Mitigating Context-Memory Conflicts in LLMs through Dynamic Cognitive Reconciliation Decoding

• URL: http://arxiv.org/abs/2605.12185v1
• Date: Tue, 12 May 2026 14:29:45 GMT
• Title: Mitigating Context-Memory Conflicts in LLMs through Dynamic Cognitive Reconciliation Decoding
• Abstract summary: Large language models accumulate parametric knowledge through pre-training.<n> knowledge conflicts occur when outdated or incorrect parametric knowledge conflicts with external knowledge in the context.<n>Existing methods address knowledge conflicts through contrastive decoding, but in conflict-free scenarios, static approaches disrupt output distribution.<n>We propose Dynamic Cognitive Reconciliation Decoding (DCRD), to predict and context-memory conflicts.
• Score: 51.242997961841276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models accumulate extensive parametric knowledge through pre-training. However, knowledge conflicts occur when outdated or incorrect parametric knowledge conflicts with external knowledge in the context. Existing methods address knowledge conflicts through contrastive decoding, but in conflict-free scenarios, static approaches disrupt output distribution. Other dynamic decoding methods attempt to measure the degree of conflict but still struggle with complex real-world situations. In this paper, we propose a two-stage decoding method called Dynamic Cognitive Reconciliation Decoding (DCRD), to predict and mitigate context-memory conflicts. DCRD first analyzes the attention map to assess context fidelity and predict potential conflicts. Based on this prediction, the input is directed to one of two decoding paths: (1) greedy decoding, or (2) context fidelity-based dynamic decoding. This design enables DCRD to handle conflicts efficiently while maintaining high accuracy and decoding efficiency in conflict-free cases. Additionally, to simulate scenarios with frequent knowledge updates, we constructed ConflictKG, a knowledge conflict QA benchmark. Experiments on four LLMs across six QA datasets show that DCRD outperforms all baselines, achieving state-of-the-art performance.

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