ORDDAR Framework Repairs AI Agent Reasoning Distortions

This research introduces ORDDAR, a novel reasoning framework designed to enhance the robustness and reliability of AI agents performing long-term reasoning and decision-making. A common challenge in complex AI tasks is the propagation of erroneous intermediate states, leading to inconsistent decisions and unreliable outputs. Unlike existing methods that often regenerate entire reasoning trajectories, ORDDAR focuses on localized repair. ORDDAR models reasoning as a series of cognitive state transitions. It is capable of detecting specific distortions within these states, retrieving relevant reasoning from past experiences, and then repairing only the affected parts of the reasoning process. This selective recovery mechanism significantly improves reasoning quality, enhances the agent's ability to recover from errors, and offers greater interpretability compared to traditional iterative planning, self-reflection, or verification approaches. Experiments across various reasoning benchmarks, including mathematical, commonsense, multi-hop, and clinical reasoning, demonstrate its effectiveness.