arXiv Computation and Language
To Copy or Not to Copy: Copying Is Easier to Induce Than Recall
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FIDES: Faithful Inference via Deep Evidence Signals for Retrieval-Memory Conflict in RAG
arXiv:2606. 05644v1 Announce Type: new Abstract: When retrieved evidence contradicts parametric memory, language models frequently ignore context and default to memorized priors -- a failure that undermines the core purpose of retrieval augmentation.
arXiv AI
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TokenMem: Faithful Knowledge Injection for Frozen LLMs
arXiv:2607. 22625v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge, but suffers from knowledge conflicts: when retrieved information contradicts parametric memory, the shared self-attention pathway produces unpredictable outputs.
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Quantifying Prior Dominance in RAG Systems
arXiv:2606. 23695v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds Large Language Models in external knowledge, yet current evaluations rely on discrete heuristics that suffer from ''epistemic blindness'' - failing to distinguish genuine contextual information extraction from parametric memory recall.
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Language Models Can Control Their Own Attention
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How Do Language Models Choose Between Context and Memory?
arXiv:2609.00753v1 Announce Type: new Abstract: When contextual information conflicts with the knowledge stored in model parameters, activation directions can be used to decode and steer which source...
arXiv AI
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ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation
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