  relational/dimensional
   (quantities)   propositions  grammars
        │          │           │
        ▼          ▼           ▼
        the reasoners (already built, already independent-gold)

The reasoners are built. The yardstick is built. The articulation path is built. The general parser from prose into the reasoners' world is the unbuilt make-or-break.

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3. Definitions made precise (carrying the corrected epistemic frame)

These align with the roadmap's epistemic foundation (honesty designed, estimation learned) and the user's corrections.

• Comprehend = turn arbitrary input into a structured meaning keyed on general structure (syntax + grounding), not on domain word-lists. Output is a general meaning structure (§4), or a refusal — never a fabricated parse.
• Realize = integrate that meaning into the held self with an EpistemicStatus (told / coherent-with-evidence / verified). "Being told" is first-class: most knowledge arrives as told facts the engine realizes and earns the why/how over time. Realization is what makes intake recallable.
• Intake is first-class (NOT "no ingestion"): take in inputs, comprehend, realize as structured grounded memory it can recall. The ban is on bulk indiscriminate absorption into a database, not on ingesting knowledge.
• Parse-or-refuse floor = a statement comprehends iff its structure maps to the meaning structure via general rules and its content grounds (known lemmas, or honest typed unknowns). Anything else → refuse. This is how wrong=0 holds at the comprehension layer: the engine never invents a reading it cannot ground. (Note: wrong=0 here is the comprehension gear of the roadmap's "honesty designed in," not a universal law.)

Non-goal restated: comprehension does not include a guess organ. If the structure or grounding is absent, it refuses. Estimation, where it ever applies, is a learned, ratified competence built later (Phase 6), never designed into the parser.

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4. The architectural decision: what does comprehension emit?

This is the load-bearing decision and the reason to scope before building. Meaning-structure today is spread across three substrates, none general:

1. binding-graph (quantities/equations) — math-shaped, INV-26 neutral.
2. proof_chain propositions (ADR-0201/0202) — logic-shaped.
3. PropositionGraph (generate/proposition.py) — field-resonance articulation, wrong tool, name-collision hazard.

To comprehend general declarative/interrogative prose across domains we need to represent at least: entities (have), n-ary named relations / predicates (missing), class-membership / subsumption (missing), attribution / properties (missing), quantified statements (missing), quantities & equations (have, in binding-graph), logical connectives (have, in proof_chain).

The options

• Option A — extend the binding-graph's closed vocab with general roles + an n-ary relation node + class/quantifier nodes. Pro: one canonized neutral meeting point; reuse refusal/provenance/canonical discipline. Con: bloats a structure designed for quantities into a god-structure; the closed-vocab ADRs explicitly say "extend deliberately in a future ADR"; couples logic/relations into the math interlingua.

• Option B (recommended) — a general meaning structure that the existing structures project into. Comprehension emits a general claim/meaning graph (working name MeaningGraph) sharing the binding-graph's discipline (frozen/slots, SourceSpanLink provenance, refusal-first, to_canonical_string, INV-neutral). A thin projector maps it to whichever reasoner's input shape (binding-graph for arithmetic, proof_chain propositions for logic, the relational grammar for relational_metric). The binding-graph and proof_chain propositions become downstream projections, not rivals. Pro: doesn't bloat math; keeps INV-26 (a neutral meeting point); reuses every existing reasoner unchanged; the general organ has one general target. Con: a new substrate to design — mitigated by building it minimally, one class at a time, per the defer-substrate-vocab discipline.

• Option C — reuse PropositionGraph. Rejected: it is field-resonance frame-fill for articulation, not refusal-first reasoning-ready structure.

Recommendation

Option B, built minimally and use-case-driven. Do not pre-lock a full general vocabulary (that violates the defer-substrate-vocab discipline). Instead:

Introduce MeaningGraph carrying exactly the node kinds the first increment needs (entities + one general relation kind), with the binding-graph's refusal/provenance/canonical discipline and an INV firewall keeping it neutral. Every later class (subsumption, quantifier, attribution) is a deliberate, use-case-driven vocabulary extension with its own cross-domain proof.

The field as a standing hand (CL(4,1) inner product / incidence) is a candidate for relation-consistency checks (transitivity, contradiction) — note it, do not depend on it. The field-reasoner wedge found metric reading-independence unproven and field-as-reasoner deferred; comprehension must stand without it, and may later borrow it where it is geometrically honest.

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5. The general reader architecture (how the organ works)

NL statement
   │
   ▼  (1) STRUCTURAL PARSE — keyed on SYNTAX, not domain words
        en_core_syntax_v1 (definitional layer, reviewed) supplies the
        general structural skeleton: subject–predicate–object, modifiers,
        quantifiers, connectives. Domain-agnostic.
   │
   ▼  (2) GROUNDING-FILL — content fills the skeleton
        entities/relations resolved against packs + vault (known lemmas) or
        marked as honest typed unknowns. Content is NEVER hard-coded per domain.
   │
   ▼  (3) PARSE-OR-REFUSE GATE
        emit MeaningGraph iff structure maps via general rules AND content
        grounds; otherwise REFUSE (typed, audited). No fabricated reading.
   │
   ▼  MeaningGraph  ──projector──▶  reasoner input (binding-graph / proposition / grammar)

The decisive design commitment: step (1) keys on structure (syntax), step (2) on grounding (packs/vault). A class of statement comprehends because of its grammar, which is domain-agnostic; the content that fills it varies by domain. This is what makes the organ general rather than a pile of recognizers — and it is exactly the property the overfit trap violates.

