docs(session): epistemic question articulation — first skill of contemplation
Records the design reached in discussion: the first real skill of contemplation is typed, deterministic, failure-family-driven question generation. A question is a typed request for missing state, not a conversational habit. Proposes core/epistemic_questions/ organ (EpistemicQuestion, MissingSlot, AnswerBinding), the QUESTION_NEEDED terminal distinct from PROPOSAL_EMITTED, and the Q1/Q2 PR ladder. Traces the connection to the ServabilityBlade clarify mode and the minimal-sufficient-question discipline.
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# Session 2026-06-08 — Epistemic question articulation: the first skill of contemplation
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**Status:** discussion / design note — **session document, not an ADR yet.** No
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code shipped. This records a design reached in conversation immediately after the
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servability-blade discussion
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([`2026-06-08-practice-attempts-and-servability-blade`](./2026-06-08-practice-attempts-and-servability-blade.md)),
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and deliberately preserves the train of thought so the idea cannot be silently
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collapsed into a generic "ask clarifying questions" feature. **Headline:** The
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first real skill of contemplation is not thinking harder, not adding more steps,
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not producing reflection text. It is: **recognise that the current state is
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underdetermined, articulate the missing information, and ask the question whose
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answer would collapse the uncertainty.** For CORE this becomes a typed,
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deterministic, failure-family-driven organ — `core/epistemic_questions/` — that
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produces a typed `EpistemicQuestion` artifact, routes it to the surface as
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`QUESTION_NEEDED`, and reserves an `AnswerBinding` slot so the answer drives the
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solve, not a guess.
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`No serving path, algebra, versor, recall, or gate touched. This is the research`
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`trail — design and reasoning preserved for the open-source record.`
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> **Why this doc exists.** CORE is open-source research in an uncharted corner
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> of AI. The *reasoning behind* a design is as load-bearing as the design itself.
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> This captures not just *what* to build but *why the idea matters*, *how the
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> conversation arrived at it*, and *what the sharp edges are* — so future agents,
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> the operator, and outside researchers can follow the actual train of thought
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> rather than just read a finished spec.
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---
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## TL;DR — the core insight in four lines
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```text
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A question is not language. A question is a typed request for missing state.
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The first skill of contemplation is: ask the smallest question that unblocks the proof.
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question ≠ proposal (missing information in the world vs missing capability in CORE)
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Question generation must be deterministic, slot-directed, template-driven — never vibes.
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```
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---
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## 1. How we got here — the chain of reasoning
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### 1.1 The provocation
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This session grew directly from the servability-blade discussion. Once we
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established that `wrong=0` means *no false presentation of epistemic status* —
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not silence unless omniscient — the next question was immediate: if the engine
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**cannot** produce a verified answer and **should not** guess, what *should* it
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do with that underdetermined state?
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The first answer is the blade's `REFUSE_UNSUPPORTED`. But that throws the problem
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away. The better answer, for problems that are merely *underdetermined*, is to
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ask the one question that would make the problem solvable.
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### 1.2 The reframing of contemplation
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Generic AI systems treat "asking a question" as a conversational habit — a
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politeness or a hedge. The reframing arrived in this conversation:
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> A question is not merely language. A question is a typed request for missing state.
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That single sentence reorients the whole design. If a question is a *typed
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request*, then:
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- it has a *kind* (what category of missing information),
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- it has a *target slot* (exactly where the answer will bind),
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- it has an *expected answer type* (what form the answer must take),
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- it has a *blocking reason* (why the current attempt cannot proceed),
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- it has a *resolution target* (which constraints it unblocks).
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And therefore it must be *generated from typed failure evidence*, not from free
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text improvisation.
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### 1.3 The minimal sufficient question
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The discipline that followed from that reframing:
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> Ask the smallest question that would unblock the current proof.
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This is the criterion that distinguishes *epistemic query generation* from
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chatbot clarification-seeking. A valid question must satisfy all of:
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1. It targets exactly one missing slot.
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2. The expected answer type is known.
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3. If answered, the system knows exactly where to bind the value.
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4. It does not ask for information the system could derive.
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5. It does not ask for unsupported capabilities.
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6. It does not ask multiple things at once.
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Violation of any criterion is a quality failure in the question organ, not a
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"good enough" clarification.
