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 PositioningThis 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-agnosticnon-invasiveoutput-independentreal-time or post-hoc deployablecompatible with black-box systemsIt is applicable to language models, agent stacks, tool-using systems, and other recursive decision processes. What Is IncludedTheoretical Foundations:Formal definitions of recursive coherence, drift, contradiction density, and phase stabilityLyapunov-style boundedness criteria for reasoning trajectoriesCorrection viability and recovery dynamics under bounded intervention Measurement & Evaluation:A composite coherence drift index suitable for continuous monitoringFalsifiable stress tests and known-weakness cases (false negatives, false positives, mislocalization)Evaluation manifests demonstrating cross-domain structural invarianceImplementation Guidance:Deployment pathways for research, auditing, and safety instrumentationIntegration patterns for real-time and post-hoc analysisAll components are expressed in a non-interpretive, measurement-first framework. What This Is NotTo avoid misinterpretation, this work explicitly does not:claim to solve alignmentenforce values or ethicsclassify content as true or falsereplace existing safety policiesinfer mental states, intent, or psychologyrequire access to model weights or training dataguarantee detection of all failure modes (explicit detection boundaries are provided)Any corrective mechanisms described are optional and external to the measurement core. Intended AudienceThis deposit is intended for:AI safety and evaluation researchersAlignment and governance teamsDevelopers of long-horizon agentsAuditors and regulatory reviewersResearchers studying failure modes in recursive systemsThe material is suitable for institutional review, independent replication, regulatory assessment, and reproducible testing. Methodological EmphasisAll 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 ContributionsThis work provides:The first formal framework for measuring coherence drift in recursive reasoning independent of task performanceBounded stability criteria applicable to black-box systemsCharacterized failure modes with reproducible test casesCross-domain evaluation demonstrating structural invarianceDeployment-ready instrumentation compatible with existing AI safety pipelines File ManifestRecursive_Coherence_Drift_Detector__RCDD_.docx - Core framework definitionRCDD_-_Lyapunov-Style_Stability_Instrumentation_for_Long-Horizon_AI_Reasoning.docx - Stability criteria and boundedness analysisRecursive_Coherence_Engine__RCE_.docx - Reference implementation architectureRELATIONAL_COHERENCE_PLATFORM.docx - Deployment platform specificationRCDD_-_Pilot_Proposal_for_AI_Safety_Teams.docx - Integration guidance for safety teamstwo_falsifiable__known_weakness_tests__for_any_RCDD_implementation.docx - Characterized failure modes and test casesRCDD_-High_Energy_Physics__HEP.docx - Domain-specific application case study Licensing and AttributionThis work is released under the Copeland Resonant Harmonic Formalism license (CRHC v1.0).Attribution required for all useNon-commercial use only (commercial licensing available on request)Derivative works must preserve structural equivalence and attributionVersion 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