Part of CNS 7.1 / GCTS: Grounded Chiral Tensor Synthesis

GCTS Architecture

GCTS is an evidence-first pipeline. LLMs may propose extractions or render reports, but truth ranking is produced by structured evidence, access modeling, rule closure, possible-world scoring, and calibrated parameters.

flowchart TD A[Raw corpus / documents /
observations] --> B[Evidence Ingestor] B --> C[Evidence Atom Store] C --> D[Claim Proposer] C --> RA[Record Access
Modeler] D --> E[Grounding Verifier] RA --> IM[Institutional
Incentive Modeler] E --> F[Rule Compiler] IM --> F F --> G[Tensor Logic Closure] G --> H[World Builder] RA --> H IM --> H H --> I[Chirality +
Residual Analyzer] I --> J[Latent Context +
Access Orthesist] J --> H H --> K[World Ranker] K --> L[Synthesizer /
Renderer] L --> M[Audit + Report]

Where GCTS Differs From Standard Fact Verification

Standard pipelineGCTS addition
Retrieve evidenceModel expected-but-unproduced records
Classify support/refute/insufficient evidenceRank claims across access-aware possible worlds
Attach citationsPreserve provenance, access path, and production history
Estimate confidenceSeparate posterior mass, strict proof support, and confidence
Resolve contradictionPreserve contradiction residuals and competing worlds
Treat missing evidence as weak supportClassify absence by duty, observability, control, access state, and production response
Use model judgment as answerEnforce runtime oracle-boundary controls

Core Modules

Evidence Ingestor

Parses the corpus, assigns stable evidence IDs, segments spans, computes source quality priors, and stores provenance, temporal metadata, and access path.

Output: EvidenceAtom[].

Record Access Modeler

Identifies records expected by procedure, role, instrumentation, policy, or ordinary practice. It classifies access states, distinguishes absence of evidence from evidence of absence, and emits record-contingency notes.

Output: RecordAccessState[].

Institutional Incentive Modeler

Models actor roles and evidence-control asymmetries. It estimates incentives to disclose, conceal, delay, narrow, or frame evidence. It adjusts source reliability and missingness likelihood while leaving claim proof to evidence and rules.

Output: InstitutionalIncentiveProfile[].

Claim Proposer

Extracts candidate claims, attaches evidence references, proposes typed relations, preserves extraction confidence, and marks claims that depend on unavailable or expected records.

LLMs may be used here, but proposed claims are untrusted until verified.

Grounding Verifier

Resolves citations, runs claim-evidence entailment, detects invalid references, rejects unsupported strict promotion, and emits grounding reports.

Rule Compiler And Tensor Logic Closure

The compiler converts verified claims, relations, and access states into strict and soft rules. The closure engine computes zero-temperature closure for strict rules, soft closure for hypotheses, proof traces, and contradiction structure.

World Builder And Ranker

The world builder enumerates or searches possible worlds with alternative assumptions, contexts, access states, missingness hypotheses, and institutional-incentive hypotheses. The ranker computes:

  • world posterior mass;
  • claim likely-truth rankings;
  • strict support mass;
  • confidence;
  • uncertainty decomposition;
  • record-contingency notes.

Synthesizer / Renderer

The renderer produces top-K worlds and natural-language reports with proof links, evidence links, access-contingency notes, calibrated hedging, and next record requirements. It must refuse unsupported strict claims.

Data Flow

  1. Evidence enters as immutable atoms.
  2. Expected records and access states are modeled separately from available evidence.
  3. Claims are proposed and linked to evidence, access states, or record contingencies.
  4. Verification rejects non-resolving references and low-entailment strict links.
  5. Rules compile verified claims, relations, and access states into a proof substrate.
  6. Worlds are generated from alternative assumptions, contexts, access models, and missingness hypotheses.
  7. Worlds are ranked by evidence support, contradiction energy, parsimony, source reliability, source risk, and access coherence.
  8. Claims receive posterior mass, strict proof support, confidence, and status.
  9. The renderer outputs ranked alternatives and collapses to a single answer only when uncertainty is low.

Audit Artifacts

Every run emits an input corpus manifest, evidence atom manifest, record-access manifest, institutional-incentive manifest, claim extraction manifest, grounding report, rule compilation manifest, world distribution report, proof trace file, access-contingency report, rendered synthesis, and metrics report.

If any strict gate fails, no strict promoted truth claim is produced. The report uses statuses such as unsupported, record_contingent, conflicted, or insufficient_evidence and lists missing records, access constraints, and next collection actions.

Step 2 of 11 in CNS 7.1 / GCTS: Grounded Chiral Tensor Synthesis