Recursive Coherence Drift Detection: Stability Instrumentation for Long-Horizon Reasoning Systems

This deposit contains a consolidated instrumentation package for measuring, analyzing, and bounding recursive coherence drift and stability in long-horizon reasoning systems—including language models, autonomous agents, and tool-using AI systems operating over extended inference horizons.
The materials define and evaluate a coherence meter framework designed to observe internal reasoning stability over time, independent of model outputs, task success, or semantic correctness. The focus is structural and dynamical: how reasoning trajectories evolve, destabilize, compensate, or silently fail under recursion, self-reference, and correction loops.

Scope and Positioning
This work does not propose a new model architecture, training method, reward function, or alignment objective. It does not claim semantic truth detection, moral judgment, or domain authority.
Instead, it contributes a measurement layer treating reasoning as a dynamical process subject to boundedness, drift, and instability—analogous to stability analysis in control systems or observability theory in dynamical systems.

The coherence meter operates as:
model-agnostic
non-invasive
output-independent
real-time or post-hoc deployable
compatible with black-box systems
It is applicable to language models, agent stacks, tool-using systems, and other recursive decision processes.

What Is Included
Theoretical Foundations:
Formal definitions of recursive coherence, drift, contradiction density, and phase stability
Lyapunov-style boundedness criteria for reasoning trajectories
Correction viability and recovery dynamics under bounded intervention

Measurement & Evaluation:
A composite coherence drift index suitable for continuous monitoring
Falsifiable stress tests and known-weakness cases (false negatives, false positives, mislocalization)
Evaluation manifests demonstrating cross-domain structural invariance
Implementation Guidance:
Deployment pathways for research, auditing, and safety instrumentation
Integration patterns for real-time and post-hoc analysis
All components are expressed in a non-interpretive, measurement-first framework.

What This Is Not
To avoid misinterpretation, this work explicitly does not:
claim to solve alignment
enforce values or ethics
classify content as true or false
replace existing safety policies
infer mental states, intent, or psychology
require access to model weights or training data
guarantee detection of all failure modes (explicit detection boundaries are provided)
Any corrective mechanisms described are optional and external to the measurement core.

Intended Audience
This deposit is intended for:
AI safety and evaluation researchers
Alignment and governance teams
Developers of long-horizon agents
Auditors and regulatory reviewers
Researchers studying failure modes in recursive systems
The material is suitable for institutional review, independent replication, regulatory assessment, and reproducible testing.

Methodological Emphasis
All claims are framed in terms of observables, bounded behavior, and detectable failure modes. Where limits exist, they are explicitly stated. Where detection fails, those failures are characterized rather than concealed.
The coherence meter is designed to make instability visible—not to decide what systems ought to do.

Key Contributions
This work provides:
The first formal framework for measuring coherence drift in recursive reasoning independent of task performance
Bounded stability criteria applicable to black-box systems
Characterized failure modes with reproducible test cases
Cross-domain evaluation demonstrating structural invariance
Deployment-ready instrumentation compatible with existing AI safety pipelines

File Manifest
Recursive_Coherence_Drift_Detector__RCDD_.docx - Core framework definition
RCDD_-_Lyapunov-Style_Stability_Instrumentation_for_Long-Horizon_AI_Reasoning.docx - Stability criteria and boundedness analysis
Recursive_Coherence_Engine__RCE_.docx - Reference implementation architecture
RELATIONAL_COHERENCE_PLATFORM.docx - Deployment platform specification
RCDD_-_Pilot_Proposal_for_AI_Safety_Teams.docx - Integration guidance for safety teams
two_falsifiable__known_weakness_tests__for_any_RCDD_implementation.docx - Characterized failure modes and test cases
RCDD_-High_Energy_Physics__HEP.docx - Domain-specific application case study

Licensing and Attribution
This work is released under the Copeland Resonant Harmonic Formalism license (CRHC v1.0).
Attribution required for all use
Non-commercial use only (commercial licensing available on request)
Derivative works must preserve structural equivalence and attribution
Version 1.0 - Initial Release

Keywords: AI safety, coherence measurement, drift detection, stability analysis, recursive reasoning, long-horizon AI, dynamical systems, Lyapunov stability, observability, failure mode analysis