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### 1.4 The sharp distinction: question vs proposal
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This is the most critical architectural boundary. Two different contemplation
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terminals are possible:
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| Terminal | Meaning | Example |
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|---|---|---|
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| `QUESTION_NEEDED` | The problem has enough structure to be solvable, but a specific value / referent / choice is missing from the *input* | Anna + Ben painting, only Anna's rate given → "What is Ben's rate in rooms per hour?" |
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| `PROPOSAL_EMITTED` | The problem is fully specified, but CORE lacks the *capability* to solve it | Anna finishes in 3 h, Ben in 6 h → reciprocal work-rate, CORE has no solver for that yet |
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These must never be conflated:
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- **Asking a question** says: *the problem is knowable; I need one more datum from
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the world.*
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- **Emitting a proposal** says: *the problem is knowable; I need one more organ
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from engineering.*
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Confusing them would let the engine ask users for things the users already told
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it, or propose capability additions for problems it could solve if it just asked.
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The blocking reason from the failure family is the gate: if the failure family
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signals *missing input*, route to `QUESTION_NEEDED`; if it signals *missing
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solver*, route to `PROPOSAL_EMITTED`.
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---
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## 2. The organ — `core/epistemic_questions/`
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Located in `core/` (not `generate/`) because question articulation will
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eventually apply across math, language, modality, planning, memory, UI, and the
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lived-loop — not only comprehension.
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```text
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core/epistemic_questions/
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model.py — typed data model (EpistemicQuestion, MissingSlot, AnswerBinding)
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derive.py — failure-family → missing-slot → EpistemicQuestion
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render.py — deterministic template rendering of the natural-language prompt
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rank.py — (Q2+) rank multiple candidates, pick minimal sufficient question
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answer_bind.py — (Q2) typed slot binding after user answer
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```
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### 2.1 Data model
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```python
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QuestionKind = Literal[
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"missing_value",
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"missing_unit",
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"ambiguous_referent",
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"ambiguous_operation",
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"missing_time_interval",
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"missing_category",
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"missing_rate",
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"missing_query_target",
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"contradiction_resolution",
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"scope_boundary",
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]
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@dataclass(frozen=True)
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class MissingSlot:
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name: str # the slot identifier (e.g. "rate_b")
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role: str # its semantic role (e.g. "second_combined_rate")
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entity: str | None # the referent entity (e.g. "Ben")
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unit: str | None # expected unit (e.g. "rooms/hour")
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constraints: tuple[str, ...] # what this slot must satisfy
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@dataclass(frozen=True)
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class EpistemicQuestion:
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id: str
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source_attempt_id: str
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question_kind: QuestionKind
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missing_slot: MissingSlot
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prompt: str # deterministic rendered text; never improvised
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expected_answer_type: AnswerType
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expected_unit: str | None
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owner_organ: str # which reader/solver will consume the answer
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blocking_reason: str # the failure-family key
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resolves: tuple[str, ...] # slot names this answer will unlock
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priority: int # lower = ask first
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@dataclass(frozen=True)
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class AnswerBinding: # reserved in Q1; implemented in Q2
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question_id: str
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target_organ: str
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target_slot: str
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parser: str # which typed parser handles the raw answer
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unit: str | None
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```
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All dataclasses are **frozen** (deterministic, hashable, replayable) — consistent
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with the determinism doctrine throughout CORE.
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### 2.2 Example artifact
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```json
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{
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"question_kind": "missing_rate",
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"missing_slot": {
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"name": "rate_b",
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"role": "second_combined_rate",
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"entity": "Ben",
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"unit": "rooms/hour",
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"constraints": ["positive_integer"]
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},
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"prompt": "What is Ben's painting rate in rooms per hour?",
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"expected_answer_type": "positive_integer_rate",
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"owner_organ": "combined_rate_comprehension",
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"blocking_reason": "cmb_missing_second_rate",
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"resolves": ["rate_b"],
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"priority": 100
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}
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```
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The `prompt` is not generated by an LLM. It is rendered by `render.py` from a
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deterministic template keyed on `blocking_reason`, with required fields drawn
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from the `MissingSlot`. If required fields are absent, `render.py` emits
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`question_unrenderable` — **it does not hallucinate**.
