01 — CNS 8.0 Research Proposal

01 — CNS 8.0 Research Proposal

Title

Chiral Narrative Synthesis 8.0: Grounded Dialectical Orthesis through Chiral Tension, Evidential Entanglement, Tensor Logic, and Predicate Invention

Abstract

Chiral Narrative Synthesis 8.0 (CNS 8.0) is a research and implementation plan for synthesizing grounded narrative objects under contradiction and incomplete information. The system operates over Structured Narrative Objects (SNOs), not loose claims. It identifies productive conflicts by combining chirality with Evidential Entanglement, stress-tests them through an Antagonist and critic ensemble, grounds all promoted claims through tensor-logic proof traces, uses residual contradictions to propose latent predicates, and emits a synthesized SNO called an orthesis candidate when the synthesis survives repeated grounding and rendering.

CNS 8.0 uses fact verification, access states, possible worlds, calibration, and audit reporting as constraints on narrative synthesis. It performs grounded dialectical synthesis: constructing a new narrative object from structured disagreement while preserving provenance, residual uncertainty, and unresolved contradiction.

Core hypothesis

Conflicting accounts become productive when they have both:

1. high chiral tension — structured asymmetry or non-commuting language–logic round-trip distortion; and
2. high Evidential Entanglement — substantial overlap in the evidence base they interpret differently.

CNS 8.0 predicts that high-chirality / high-entanglement pairs are better synthesis targets than pairs selected by embedding distance, debate disagreement, RAG retrieval score, or claim-level contradiction alone.

Research questions

RQ1 — Productive conflict selection

Can a combined chirality–entanglement score identify pairs of narrative objects that yield useful synthesis better than embedding-distance or contradiction-only baselines?

RQ2 — Orthesis convergence

Can repeated grounding and synthesis produce stable SNOs whose proof traces, evidence coverage, and topology diagnostics improve over iterations?

RQ3 — Predicate invention

When contradictions persist under zero-temperature proof closure, can residual-tensor decomposition recover latent context variables such as time, subgroup, measurement method, source frame, jurisdiction, mechanism, or definition boundary?

RQ4 — Runtime oracle discipline

Can the system train with labels and expert oracles while running without runtime gold labels, answer keys, or LLM judgments?

RQ5 — Multiverse-aware output

Can possible-world ranking and record-access states improve uncertainty reporting without replacing the synthesis operation?

Contributions

1. SNO-8: a typed, proof-carrying Structured Narrative Object that keeps narrative identity, reasoning graph, evidence, access state, proof trace, residual contradictions, and synthesis lineage in one object.
2. CNS Productive Conflict Score: a pair-selection metric combining chiral tension and evidential entanglement.
3. Grounded Dialectical Orthesis: a synthesis loop that emits an orthesis candidate only after surviving grounding, antagonist pressure, proof closure, and residual analysis.
4. Contradiction-Driven Predicate Invention: tensor decomposition over residual contradiction mass to propose latent context predicates.
5. Runtime Oracle Boundary: offline oracle use for training/calibration/evaluation, with no runtime label leakage.
6. MVP: a staged implementation plan using retrieval, extraction, NLI, graph topology, tensor closure, residual decomposition, and bounded LLM rendering.
7. Evaluation Plan: synthetic latent-context tests, SciFact/FEVER grounding tests, SNO-pair synthesis tests, and ablations.

Scope

CNS 8.0 is a research and engineering plan. The Python files in sketches/ are small examples for implementation and test design.

System-level pipeline

source corpus
→ evidence atomization
→ Proposer builds candidate SNOs
→ grounding critics validate citations and entailment
→ Antagonist finds chiral tension, contradictions, topology issues, access gaps
→ pair selector ranks high-chirality/high-entanglement SNO pairs
→ tensor prover computes zero-temperature closure
→ residual analyzer identifies unresolved contradiction mass
→ predicate inventor proposes latent context predicates when needed
→ Synthesizer constructs a new grounded SNO
→ orthesis loop tests G(S(T)) stability
→ multiverse/access layer ranks remaining interpretations
→ audit report exposes proof traces, uncertainties, and residual contradictions

Boundary conditions

• LLM debate used to decide truth.
• RAG as final synthesis.
• Possible-world posterior mass as replacement for narrative synthesis.
• Evidence atoms as replacement for SNOs.
• Record-access state as replacement for contradiction analysis.
• Audit report as replacement for synthesis.

Expected MVP result

The first CNS 8.0 MVP targets:

1. citation-valid SNO extraction on a small corpus;
2. productive-pair selection by chirality and evidential entanglement;
3. zero-temperature proof closure over at least one rule family;
4. residual tensor construction over unresolved support/refute mass;
5. latent context recovery on synthetic examples;
6. synthesized SNO output with proof traces;
7. orthesis stability diagnostics;
8. calibrated uncertainty report with explicit access states.