@inproceedings{chen-etal-2026-retrieve,
title = "To Retrieve or To Think? Cross-Boundary Context Evolution for Multi-hop Complex Reasoning",
author = "Chen, Rubing and
Wang, Jian and
Li, Wenjie and
Wei, Xiao-Yong and
Qing, Li",
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.2385/",
pages = "51247--51259",
ISBN = "979-8-89176-462-0",
abstract = "Current context augmentation methods, such as retrieval-augmented generation, play a crucial role in bridging a model{'}s internal knowledge boundary and external evidence for multi-hop reasoning. However, they often follow a rigid policy and treat external retrieval as the default action at each step. Such brute-force context expansion incurs unnecessary computational cost and may degrade reasoning performance by saturating the context with redundant or weakly relevant evidence. In this paper, we propose Cross-Boundary Context Evolution (EvoCtx), a framework that models complex reasoning as an adaptive process of boundary-aware context evolution. EvoCtx dynamically decides whether the next reasoning transition should cross the current evidence boundary through retrieval or refine the reasoning state within the existing context. It estimates the semantic gap between the reasoning state and the accumulated evidence, and strategically alternates between boundary expansion and intra-boundary trajectory refinement. This eliminates redundant retrieval steps and preserves a compact, evidence-supported reasoning trajectory. Extensive experiments on challenging open-domain and multi-hop QA benchmarks demonstrate that EvoCtx significantly outperforms previous methods, offering an effective approach to complex tasks. The source code can be accessed at https://github.com/Anya-RB-Chen/EvoCtx."
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<abstract>Current context augmentation methods, such as retrieval-augmented generation, play a crucial role in bridging a model’s internal knowledge boundary and external evidence for multi-hop reasoning. However, they often follow a rigid policy and treat external retrieval as the default action at each step. Such brute-force context expansion incurs unnecessary computational cost and may degrade reasoning performance by saturating the context with redundant or weakly relevant evidence. In this paper, we propose Cross-Boundary Context Evolution (EvoCtx), a framework that models complex reasoning as an adaptive process of boundary-aware context evolution. EvoCtx dynamically decides whether the next reasoning transition should cross the current evidence boundary through retrieval or refine the reasoning state within the existing context. It estimates the semantic gap between the reasoning state and the accumulated evidence, and strategically alternates between boundary expansion and intra-boundary trajectory refinement. This eliminates redundant retrieval steps and preserves a compact, evidence-supported reasoning trajectory. Extensive experiments on challenging open-domain and multi-hop QA benchmarks demonstrate that EvoCtx significantly outperforms previous methods, offering an effective approach to complex tasks. The source code can be accessed at https://github.com/Anya-RB-Chen/EvoCtx.</abstract>
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%0 Conference Proceedings
%T To Retrieve or To Think? Cross-Boundary Context Evolution for Multi-hop Complex Reasoning
%A Chen, Rubing
%A Wang, Jian
%A Li, Wenjie
%A Wei, Xiao-Yong
%A Qing, Li
%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 chen-etal-2026-retrieve
%X Current context augmentation methods, such as retrieval-augmented generation, play a crucial role in bridging a model’s internal knowledge boundary and external evidence for multi-hop reasoning. However, they often follow a rigid policy and treat external retrieval as the default action at each step. Such brute-force context expansion incurs unnecessary computational cost and may degrade reasoning performance by saturating the context with redundant or weakly relevant evidence. In this paper, we propose Cross-Boundary Context Evolution (EvoCtx), a framework that models complex reasoning as an adaptive process of boundary-aware context evolution. EvoCtx dynamically decides whether the next reasoning transition should cross the current evidence boundary through retrieval or refine the reasoning state within the existing context. It estimates the semantic gap between the reasoning state and the accumulated evidence, and strategically alternates between boundary expansion and intra-boundary trajectory refinement. This eliminates redundant retrieval steps and preserves a compact, evidence-supported reasoning trajectory. Extensive experiments on challenging open-domain and multi-hop QA benchmarks demonstrate that EvoCtx significantly outperforms previous methods, offering an effective approach to complex tasks. The source code can be accessed at https://github.com/Anya-RB-Chen/EvoCtx.
%U https://aclanthology.org/2026.findings-emnlp.2385/
%P 51247-51259
Markdown (Informal)
[To Retrieve or To Think? Cross-Boundary Context Evolution for Multi-hop Complex Reasoning](https://aclanthology.org/2026.findings-emnlp.2385/) (Chen et al., Findings 2026)
ACL