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arXiv (Cornell University)· 2026· Preprint

Affordance-Compiled Intelligence: Observable-Only Cognitive Impedance Matching for No-Meta LLM-Integrated Systems

Patrick Lewis

Short summary

This paper introduces Cognitive Impedance Matching Theory (CIMT), a new framework for enhancing LLM system reliability by redesigning the surrounding observable world, rather than improving model weights. CIMT treats system capability as a world-side compilation problem, defining operational claims through explicit objects and ledgers.

AI-generated from the title and abstract; the full text is not read.

Key points

  • CIMT treats LLM system capability as a world-side compilation problem, not a model-weight improvement problem.
  • It defines operational claims and evidence using explicit objects and observable ledgers.
  • The theory models human reviewers and benchmarks as fallible measurement channels, not privileged evaluators.
  • CIMT offers a conservative certification framework for improving paired target-channel performance and reliability.

AI-generated from the title and abstract; the full text is not read.

Abstract

Affordance-Compiled Intelligence develops Cognitive Impedance Matching Theory (CIMT), an observable-only and no-meta protected compiler theory for LLM-integrated systems. The paper studies how a fixed model-policy can exhibit different operational capability when the surrounding world is redesigned through observations, typed action handles, validators, repair paths, rollback modes, authority scopes, context summaries, and auditable receipts. CIMT treats system-level capability amplification as a world-side compilation problem rather than a model-weight improvement problem. It defines operational claims through explicit claim objects and evidence objects, using committed observable ledgers, target-evaluation channels, deterministic reducers, validity budget ledgers, evidence dependency graphs, artifact I/O manifests, conformance envelopes, and finite-sample or sequential certificates. Human reviewers, LLM judges, benchmarks, and external auditors are not treated as privileged evaluators; they are modeled as named, fallible measurement channels. The theory provides a conservative certification framework for paired target-channel improvement, vector debt accounting, forbidden-coordinate zero certificates, target-firewall discipline, scope simulation, dynamic widening, runtime and model-policy conformance, macro reliability, repair contraction, distribution-shift transfer, and receipt sufficiency. It also includes worked examples for code-editing agents and retrieval-augmented generation systems. The intended contribution is a practical formal foundation for making fixed-model LLM systems more reliable through observable world-side interface, authority, validation, repair, and audit design.

The authors' abstract, as published at the source. arXiv (Cornell University), 2026 · DOI ↗

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Field: Artificial Intelligence

Artificial IntelligenceComputer Science