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---
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## 3. Derivation — failure family to question
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Questions are not generated by the reader directly. The flow is:
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```text
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reader / router / contemplation
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→ ComprehensionAttempt
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→ FailureFamily
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→ MissingSlot
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→ EpistemicQuestion
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```
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This means `derive.py` consumes *already-typed failure state*. It does not
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re-read the problem text. That keeps question generation deterministic and
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failure-family-scoped — it cannot ask about things it has not already identified
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as missing through the typed comprehension path.
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### 3.1 The ask/don't-ask decision is per family
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Not every failure family should produce a question. The decision table for the
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initial set (Q1):
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| Failure family | Ask? | Why |
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|---|---|---|
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| `cmb_missing_second_rate` | ✅ | Input incomplete; one rate datum is missing |
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| `cmb_combine_mode_ambiguous` | ✅ | Input ambiguous; operation choice is underspecified |
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| `r1_ambiguous_referent` | ✅ | Referent resolution blocked by pronoun without antecedent |
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| `r2_missing_weighted_total` | ✅ | A required aggregate is not given |
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| `r2_missing_total_count` | ✅ | A required count is not given |
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| `r3_missing_rate` | ✅ | Rate datum absent from problem text |
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| `non_integer_solution` | ❌ | Mathematical exactness boundary; asking changes nothing |
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| `non_positive_net_rate` | ❌ | Mathematical boundary; not a missing-input problem |
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| `rate_unit_mismatch` | ❌ (for now) | Ask deferred until dimension registry exists |
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| `answer_key_contradiction` | ❌ | Report contradiction; do not ask unless source-authority lane exists |
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| `cmb_reciprocal_work_rate_deferred` | ❌ → `PROPOSAL_EMITTED` | Capability gap, not input gap |
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The ask/don't-ask decision is **a property of the failure family**, not a
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runtime heuristic. It is a closed, tested mapping.
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### 3.2 Template table (Q1 scope)
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```text
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cmb_missing_second_rate:
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required: [missing_agent, rate_unit]
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template: "What is {missing_agent}'s rate in {numerator} per {denominator}?"
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cmb_combine_mode_ambiguous:
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required: [rate_a_entity, rate_b_entity]
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template: "Are {rate_a_entity} and {rate_b_entity} working together,
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opposing each other, or separately?"
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r1_ambiguous_referent:
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required: [pronoun, candidates]
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template: "Who or what does '{pronoun}' refer to: {candidates}?"
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r2_missing_weighted_total:
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required: [unit]
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template: "What is the total number of {unit}?"
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r2_missing_total_count:
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required: [entity_class]
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template: "How many {entity_class} are there in total?"
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r3_missing_rate:
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required: [entity, rate_unit]
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template: "What is {entity}'s rate in {rate_unit}?"
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```
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Templates are data, not code. They are validated at load time. A template with a
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missing required field at render time produces `question_unrenderable` — a typed
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failure, not a crash or improvisation.
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---
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## 4. The new contemplation terminal: `QUESTION_NEEDED`
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The contemplation pass manager gains a new terminal state:
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```python
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terminal: Literal[
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...,
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"PROPOSAL_EMITTED", # existing: missing capability
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"QUESTION_NEEDED", # new: missing input datum
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"CONTRADICTION_DETECTED", # (planned: contradiction report)
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]
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```
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The epistemic state / resolution action split is:
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```text
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epistemic_state: UNDETERMINED
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resolution_action: ASK_QUESTION
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terminal: QUESTION_NEEDED
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artifact: EpistemicQuestion(...)
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```
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`QUESTION_NEEDED` is not a subtype of `PROPOSAL_EMITTED`. They are sibling
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terminals. The distinction is real and must be preserved in every downstream
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consumer (the servability blade, the surface realizer, the teaching corridor).
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The `ContemplationResult` (or the relevant terminal object) carries the question
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artifact when the terminal is `QUESTION_NEEDED`, just as it carries the proposal
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artifact when the terminal is `PROPOSAL_EMITTED`.
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---
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## 5. How question-asking becomes active problem solving
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The full loop, once Q2 (answer binding) exists:
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```text
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1. Attempt solution
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2. Detect blocked state → FailureFamily
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3. Identify missing slot → MissingSlot
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4. Generate minimal sufficient question → EpistemicQuestion
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5. Surface question to user (via ServabilityBlade mode = "clarify")
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6. User answers
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7. AnswerBinding: parse typed answer → bind to target slot
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8. Re-run owner organ with augmented problem
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9. Resume solving
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```
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Steps 1–5 are Q1. Steps 6–9 are Q2. **Q1 must design the artifacts for Q2** —
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specifically: `expected_answer_type`, `owner_organ`, and `target_slot` must be
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present in the Q1 `EpistemicQuestion` even before binding is implemented, so Q2
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does not require an artifact-schema migration.
