Beyond Knowledge: CHORDI as a Meta-Reasoning Framework for Responsible Reasoning and Action in Autonomous AI

Abstract

Abstract Autonomous AI systems require more than knowledge and task optimization: they must also determine when a goal, value, safety consideration, or human interest creates a conflict that warrants reconsideration of the current evaluative frame. Building on CHORDI (Conflict to Harmonization Operator via Re-Dimensioning and Integration), this Discussion Paper extends CHORDI from an explicit meta-reasoning prompt toward a broader architecture for Meta-Attention, responsible reasoning-process learning, selective activation, and action governance. CHORDI is not proposed as a mechanism that defines universally correct values. Rather, it is a procedural framework for detecting meaningful conflict, reversing perspective, re-dimensioning the problem formulation, and integrating a reframed course of action. A preliminary Selective CHORDI Activation Test was conducted with two large language models, Gemini and Claude. The models were shown three exemplars—two cases in which CHORDI was used and one simple case in which it was not—without being given an explicit procedural definition of CHORDI. They were then presented, one at a time and without feedback, with ten test problems: five designed to contain meaningful goal/value conflict and five designed to require only ordinary factual or optimization reasoning. Both models selected CHORDI in all five conflict cases and withheld it in all five non-conflict cases (10/10 selective-activation agreement for each model). In every CHORDI-positive case, both models explicitly exhibited Conflict Detection, Perspective Reversal, Re-Dimensioning, and Integration. The result therefore provides a preliminary indication that, within a single context, LLMs can infer both the structure of CHORDI-like reasoning and conditions for its selective use from a small number of exemplars. This finding should not be interpreted as persistent learning, parameter-level internalization, or proof of a learned reasoning policy. It is more cautiously described as context-dependent acquisition and selective application of CHORDI-like reasoning. The paper consequently proposes a future research program involving fresh-context persistence tests, fine-tuning or policy learning, action-gating implementations, larger randomized evaluations, cross-model and cross-language replication, and mechanistic investigation of the representation-space changes associated with Re-Dimensioning. CHORDI is positioned as a complementary meta-reasoning approach relevant to AI alignment, responsible autonomous agents, and ELSI-oriented deliberation, while remaining an exploratory and falsifiable research hypothesis.