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6. Cross-domain proof obligation & overfit-trap guardrails

The overfit trap: build a per-domain matcher that lifts one lane's coverage and fakes the capability number. The Phase-1 yardstick was built precisely to make this visible (geomean → 0 if any domain stays at zero), but the discipline must be enforced at the comprehension layer too:

1. Every comprehension class is proven on ≥3 distinct domains with the same grammar, different content (e.g. binary relation "X R Y" over kinship, biology, geometry). Works in only one domain ⇒ it is a matcher ⇒ rejected.
2. The Phase-1 capability index is the acceptance gate. A comprehension increment is accepted only if breadth rises (or coverage rises across multiple domains' geomean), with wrong_total == 0. A one-domain bump that leaves the geomean flat is, by construction, not progress.
3. Schema-defined proof obligation (CLAUDE.md rule). The parse-or-refuse gate is load-bearing only if a test meaningfully fails when a fabricated reading is admitted. Each class ships with a refusal test that fails if the gate is loosened to admit an ungrounded parse.
4. INV firewall for MeaningGraph neutrality (sibling of INV-26): the structure imports no engine/benchmark/domain code, so two independent decodings can meet there honestly.
5. No silent caps. If an increment bounds coverage (clause types handled, grounding sources), it is logged — silent truncation reads as "general" when it is not.

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7. Increment decomposition (build order)

Each increment is a small, load-bearing PR with the yardstick as its gate.

2a — MeaningGraph substrate + the first general class (binary relations), end-to-end

• MeaningGraph data model (frozen/slots, SourceSpanLink provenance, refusal-first, to_canonical_string, INV firewall) carrying entities + one general n-ary relation node.
• The structural reader (step 1–3) for binary-relation declaratives ("X R Y"), keyed on syntax via en_core_syntax_v1, grounded against packs/vault, parse-or-refuse.
• A projector MeaningGraph → relational grammar so the existing relational_metric reasoner consumes it unchanged (proves the projection pattern on a real reasoner).
• Acceptance: binary-relation comprehension proven on ≥3 distinct domains on the capability index; wrong_total == 0; refusal tests bite; index digest recorded as the new baseline.

2b — widen relation classes (use-case-driven)

• Add class-membership / subsumption ("a raven is a bird", "all ravens are birds") with a projector into the proof_chain proposition repr so the deductive_logic reasoner consumes comprehended prose (closing the formal-input gap for the largest lane).
• Each class: ≥3-domain proof, wrong=0, refusal test, geomean must move.

2c — attribution / quantity bridges + loop-until-coverage

• Attribution ("the ball is red"), and the quantity bridge MeaningGraph → binding-graph so comprehended math prose reaches the GSM8K reasoner without the MathProblemGraph shortcut.
• Loop one class at a time until coverage stops rising (loop-until-dry), each cross-domain-proven.

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8. Risks, invariants, non-goals

Invariants preserved: versor_condition < 1e-6 (untouched — comprehension is symbolic/structural, no field repair); exact CGA recall (no approximate match introduced); wrong=0 as the comprehension gear (parse-or-refuse); INV-26-style neutrality extended to MeaningGraph; reviewed learning stays HITL (comprehension feeds REALIZE/PROPOSE, never self-ratifies).

Risks:

• Vocabulary creep → mitigated by use-case-driven extension, one class at a time, each with its own ADR and proof (defer-substrate-vocab discipline).
• MeaningGraph becoming a third orphan structure → mitigated by the projector pattern: it must feed an existing reasoner from increment 2a, or it is not built.
• Syntax-pack depth unknown → en_core_syntax_v1 is a lexicon/gloss pack, not a parser. 2a must establish how much general structural parsing it actually supports; if insufficient, the first sub-task is a minimal, collision-audited structural-parse capability (NOT a bulk grammar import — recall the #503 syntax revert).
• Name collision PropositionGraph (articulation) vs proof_chain propositions vs MeaningGraph → documented here; keep them distinct.

Non-goals (this phase): a guess/estimate organ (Phase 6, learned+ratified); field-as-reasoner (deferred research); bulk corpus ingestion; touching the serving GSM8K metric (this is additive — comprehension feeds reasoners, it does not change their gold).

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9. Open questions for design review

1. Option B vs A — do we accept a sibling MeaningGraph that existing structures project into, or extend the binding-graph in place? (Recommend B.)
2. First class — binary relations as 2a's first class, projecting into relational_metric? (Recommend yes: smallest general structure beyond entities, with an existing reasoner + reader to lift from.)
3. Syntax-parse depth — is en_core_syntax_v1 enough for general structural parsing of declaratives, or does 2a need a minimal structural-parse capability first? (Needs a spike inside 2a before the reader is built.)
4. Field standing-hand — reserve CL(4,1) incidence as the relation-consistency checker for a later increment, or leave it out entirely until the wedge resolves? (Recommend: note, don't depend.)