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The same loop generalises past math:
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| Domain | Example failure | Minimal question |
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|---|---|---|
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| Math (combined rate) | `cmb_missing_second_rate` | "What is Ben's rate in rooms per hour?" |
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| Business problem-solving | `missing_objective_metric` | "What outcome are you trying to improve first: revenue, profit, retention, or workload?" |
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| Debugging | `missing_observed_failure` | "What error message or failing behavior do you see?" |
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| Planning | `missing_target_goal` | "What is the target event or outcome?" |
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| Language referent | `r1_ambiguous_referent` | "Who does 'they' refer to?" |
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The organ is the same in all cases. Only the failure families and templates
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differ. This is why `core/epistemic_questions/` is not inside `generate/` — it
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is general-intelligence infrastructure.
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---
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## 6. Why question generation must be deterministic
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The whole CORE architecture rests on determinism and replayability
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(CLAUDE.md: "prefer inspectable state, provenance, and deterministic replay over
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impressive-looking but ungrounded outputs"). The same obligation extends to
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question generation:
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- **No freeform improvisation.** The renderer never calls an LLM to generate the
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question text. It fills a template from typed slot data.
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- **No hallucination.** If a required template field is absent, `render.py`
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returns `question_unrenderable` — a typed, auditable failure — rather than
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substituting a guess.
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- **No multi-question improvisation.** Q1 emits at most one question per
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contemplation pass. Ranking (`rank.py`) is Q2+. Dialogue management is later
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still.
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- **Replayable.** Same `ComprehensionAttempt` + same failure family → same
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`EpistemicQuestion`, always.
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This is how *asking a question* remains a cognitive operation rather than a
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conversational habit.
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---
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## 7. Proposed Q1 PR ladder
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### Q1-a — Question artifact model
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`core/epistemic_questions/model.py` + `tests/test_epistemic_question_model.py`
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No contemplation integration. Tests cover: deterministic serialization, stable
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id/hash, no empty prompt, `expected_answer_type` required, `owner_organ`
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required.
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### Q1-b — Failure-family → missing-slot derivation
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`core/epistemic_questions/derive.py` + `tests/test_epistemic_question_derivation.py`
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Maps the initial 6–8 failure families (ask/don't-ask table in §3.1). Tests
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include one case per mapped family proving the right terminal is selected, and
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one case per non-asking family proving no question is emitted.
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### Q1-c — Deterministic template renderer
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`core/epistemic_questions/render.py` + `tests/test_epistemic_question_rendering.py`
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Tests: correct prompt for each template, `question_unrenderable` when required
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fields absent, no improvisation possible (no LLM call path).
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### Q1-d — Contemplation terminal
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`generate/comprehension_contemplation/pass_manager.py` (or equivalent)
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Add `QUESTION_NEEDED` as a terminal. Wire two or three routed examples
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(`cmb_missing_second_rate`, `cmb_combine_mode_ambiguous`,
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`r1_ambiguous_referent`). Tests: terminal state correct, artifact present, no
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impact on existing `PROPOSAL_EMITTED` and `REFUSED` paths.
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### Q1-e — Analysis doc
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`docs/analysis/epistemic-question-articulation-v1-2026-06-08.md`
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Non-claims explicitly stated:
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- No freeform clarification.
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- No multi-question dialogue manager.
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- No answer binding (Q2).
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- No serving change.
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- No self-modification.
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---
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## 8. Q1 acceptance criteria
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After Q1, CORE must be able to demonstrate:
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```text
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cmb_missing_second_rate
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→ QUESTION_NEEDED
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→ "What is Ben's painting rate in rooms per hour?"
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→ no proposal emitted, no answer guessed
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cmb_combine_mode_ambiguous
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→ QUESTION_NEEDED
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→ "Are the two rates working together, opposing each other, or separately?"
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r1_ambiguous_referent
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→ QUESTION_NEEDED
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→ "Who does 'they' refer to?"
