@inproceedings{mao-etal-2026-egss,
title = "{EGSS}: Entropy-guided Stepwise Scaling for Reliable Software Engineering",
author = "Mao, Chenhui and
Lei, Yuanting and
Wei, Zhixiang and
Liang, Ming 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 = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1359/",
pages = "29481--29499",
ISBN = "979-8-89176-390-6",
abstract = "Agentic Test-Time Scaling (TTS) has delivered state-of-the-art (SOTA) performance on complex software engineering tasks such as code generation and bug fixing. However, its practical adoption remains limited due to significant computational overhead, primarily driven by two key challenges: (1) the high cost associated with deploying excessively large ensembles, and (2) the lack of a reliable mechanism for selecting the optimal candidate solution{---}ultimately constraining the performance gains that can be realized. To address these challenges, we propose Entropy-Guided Stepwise Scaling (EGSS), a novel TTS framework that dynamically balances efficiency and effectiveness through entropy-guided adaptive search and robust test-suite augmentation.Extensive experiments on SWE-Bench-Verified demonstrate that EGSS consistently boosts performance by 5{--}10{\%} across all evaluated models. Specifically, it increases the resolved ratio of Kimi-K2-Intruct from 63.2{\%} to 72.2{\%}, and GLM-4.6 from 65.8{\%} to 74.6{\%}. Furthermore, when paired with GLM-4.6, EGSS achieves new state-of-the-art among open-source large language models. In addition to these accuracy improvements, EGSS reduces inference-time token usage by over 28{\%} compared to existing TTS methods, achieving simultaneous gains in both effectiveness and computational efficiency."
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<abstract>Agentic Test-Time Scaling (TTS) has delivered state-of-the-art (SOTA) performance on complex software engineering tasks such as code generation and bug fixing. However, its practical adoption remains limited due to significant computational overhead, primarily driven by two key challenges: (1) the high cost associated with deploying excessively large ensembles, and (2) the lack of a reliable mechanism for selecting the optimal candidate solution—ultimately constraining the performance gains that can be realized. To address these challenges, we propose Entropy-Guided Stepwise Scaling (EGSS), a novel TTS framework that dynamically balances efficiency and effectiveness through entropy-guided adaptive search and robust test-suite augmentation.Extensive experiments on SWE-Bench-Verified demonstrate that EGSS consistently boosts performance by 5–10% across all evaluated models. Specifically, it increases the resolved ratio of Kimi-K2-Intruct from 63.2% to 72.2%, and GLM-4.6 from 65.8% to 74.6%. Furthermore, when paired with GLM-4.6, EGSS achieves new state-of-the-art among open-source large language models. In addition to these accuracy improvements, EGSS reduces inference-time token usage by over 28% compared to existing TTS methods, achieving simultaneous gains in both effectiveness and computational efficiency.</abstract>
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%0 Conference Proceedings
%T EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering
%A Mao, Chenhui
%A Lei, Yuanting
%A Wei, Zhixiang
%A Liang, Ming
%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 Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F mao-etal-2026-egss
%X Agentic Test-Time Scaling (TTS) has delivered state-of-the-art (SOTA) performance on complex software engineering tasks such as code generation and bug fixing. However, its practical adoption remains limited due to significant computational overhead, primarily driven by two key challenges: (1) the high cost associated with deploying excessively large ensembles, and (2) the lack of a reliable mechanism for selecting the optimal candidate solution—ultimately constraining the performance gains that can be realized. To address these challenges, we propose Entropy-Guided Stepwise Scaling (EGSS), a novel TTS framework that dynamically balances efficiency and effectiveness through entropy-guided adaptive search and robust test-suite augmentation.Extensive experiments on SWE-Bench-Verified demonstrate that EGSS consistently boosts performance by 5–10% across all evaluated models. Specifically, it increases the resolved ratio of Kimi-K2-Intruct from 63.2% to 72.2%, and GLM-4.6 from 65.8% to 74.6%. Furthermore, when paired with GLM-4.6, EGSS achieves new state-of-the-art among open-source large language models. In addition to these accuracy improvements, EGSS reduces inference-time token usage by over 28% compared to existing TTS methods, achieving simultaneous gains in both effectiveness and computational efficiency.
%U https://aclanthology.org/2026.acl-long.1359/
%P 29481-29499
Markdown (Informal)
[EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering](https://aclanthology.org/2026.acl-long.1359/) (Mao et al., ACL 2026)
ACL
- Chenhui Mao, Yuanting Lei, Zhixiang Wei, Ming Liang, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, and Yong Li. 2026. EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 29481–29499, San Diego, California, United States. Association for Computational Linguistics.