AACoT: Chain-of-Thought Fine-Tuning via Associative Memory and Adaptive Error Correction

Traditional Chain-of-Thought (CoT) approaches in large language models (LLMs) often miss long-range semantic dependencies. As a result, early reasoning errors may cause subsequent cascading failures. To address these issues, we introduce AACoT, a fine-tuning framework that integrates associative memory and adaptive error correction within the CoT reasoning process. The AACoT memory functions in a dualmode capacity that differentiates entity-level knowledge from relation-level knowledge, facilitating dynamic knowledge interaction and efficient retrieval for reasoning. The adaptive error correction mechanism monitors the reasoning process, backtracks upon error detection, and regenerates the corrected reasoning paths. To improve robustness, a prompt refinement module adjusts short-term memory by collecting frequent error patterns to direct future reasoning, and a memory warm-up strategy loads crucial knowledge in advance of inference to minimize dependency on additional training. In the inference process, AACoT produces several reasoning paths and employs weighted voting to determine the final result. Results from experiments conducted on mathematical reasoning benchmarks reveal significant improvements in accuracy, validating that AACoT provides a clear and effective method for enhancing complex reasoning in foundational LLMs.