@inproceedings{mishra-etal-2026-beyond,
title = "Beyond Epistemic Collapse: Disagreement-Aware Scientific Retrieval-Augmented Generation",
author = "Mishra, Animesh and
Sharma, Krishang and
Khetarpaul, Sonia",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2421/",
pages = "54964--54985",
ISBN = "979-8-89176-459-0",
abstract = "Scientific retrieval-augmented generation (RAG) systems typically return one fluent answer even when the underlying literature contains multiple well-supported but incompatible views. We call this failure mode epistemic collapse: compression of structured disagreement into a single response. We present EVIRAG (Evidence-centric, Viewpoint-Inclusive RAG), a framework for scientific question answering that combines adversarial retrieval, claim-level disagreement graphs, causal disagreement attribution, multi-view synthesis, and evidence structured confidence labels. We also introduce EVIRAG-BENCH, a 1,250-query benchmark spanning five contested scientific domains. Across eight compared systems, EVIRAG substantially outperforms vanilla RAG on ContradictionRecall and dense-encoder ViewpointCoverage while reducing Consensus Collapse Score. Human evaluation with three annotators confirms markedly higher epistemic completeness for EVIRAG than for vanilla RAG (Krippendorff{'}s {\ensuremath{\alpha}}=0.72, p{\ensuremath{<}}0.001, Cohen{'}s d=2.71). An offline precomputed graph reduces warm-query latency by over an order of magnitude. These results suggest that treating disagreement preservation as a primary design objective substantially improves how scientific RAG systems represent contested evidence in disagreement-heavy QA settings."
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<abstract>Scientific retrieval-augmented generation (RAG) systems typically return one fluent answer even when the underlying literature contains multiple well-supported but incompatible views. We call this failure mode epistemic collapse: compression of structured disagreement into a single response. We present EVIRAG (Evidence-centric, Viewpoint-Inclusive RAG), a framework for scientific question answering that combines adversarial retrieval, claim-level disagreement graphs, causal disagreement attribution, multi-view synthesis, and evidence structured confidence labels. We also introduce EVIRAG-BENCH, a 1,250-query benchmark spanning five contested scientific domains. Across eight compared systems, EVIRAG substantially outperforms vanilla RAG on ContradictionRecall and dense-encoder ViewpointCoverage while reducing Consensus Collapse Score. Human evaluation with three annotators confirms markedly higher epistemic completeness for EVIRAG than for vanilla RAG (Krippendorff’s \ensuremathα=0.72, p\ensuremath<0.001, Cohen’s d=2.71). An offline precomputed graph reduces warm-query latency by over an order of magnitude. These results suggest that treating disagreement preservation as a primary design objective substantially improves how scientific RAG systems represent contested evidence in disagreement-heavy QA settings.</abstract>
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%0 Conference Proceedings
%T Beyond Epistemic Collapse: Disagreement-Aware Scientific Retrieval-Augmented Generation
%A Mishra, Animesh
%A Sharma, Krishang
%A Khetarpaul, Sonia
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F mishra-etal-2026-beyond
%X Scientific retrieval-augmented generation (RAG) systems typically return one fluent answer even when the underlying literature contains multiple well-supported but incompatible views. We call this failure mode epistemic collapse: compression of structured disagreement into a single response. We present EVIRAG (Evidence-centric, Viewpoint-Inclusive RAG), a framework for scientific question answering that combines adversarial retrieval, claim-level disagreement graphs, causal disagreement attribution, multi-view synthesis, and evidence structured confidence labels. We also introduce EVIRAG-BENCH, a 1,250-query benchmark spanning five contested scientific domains. Across eight compared systems, EVIRAG substantially outperforms vanilla RAG on ContradictionRecall and dense-encoder ViewpointCoverage while reducing Consensus Collapse Score. Human evaluation with three annotators confirms markedly higher epistemic completeness for EVIRAG than for vanilla RAG (Krippendorff’s \ensuremathα=0.72, p\ensuremath<0.001, Cohen’s d=2.71). An offline precomputed graph reduces warm-query latency by over an order of magnitude. These results suggest that treating disagreement preservation as a primary design objective substantially improves how scientific RAG systems represent contested evidence in disagreement-heavy QA settings.
%U https://aclanthology.org/2026.emnlp-main.2421/
%P 54964-54985
Markdown (Informal)
[Beyond Epistemic Collapse: Disagreement-Aware Scientific Retrieval-Augmented Generation](https://aclanthology.org/2026.emnlp-main.2421/) (Mishra et al., EMNLP 2026)
ACL