PhilArchive
open access
Cognitive Compatibility - Why Drift Must Be Measured, Not Eliminated
This paper introduces cognitive compatibility as a measurable property of AI systems operating under constraint. It argues that prevailing evaluation approaches—focused on accuracy, speed, and output quality—misidentify where system failure originates. Rather than emerging at the level of outputs, failure begins upstream in how systems structure the space of permissible variation. A dominant response has been to reduce or eliminate drift (variation) in pursuit of stability and alignment. This paper challenges that premise. It shows that eliminating drift collapses a system’s capacity to adapt, respond proportionally to perturbation, and expose its own misalignment. In such conditions, systems either become deterministic transformation engines or exhibit opaque stability where misalignment persists without visibility. Drawing on constraint-based experimentation within the Architecture of Limitation (AoL), the paper reframes drift as a necessary diagnostic signal. Drift enables a feedback loop in which misalignment produces error, error surfaces collapse, and collapse enables recalibration. Without this loop, alignment cannot be internally sustained. The paper proposes that the goal is not the elimination of drift, but its regulation. A cognitively compatible system permits bounded variation, makes that variation observable, and supports recovery toward proportioned alignment. To operationalize this, a formal measurement framework—the Cognitive Compatibility Index (CCI)—is introduced. The framework evaluates systems across five indices: bounded drift, observability, recovery capacity, perturbation responsiveness, and alignment stability. Derived indicators assess risks such as silent misalignment and over-determinism. The framework is explicitly diagnostic, non-normative, and carries no governance authority. The central claim is structural: systems that eliminate drift cannot function as cognitive extensions to human reasoning. Systems that preserve bounded, observable, and recoverable drift remain compatible with cognition as a co-participating process.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- When Human-in-the-Loop Fails: An Answerability Test for Deployed AI Systems#28983
- When Human-in-the-Loop Fails: An Answerability Test for Deployed AI Systems#28982
- A Counterfactual Account of Algorithmic Robustness#81072
- Unmonitorability of Artificial Intelligence#101102
- Faultless: A Program Equivalence Technique for Validating and Evaluating Neural Decompilers#1119162
Record · ID 108592
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive