@inproceedings{shi-etal-2026-dualresearch,
title = "{D}ual{R}esearch: Entropy-Gated Dual-Graph Retrieval for Answer Reconstruction in Deep Research Agents",
author = "Shi, Jinxin and
Cao, Zongsheng and
Ma, Runmin and
Hu, Yusong and
Zhou, Jie and
Fan, Yue and
Li, Xin and
Bai, Lei and
He, Liang and
Zhang, Bo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.875/",
pages = "18486--18501",
ISBN = "979-8-89176-462-0",
abstract = "Deep Research Agents (DR-Agents) extend LLMs with multi-step retrieval, tool use, and intermediate computation to solve complex problems. However, their execution logs often contain redundant context, noisy evidence, and brittle dependencies among intermediate decisions. As a result, final answers can be sensitive to irrelevant or weakly verified information. We propose DualResearch, a log-based post-processing framework for answer reconstruction. Given a query and a completed DR-Agent log, DualResearch normalizes the log into event records and constructs two evidence graphs: a breadth semantic graph for declarative evidence and a depth causal graph for procedural provenance. It then retrieves breadth nodes and depth paths, assigns higher weights to retrieval channels with more concentrated retained candidate distributions, and jointly considers relevance and cross-view agreement to select evidence for reconstruction; the concentration measure reflects retrieval confidence rather than factual correctness. Experiments on HLE, GAIA, and GPQA show consistent accuracy improvements over underlying DR-Agents and controlled log-reuse baselines."
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<abstract>Deep Research Agents (DR-Agents) extend LLMs with multi-step retrieval, tool use, and intermediate computation to solve complex problems. However, their execution logs often contain redundant context, noisy evidence, and brittle dependencies among intermediate decisions. As a result, final answers can be sensitive to irrelevant or weakly verified information. We propose DualResearch, a log-based post-processing framework for answer reconstruction. Given a query and a completed DR-Agent log, DualResearch normalizes the log into event records and constructs two evidence graphs: a breadth semantic graph for declarative evidence and a depth causal graph for procedural provenance. It then retrieves breadth nodes and depth paths, assigns higher weights to retrieval channels with more concentrated retained candidate distributions, and jointly considers relevance and cross-view agreement to select evidence for reconstruction; the concentration measure reflects retrieval confidence rather than factual correctness. Experiments on HLE, GAIA, and GPQA show consistent accuracy improvements over underlying DR-Agents and controlled log-reuse baselines.</abstract>
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%0 Conference Proceedings
%T DualResearch: Entropy-Gated Dual-Graph Retrieval for Answer Reconstruction in Deep Research Agents
%A Shi, Jinxin
%A Cao, Zongsheng
%A Ma, Runmin
%A Hu, Yusong
%A Zhou, Jie
%A Fan, Yue
%A Li, Xin
%A Bai, Lei
%A He, Liang
%A Zhang, Bo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F shi-etal-2026-dualresearch
%X Deep Research Agents (DR-Agents) extend LLMs with multi-step retrieval, tool use, and intermediate computation to solve complex problems. However, their execution logs often contain redundant context, noisy evidence, and brittle dependencies among intermediate decisions. As a result, final answers can be sensitive to irrelevant or weakly verified information. We propose DualResearch, a log-based post-processing framework for answer reconstruction. Given a query and a completed DR-Agent log, DualResearch normalizes the log into event records and constructs two evidence graphs: a breadth semantic graph for declarative evidence and a depth causal graph for procedural provenance. It then retrieves breadth nodes and depth paths, assigns higher weights to retrieval channels with more concentrated retained candidate distributions, and jointly considers relevance and cross-view agreement to select evidence for reconstruction; the concentration measure reflects retrieval confidence rather than factual correctness. Experiments on HLE, GAIA, and GPQA show consistent accuracy improvements over underlying DR-Agents and controlled log-reuse baselines.
%U https://aclanthology.org/2026.findings-emnlp.875/
%P 18486-18501
Markdown (Informal)
[DualResearch: Entropy-Gated Dual-Graph Retrieval for Answer Reconstruction in Deep Research Agents](https://aclanthology.org/2026.findings-emnlp.875/) (Shi et al., Findings 2026)
ACL
- Jinxin Shi, Zongsheng Cao, Runmin Ma, Yusong Hu, Jie Zhou, Yue Fan, Xin Li, Lei Bai, Liang He, and Bo Zhang. 2026. DualResearch: Entropy-Gated Dual-Graph Retrieval for Answer Reconstruction in Deep Research Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 18486–18501, Budapest, Hungary. Association for Computational Linguistics.