Recursive Assumption Conflict Monitoring for AI Decision Synthesis Detecting Deep Incompatibilities Beneath Surface-Level Reasoning

This publication introduces Recursive Assumption Conflict Monitoring (RACM), a conceptual and methodological framework for improving AI decision synthesis. The central idea is that AI-generated answers can appear coherent, balanced, and useful while still depending on deeper assumption structures that contain unresolved conflicts. These conflicts may not appear as direct contradictions in the final answer, but may exist beneath the surface in competing definitions of success, value priorities, causal interpretations, evidence standards, time horizons, or model-world assumptions. The paper argues that reliable AI decision support should not only evaluate the final output, but also examine the recursive assumptions that support it. RACM proposes a process for decomposing AI reasoning into assumption trees, identifying conflicts between deeper branches, and reducing unresolved incompatibility before final synthesis. The framework includes recursive assumption extraction, conflict mapping, and resolution strategies such as prioritization, rejection, conditional separation, reinterpretation, hierarchical synthesis, parallel synthesis, and human oversight. The importance of this idea lies in its focus on a hidden weakness of AI reasoning: the risk of false synthesis, where an answer sounds careful and complete but actually merges incompatible reasoning foundations. By shifting attention from surface-level fluency to the stability of the underlying assumption structure, this work offers a way to make AI recommendations more transparent, conditional, and responsible in complex decision-support contexts. This publication is intended as a conceptual and proof-of-concept contribution for researchers interested in AI reliability, metacognition, explainable AI, argumentation, decision support, multi-agent reasoning, and AI safety. It does not claim final empirical validation, but presents a structured framework, proposed metrics, and exploratory case logic for future development and testing.