@inproceedings{xu-etal-2026-fusesearch,
title = "{F}use{S}earch: Learning Adaptive Parallel Execution for Efficient Code Localization",
author = "Xu, Ke and
Xiao, Siyang and
Liang, Ming and
Yu, Yichen and
Wang, Zhixiang and
Xu, Jingxuan and
Chen, Dajun and
Jiang, Wei and
Li, Yong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.143/",
pages = "2954--2967",
ISBN = "979-8-89176-395-1",
abstract = "Code localization is a primary bottleneck in automated software development. While parallel tool execution can accelerate discovery, existing agents suffer from a 34.9{\%} redundant tool invocation rate, negating the benefits of parallelism. We introduce FuseSearch, which reframes parallel code localization as a quality{--}efficiency co-optimization problem. By defining tool efficiency{---}the ratio of novel information gain to total invocations{---}we employ a two-stage SFT and RL pipeline to train models in adaptive parallel strategies. Unlike fixed-breadth methods, FuseSearch dynamically adjusts search breadth based on task context, transitioning from exploration to refinement. On SWE-bench Verified, FuseSearch-4B matches SOTA performance (84.7{\%} file-level and 56.4{\%} function-level $F_1$ scores) while being 93.6{\%} faster, using 67.7{\%} fewer turns and 68.9{\%} fewer tokens. Our findings demonstrate that efficiency-aware training inherently boosts quality by eliminating noisy, redundant signals, enabling high-performance, low-cost localization agents. Code: \url{https://github.com/sxthunder/FuseSearch}"
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<abstract>Code localization is a primary bottleneck in automated software development. While parallel tool execution can accelerate discovery, existing agents suffer from a 34.9% redundant tool invocation rate, negating the benefits of parallelism. We introduce FuseSearch, which reframes parallel code localization as a quality–efficiency co-optimization problem. By defining tool efficiency—the ratio of novel information gain to total invocations—we employ a two-stage SFT and RL pipeline to train models in adaptive parallel strategies. Unlike fixed-breadth methods, FuseSearch dynamically adjusts search breadth based on task context, transitioning from exploration to refinement. On SWE-bench Verified, FuseSearch-4B matches SOTA performance (84.7% file-level and 56.4% function-level F₁ scores) while being 93.6% faster, using 67.7% fewer turns and 68.9% fewer tokens. Our findings demonstrate that efficiency-aware training inherently boosts quality by eliminating noisy, redundant signals, enabling high-performance, low-cost localization agents. Code: https://github.com/sxthunder/FuseSearch</abstract>
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%0 Conference Proceedings
%T FuseSearch: Learning Adaptive Parallel Execution for Efficient Code Localization
%A Xu, Ke
%A Xiao, Siyang
%A Liang, Ming
%A Yu, Yichen
%A Wang, Zhixiang
%A Xu, Jingxuan
%A Chen, Dajun
%A Jiang, Wei
%A Li, Yong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F xu-etal-2026-fusesearch
%X Code localization is a primary bottleneck in automated software development. While parallel tool execution can accelerate discovery, existing agents suffer from a 34.9% redundant tool invocation rate, negating the benefits of parallelism. We introduce FuseSearch, which reframes parallel code localization as a quality–efficiency co-optimization problem. By defining tool efficiency—the ratio of novel information gain to total invocations—we employ a two-stage SFT and RL pipeline to train models in adaptive parallel strategies. Unlike fixed-breadth methods, FuseSearch dynamically adjusts search breadth based on task context, transitioning from exploration to refinement. On SWE-bench Verified, FuseSearch-4B matches SOTA performance (84.7% file-level and 56.4% function-level F₁ scores) while being 93.6% faster, using 67.7% fewer turns and 68.9% fewer tokens. Our findings demonstrate that efficiency-aware training inherently boosts quality by eliminating noisy, redundant signals, enabling high-performance, low-cost localization agents. Code: https://github.com/sxthunder/FuseSearch
%U https://aclanthology.org/2026.findings-acl.143/
%P 2954-2967
Markdown (Informal)
[FuseSearch: Learning Adaptive Parallel Execution for Efficient Code Localization](https://aclanthology.org/2026.findings-acl.143/) (Xu et al., Findings 2026)
ACL
- Ke Xu, Siyang Xiao, Ming Liang, Yichen Yu, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, and Yong Li. 2026. FuseSearch: Learning Adaptive Parallel Execution for Efficient Code Localization. In Findings of the Association for Computational Linguistics: ACL 2026, pages 2954–2967, San Diego, California, United States. Association for Computational Linguistics.