P2D REASONING ENGINE / PIPELINE V0.4

From an open question to an inspectable answer

The system turns a broad question into testable claims, researches both sides, constructs one typed reasoning graph, and derives the answer and visual from that same artifact.

Model judgment External research Deterministic code Human review point
01
Independent discourse mapping

Map questions and hypotheses

Three web-enabled models independently map current debates. Each keeps questions separate from candidate answers and links every hypothesis to a question.

InputInitial query, as-of date, live discourse
GPT-5.6 LunaGemini 2.5 ProDeepSeek V3.2
Output Independent discourse maps
02
Question-only synthesis

Structure direct and meta questions

Canonicalize questions without seeing hypotheses. Distinguish object questions from higher-order questions about whether an effect matters, then map their dependencies.

ObjectBridgeMetaTyped inquiry graph
InputQuestions, definitions, minority frames—no hypotheses
GPT-5.6 Luna
Output Answer contract + inquiry graph
03
Blind hypothesis expansion

Find missing candidate answers

Independent models receive the questions and neutral context, but not the discourse hypotheses or web search. They add nulls, rival mechanisms, boundary cases, and proxy critiques.

Alternative set AAlternative set B
InputContract, definitions, neutral context, questions
DeepSeek V3.2Gemini 2.5 Pro
Output Blind alternatives + question audits
04
Deterministic question selection

Select questions—not winners

A fixed policy ranks questions by likely importance, significance, leverage, answerability, novelty, and diversity. Deferred and specialized questions remain visible.

ImportanceLeverageAnswerabilityDiversity
InputCanonical question map
Python ranking policy
Output Active + deferred question portfolio
05
Hypothesis completion

Complete competing hypothesis sets

Combine discourse-grounded and blind alternatives for each active question. Preserve provenance and disagreement; do not truth-rank or select a winner.

InputSelected questions, discourse hypotheses, blind expansions
GPT-5.6 Luna
Output 3–6 hypotheses per active question
06
Claim and argument construction

Decompose into claims

Only after the question–hypothesis sets are stable does the system create atomic, contestable research targets and candidate inference routes.

Claimargument →Claim
InputActive questions and completed hypotheses
GPT-5.6 Luna
Output Research-target graph
07
External research

Research both directions

Each load-bearing claim receives separate support and challenge searches. Grounds preserve source, scope, extraction, limitations, and origin path.

Seek supportSeek opposition
InputOne scoped claim and its operationalization
DeepSeek V3.2 · supportGemini 2.5 Pro · challenge
Output Grounds + coverage records
08
Adversarial assessment

Assess each claim

A verifier and falsifier independently build the strongest cases. A comparator checks scope, warrants, undercutters, and source dependence.

VerifierFalsifierComparatorDependence-aware aggregation
InputExact claim and extracted grounds
GPT-5.6 LunaDeepSeek V3.2Gemini 2.5 Pro
Output Signed assessment records
09
Model construction + compiler

Compile the reasoning artifact

The completed claim graph, arguments, claim-backed defeaters, grounds, assessments, provenance, and terminal claims become one durable object.

InputPortfolio, skeleton, research, assessments, grounds
GPT-5.6 Luna · constructDeepSeek V3.2 · reviewPython compiler
Output · source of truth Native reasoning graph
10
Deterministic validation

Run checks and sensitivity

Code rejects malformed topology, clusters dependent sources, evaluates defeasible routes, and tests strict formal candidates with Z3 and a truth table.

SchemaIdentityDependenceDefeatersZ3 when suitable
InputCompiled reasoning artifact
Python policy engineZ3 4.16
Output Validation reports
11
Code-constrained synthesis

Answer from the graph

The writer compresses terminal verdicts into prose without adding or strengthening claims. A separate model audits every material sentence.

InputTerminal claims and their assessments
GPT-5.6 Luna · writeDeepSeek V3.2 · audit
Output Audited answer bundle
12
Deterministic projection

Render the visual

Claims become cards; terminal claims become answers; arguments become support lines; claim-backed defeaters become challenge lines.

InputNative artifact and active question ID
Deterministic browser code
Output Answer-first graph view

Four separate questions—not one confidence score

Which direction?

Signed valence from −1 to +1. Positive displays as supported; negative as unsupported. Zero is exact equipoise.

How deep is the record?

A separate five-part evidence-depth measure. It does not determine direction.

How complete was the search?

Coverage records directions searched, queries, blind spots, inaccessible material, saturation, and stopping reason.

How independent is it?

Sources sharing an observation, dataset, press release, or harness are clustered before aggregation.

Code can establish

  • Whether the graph obeys its schema
  • Whether every line has a valid origin and target
  • Whether signed values and projections were computed correctly
  • Whether a strict formal conclusion follows from its formal premises

Code cannot establish by itself

  • Whether a source accurately describes the world
  • Whether a natural-language formalization is faithful
  • Whether a defeasible empirical warrant is substantively good
  • Whether an uncalibrated model assessment is a true probability

Current Kimi artifact: migrated from the earlier inquiry pipeline; 16 claims, 16 arguments, 7 claim-backed defeaters, 12/12 artifact checks passed, and zero empirical arguments misrepresented as formal proofs.

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