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r2_missing_weighted_total
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→ QUESTION_NEEDED
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→ "What is the total number of [unit]?"
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|
||||
cmb_reciprocal_work_rate_deferred
|
||||
→ PROPOSAL_EMITTED
|
||||
→ no question
|
||||
|
||||
answer_key_contradiction
|
||||
→ CONTRADICTION_DETECTED (or existing refusal)
|
||||
→ no question
|
||||
```
|
||||
|
||||
And across all cases:
|
||||
|
||||
```text
|
||||
R1 / R2 / R3 / CMB serving: unchanged
|
||||
wrong=0: intact
|
||||
proposal-only teaching: unchanged
|
||||
GSM8K lane SHAs: pinned and unchanged
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 9. How this connects to the servability blade
|
||||
|
||||
The `EpistemicQuestion` artifact is the bridge between the attempt layer (PRA)
|
||||
and the `ServabilityBlade` from the previous discussion:
|
||||
|
||||
```text
|
||||
ProblemAttemptSession
|
||||
→ terminal = QUESTION_NEEDED
|
||||
→ EpistemicQuestion artifact
|
||||
│
|
||||
▼
|
||||
ServabilityBlade
|
||||
→ mode = "clarify"
|
||||
→ required_disclosures = [the question prompt]
|
||||
→ servable_claims = [] (no answer claimed; none exists yet)
|
||||
│
|
||||
▼
|
||||
surface: "What is Ben's painting rate in rooms per hour?"
|
||||
```
|
||||
|
||||
The blade's `"clarify"` mode was designed exactly for this case. The question
|
||||
organ is what *produces* the artifact the blade routes. Together they close the
|
||||
loop: **underdetermined → type the gap → ask the minimal question → blade routes
|
||||
it honestly → solve on reply.**
|
||||
|
||||
---
|
||||
|
||||
## 10. What comes after Q1
|
||||
|
||||
**Q2 — Answer binding.** The user replies; a typed parser extracts the value;
|
||||
`AnswerBinding` maps it to the `target_slot` in the `owner_organ`; the organ
|
||||
reruns with the augmented problem. For the Ben painting example:
|
||||
|
||||
```text
|
||||
Question: "What is Ben's painting rate in rooms per hour?"
|
||||
Answer: "2"
|
||||
Binding: rate_b = 2 rooms/hour (typed PositiveIntegerRate)
|
||||
Rerun: effective_rate = 3 + 2 = 5 rooms/hour
|
||||
quantity = 5 × 4 hours = 20 rooms
|
||||
```
|
||||
|
||||
That is when question-asking becomes active problem solving, not just surface
|
||||
decoration.
|
||||
|
||||
**Q3+ — Multi-question ranking.** When multiple missing slots exist,
|
||||
`rank.py` selects the minimal sufficient question (prefer one-slot numeric over
|
||||
broad; prefer questions that unblock an existing solver; prefer questions with
|
||||
known unit/type; do not ask when contradiction should be reported instead).
|
||||
|
||||
**Longer term.** The same organ applies across domains beyond math — business
|
||||
problem-solving, debugging, planning, language referent resolution — because
|
||||
`question_kind` and `MissingSlot` are not math-specific. The failure-family
|
||||
table grows; the template table grows; the core machinery is the same.
|
||||
|
||||
---
|
||||
|
||||
## Bottom line
|
||||
|
||||
The first real skill of contemplation is:
|
||||
|
||||
```text
|
||||
I cannot solve this because slot X is missing.
|
||||
The minimal question that unblocks me is Y.
|
||||
I expect an answer of type Z.
|
||||
If answered, I know exactly where to bind it.
|
||||
```
|
||||
|
||||
That is a **typed, deterministic, failure-family-driven operation** — not
|
||||
thinking, not explaining, not guessing. Not a chatbot habit. A cognitive act with
|
||||
provenance, a binding target, and a defined resolution path.
|
||||
|
||||
The proposal is `core/epistemic_questions/` (Q1: model + derive + render +
|
||||
terminal integration), followed by `answer_bind.py` (Q2). The full loop — attempt
|
||||
→ detect blocked state → articulate the gap → ask the right question → bind the
|
||||
answer → solve — is the simplest real reasoning loop CORE can run.
|
||||
Loading…
Reference in a new issue