@inproceedings{lyu-etal-2026-logic,
title = "Logic Tree: Topology-Aware Backtracking for {D}eep{R}esearch Agents",
author = "Lyu, Tianwen and
Gui, Zhengke and
Zhao, Peilong and
Zhong, Ling and
Zhang, Yiwen and
Liang, Lei and
Sun, Mengshu and
Zhou, Jun and
Zhang, Qiang and
Chen, Huajun",
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.844/",
pages = "17837--17856",
ISBN = "979-8-89176-462-0",
abstract = "Autonomous large language model (LLM) agents have shown strong potential for open-ended information-seeking tasks. However, existing agent frameworks primarily rely on sequential execution, making them vulnerable to error accumulation in long-horizon reasoning. Recent plan-and-execute approaches partially alleviate this issue, but their fixed planning strategies still struggle to recover from intermediate failures. We propose Logic Tree, a non-linear agentic reasoning framework formulated as a Directed Acyclic Graph (DAG). Instead of following a fixed plan with sequential execution, Logic Tree models complex task solving as a dynamically evolving and recoverable graph search process. The framework decomposes queries into parallel execution branches and incorporates a two-level self-correction mechanism. Local backtracking performs targeted rollback to repair failed reasoning states, while global replanning revises high-level execution strategies and preserves verified intermediate conclusions through a knowledge sanitation process. Experiments on BrowseComp, GAIA, and XBench show that Logic Tree consistently outperforms both sequential and plan-and-execute baselines. Further ablation studies demonstrate that combining DAG-based parallel execution with dynamic error recovery substantially improves robustness in long-horizon research tasks."
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<abstract>Autonomous large language model (LLM) agents have shown strong potential for open-ended information-seeking tasks. However, existing agent frameworks primarily rely on sequential execution, making them vulnerable to error accumulation in long-horizon reasoning. Recent plan-and-execute approaches partially alleviate this issue, but their fixed planning strategies still struggle to recover from intermediate failures. We propose Logic Tree, a non-linear agentic reasoning framework formulated as a Directed Acyclic Graph (DAG). Instead of following a fixed plan with sequential execution, Logic Tree models complex task solving as a dynamically evolving and recoverable graph search process. The framework decomposes queries into parallel execution branches and incorporates a two-level self-correction mechanism. Local backtracking performs targeted rollback to repair failed reasoning states, while global replanning revises high-level execution strategies and preserves verified intermediate conclusions through a knowledge sanitation process. Experiments on BrowseComp, GAIA, and XBench show that Logic Tree consistently outperforms both sequential and plan-and-execute baselines. Further ablation studies demonstrate that combining DAG-based parallel execution with dynamic error recovery substantially improves robustness in long-horizon research tasks.</abstract>
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%0 Conference Proceedings
%T Logic Tree: Topology-Aware Backtracking for DeepResearch Agents
%A Lyu, Tianwen
%A Gui, Zhengke
%A Zhao, Peilong
%A Zhong, Ling
%A Zhang, Yiwen
%A Liang, Lei
%A Sun, Mengshu
%A Zhou, Jun
%A Zhang, Qiang
%A Chen, Huajun
%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 lyu-etal-2026-logic
%X Autonomous large language model (LLM) agents have shown strong potential for open-ended information-seeking tasks. However, existing agent frameworks primarily rely on sequential execution, making them vulnerable to error accumulation in long-horizon reasoning. Recent plan-and-execute approaches partially alleviate this issue, but their fixed planning strategies still struggle to recover from intermediate failures. We propose Logic Tree, a non-linear agentic reasoning framework formulated as a Directed Acyclic Graph (DAG). Instead of following a fixed plan with sequential execution, Logic Tree models complex task solving as a dynamically evolving and recoverable graph search process. The framework decomposes queries into parallel execution branches and incorporates a two-level self-correction mechanism. Local backtracking performs targeted rollback to repair failed reasoning states, while global replanning revises high-level execution strategies and preserves verified intermediate conclusions through a knowledge sanitation process. Experiments on BrowseComp, GAIA, and XBench show that Logic Tree consistently outperforms both sequential and plan-and-execute baselines. Further ablation studies demonstrate that combining DAG-based parallel execution with dynamic error recovery substantially improves robustness in long-horizon research tasks.
%U https://aclanthology.org/2026.findings-emnlp.844/
%P 17837-17856
Markdown (Informal)
[Logic Tree: Topology-Aware Backtracking for DeepResearch Agents](https://aclanthology.org/2026.findings-emnlp.844/) (Lyu et al., Findings 2026)
ACL
- Tianwen Lyu, Zhengke Gui, Peilong Zhao, Ling Zhong, Yiwen Zhang, Lei Liang, Mengshu Sun, Jun Zhou, Qiang Zhang, and Huajun Chen. 2026. Logic Tree: Topology-Aware Backtracking for DeepResearch Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 17837–17856, Budapest, Hungary. Association for Computational Linguistics.