AIBullisharXiv – CS AI · 9h ago7/10
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FIDES: Faithful Inference via Deep Evidence Signals for Retrieval-Memory Conflict in RAG
FIDES is a training-free decoder that improves how language models handle conflicts between retrieved evidence and internal knowledge by applying selective, token-level corrections rather than uniform adjustments. The method achieves up to 92-94% context fidelity across multiple model scales, demonstrating that targeted intervention at critical decoding points outperforms existing contrastive decoding approaches.