arXiv Machine Learning July 14, 2026 By S M Asif Hossain, Ruksat Khan Shayoni, M. F. Mridha

EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning

Read the original on arXiv Machine Learning →

arXiv:2607. 10491v1 Announce Type: new Abstract: Retrieval-augmented generation grounds large language models in external evidence, but most pipelines still treat retrieved passages as deterministic and mutually consistent context.

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

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.

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Jun 9

ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation

arXiv:2605. 17301v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice.

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Jun 12

Uncertainty-Aware Hybrid Retrieval for Long-Document RAG

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Aug 11

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Jul 21

Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict

arXiv:2605. 14473v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) is usually evaluated by whether the final answer is correct.

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Jun 26

ConflictScore: Identifying and Measuring How Language Models Handle Conflicting Evidence

arXiv:2606. 26437v1 Announce Type: cross Abstract: Existing metrics for factuality and faithfulness evaluate whether an answer is supported or contradicted by its grounding documents, but they fail to capture when both supporting and contradicting evidence coexist.

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Related stories

Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG

ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation

Uncertainty-Aware Hybrid Retrieval for Long-Document RAG

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

Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict

ConflictScore: Identifying and Measuring How Language Models Handle Conflicting Evidence