arXiv AI June 24, 2026 By Barak Or

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 6

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.

By Zhe Yu, Wenpeng Xing, Tiancheng Zhao, Mohan Li, Changting Lin, Meng Han

llms rag benchmarks 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 AI

Aug 11

Bounding Hallucinations: Merlin-Arthur Protocols for Mutual-Information Bounds in Language Models

arXiv:2512. 11614v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on retrieved context to guide large language models (LLM), yet treats the retrieval as a heuristic rather than verifiable evidence -- leading to unsupported answers, hallucinations, and reliance on spurious context.

By Bj\"orn Deiseroth, Max Henning H\"oth, Kristian Kersting, Letitia Parcalabescu

llms rag agents fine-tuning benchmarks safety

arXiv AI

1d ago

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 AI

6d ago

Information Abundance Paradox: Long-Context Training Undermines Parametric Knowledge

arXiv:2608. 12218v1 Announce Type: cross Abstract: Large language models are increasingly trained and deployed with long contexts that span documents, code repositories, and interaction histories.

By Arda Uzunoglu, Benjamin van Durme, Daniel Khashabi

llms fine-tuning

arXiv Machine Learning

Jun 2

Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG

arXiv:2606. 00432v1 Announce Type: new Abstract: As retrieval-augmented generation (RAG) systems scale, it becomes increasingly challenging to ensure faithful grounding in external evidence.

By Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter, Anuj Sharma

llms rag

Related stories

FIDES: Faithful Inference via Deep Evidence Signals for Retrieval-Memory Conflict in RAG

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

Bounding Hallucinations: Merlin-Arthur Protocols for Mutual-Information Bounds in Language Models

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

Information Abundance Paradox: Long-Context Training Undermines Parametric Knowledge

Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG