@inproceedings{verma-etal-2026-nest,
title = "{NEST}: Nested Evidence Survival for Retrieval",
author = "Verma, Akshay and
Pillai, Siddharth and
Sircar, Prateek and
Gupta, Deepak",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.35/",
pages = "507--516",
ISBN = "979-8-89176-394-4",
abstract = "Retrieval-Augmented Generation (RAG) systems degrade sharply under extreme noise, where relevant evidence is sparse and easily pruned by static retrieval decisions. Existing approaches fixed top-k retrieval, hierarchical chunking, cross-encoder reranking, or policy-based iterative control- either rely on rigid heuristics or incur substantial computational overhead, and often fail to recover context-dependent evidence without introducing redundancy or latency. We introduce NEST (Nested Evidence Survival for Retrieval), a lightweight, training-free RAG framework that improves factual grounding by explicitly separating recall amplification from precision selection. NEST first maximizes recall through Nested Evidence Survival, evaluating candidates under nested retrieval contexts to rescue evidence that would otherwise be pruned by static chunking. It then applies a survival-consistent Mean Reciprocal Rank (MRR) selection mechanism to retain evidence that remains salient across retrieval scopes, removing redundancy without harming recall. Evaluated on WebQuestions, HotpotQA (distractor setting), and a proprietary InternalQA benchmark with 50M Common Crawl distractors, NEST consistently outperforms strong adaptive RAG baselines, including DeepRAG, improving EM by up to +2.4 pp and F1 by +2.1 pp, while increasing retrieval recall by +6.8 pp. These gains are achieved with only 12{--}18 ms additional latency. Ablation studies confirm that Nested Evidence Survival drives recall improvements, while MRR-based selection converts these gains into precision, demonstrating that recall-first retrieval with principled pruning can outperform iterative control and model scaling in retrieval-augmented generation."
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<abstract>Retrieval-Augmented Generation (RAG) systems degrade sharply under extreme noise, where relevant evidence is sparse and easily pruned by static retrieval decisions. Existing approaches fixed top-k retrieval, hierarchical chunking, cross-encoder reranking, or policy-based iterative control- either rely on rigid heuristics or incur substantial computational overhead, and often fail to recover context-dependent evidence without introducing redundancy or latency. We introduce NEST (Nested Evidence Survival for Retrieval), a lightweight, training-free RAG framework that improves factual grounding by explicitly separating recall amplification from precision selection. NEST first maximizes recall through Nested Evidence Survival, evaluating candidates under nested retrieval contexts to rescue evidence that would otherwise be pruned by static chunking. It then applies a survival-consistent Mean Reciprocal Rank (MRR) selection mechanism to retain evidence that remains salient across retrieval scopes, removing redundancy without harming recall. Evaluated on WebQuestions, HotpotQA (distractor setting), and a proprietary InternalQA benchmark with 50M Common Crawl distractors, NEST consistently outperforms strong adaptive RAG baselines, including DeepRAG, improving EM by up to +2.4 pp and F1 by +2.1 pp, while increasing retrieval recall by +6.8 pp. These gains are achieved with only 12–18 ms additional latency. Ablation studies confirm that Nested Evidence Survival drives recall improvements, while MRR-based selection converts these gains into precision, demonstrating that recall-first retrieval with principled pruning can outperform iterative control and model scaling in retrieval-augmented generation.</abstract>
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%0 Conference Proceedings
%T NEST: Nested Evidence Survival for Retrieval
%A Verma, Akshay
%A Pillai, Siddharth
%A Sircar, Prateek
%A Gupta, Deepak
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F verma-etal-2026-nest
%X Retrieval-Augmented Generation (RAG) systems degrade sharply under extreme noise, where relevant evidence is sparse and easily pruned by static retrieval decisions. Existing approaches fixed top-k retrieval, hierarchical chunking, cross-encoder reranking, or policy-based iterative control- either rely on rigid heuristics or incur substantial computational overhead, and often fail to recover context-dependent evidence without introducing redundancy or latency. We introduce NEST (Nested Evidence Survival for Retrieval), a lightweight, training-free RAG framework that improves factual grounding by explicitly separating recall amplification from precision selection. NEST first maximizes recall through Nested Evidence Survival, evaluating candidates under nested retrieval contexts to rescue evidence that would otherwise be pruned by static chunking. It then applies a survival-consistent Mean Reciprocal Rank (MRR) selection mechanism to retain evidence that remains salient across retrieval scopes, removing redundancy without harming recall. Evaluated on WebQuestions, HotpotQA (distractor setting), and a proprietary InternalQA benchmark with 50M Common Crawl distractors, NEST consistently outperforms strong adaptive RAG baselines, including DeepRAG, improving EM by up to +2.4 pp and F1 by +2.1 pp, while increasing retrieval recall by +6.8 pp. These gains are achieved with only 12–18 ms additional latency. Ablation studies confirm that Nested Evidence Survival drives recall improvements, while MRR-based selection converts these gains into precision, demonstrating that recall-first retrieval with principled pruning can outperform iterative control and model scaling in retrieval-augmented generation.
%U https://aclanthology.org/2026.acl-industry.35/
%P 507-516
Markdown (Informal)
[NEST: Nested Evidence Survival for Retrieval](https://aclanthology.org/2026.acl-industry.35/) (Verma et al., ACL 2026)
ACL
- Akshay Verma, Siddharth Pillai, Prateek Sircar, and Deepak Gupta. 2026. NEST: Nested Evidence Survival for Retrieval. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 507–516, San Diego, California, USA. Association for Computational Linguistics.