arXiv AI June 6, 2026 By Zhe Yu, Wenpeng Xing, Tiancheng Zhao, Mohan Li, Changting Lin, Meng Han

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 24

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.

By Barak Or

llms rag safety

arXiv Machine Learning

Jun 2

Resonant Context Anchoring: Decoupling Attention Routing and Signal Gain at Inference Time

arXiv:2606. 01923v1 Announce Type: cross Abstract: Large Language Models (LLMs) frequently exhibit "contextual disregard" when faced with input evidence that conflicts with their internal parametric memory, leading to persistent factual hallucinations.

By Mingkuan Zhao, Yide Gao, Wentao Hu, Suquan Chen, Tianchen Huang, Zhenhua An, Zetao Chang, Xiayu Sun, Yuheng Min

llms efficiency safety

arXiv AI

Jul 10

ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation

arXiv:2502. 15543v4 Announce Type: replace-cross Abstract: Large language models (LLMs) integrated with retrieval-augmented generation (RAG) have improved factuality by grounding outputs in external evidence.

By Pengcheng Huang, Zhenghao Liu, Yukun Yan, Haiyan Zhao, Xiaoyuan Yi, Hao Chen, Zhiyuan Liu, Maosong Sun, Tong Xiao, Ge Yu, Chenyan Xiong

llms rag benchmarks

arXiv Computation and Language

Sep 1

To Copy or Not to Copy: Copying Is Easier to Induce Than Recall

arXiv:2601.12075v2 Announce Type: replace Abstract: Language models used in retrieval-augmented settings must arbitrate between parametric knowledge stored in their weights and contextual information...

By Mehrdad Farahani, Franziska Penzkofer, Richard Johansson

llms rag benchmarks

arXiv AI

Aug 18

When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation

arXiv:2608. 16515v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves large language models by grounding generation in external evidence, but it also introduces a source trust problem: retrieved context may be useful, irrelevant, or even misleading.

By Haolin Jin, Pengyue Yang, Huaming Chen

llms rag benchmarks

arXiv Computation and Language

Aug 28

Selective State-Space Adaptation and Retrieval for Language Model Reasoning

The paper introduces a family of adapters that enhance language model reasoning by adding selective state-space control at token and context levels. The token-level MaLoRA makes the adapter’s scaling factor dynamic and recurrent, improving over static low‑rank adaptation. The context-level MaRA tracks cross‑segment reasoning state and retrieves relevant segments, outperforming an eight‑billion‑parameter dense retriever and boosting reasoning accuracy by an average of +6.4 F1 over LoRA.

By Atahan Dokme, Larry Heck

llms fine-tuning benchmarks

One email a morning, machine-written

Related stories

Quantifying Prior Dominance in RAG Systems

Resonant Context Anchoring: Decoupling Attention Routing and Signal Gain at Inference Time

ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation

To Copy or Not to Copy: Copying Is Easier to Induce Than Recall

When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation

Selective State-Space Adaptation and Retrieval for Language Model Reasoning