@inproceedings{peng-etal-2026-edit4agency,
title = "{E}dit4{A}gency: Efficiently Enhancing Agent Sub-Capabilities of Small Language Models via Model Editing",
author = "Peng, Jianxiang and
Chen, Yue and
Li, Zhigen and
Wang, Yanmeng and
Cheng, Ning and
Wang, Shaojun and
Ren, Yuqi and
Xiong, Deyi",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.127/",
pages = "2183--2196",
ISBN = "979-8-89176-460-6",
abstract = "Currently, most agentic systems are driven by mainstream closed-source large language models (LLMs), such as GPT-5 and Gemini 3, resulting in high operational costs. As a result, deploying customized small language models (SLMs) to replace LLMs in agent components has attracted increasing attention. However, the limited capabilities of SLMs often render them inadequate for agent tasks. To address this, we propose $\textbf{Edit4Agency}$, a novel framework that utilizes model editing to efficiently and effectively enhance agent sub-capabilities of SLMs. By formalizing tasks as a sequence generation problem triggered by specific conditions, the framework embeds task triggers into system prompts and performs model editing through localized parameter updates, eliminating the need for large-scale retraining. Extensive experiments across multiple datasets and case studies in agent systems demonstrate that model editing, with only 50 data samples, enables a base model without instruction tuning to acquire new skills such as tool calling while maintaining its general capabilities. The results show that Edit4Agency outperforms instruction-tuned SLMs, constrained decoding, and SLMs trained with large-scale supervised fine-tuning or reinforcement learning methods."
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<abstract>Currently, most agentic systems are driven by mainstream closed-source large language models (LLMs), such as GPT-5 and Gemini 3, resulting in high operational costs. As a result, deploying customized small language models (SLMs) to replace LLMs in agent components has attracted increasing attention. However, the limited capabilities of SLMs often render them inadequate for agent tasks. To address this, we propose Edit4Agency, a novel framework that utilizes model editing to efficiently and effectively enhance agent sub-capabilities of SLMs. By formalizing tasks as a sequence generation problem triggered by specific conditions, the framework embeds task triggers into system prompts and performs model editing through localized parameter updates, eliminating the need for large-scale retraining. Extensive experiments across multiple datasets and case studies in agent systems demonstrate that model editing, with only 50 data samples, enables a base model without instruction tuning to acquire new skills such as tool calling while maintaining its general capabilities. The results show that Edit4Agency outperforms instruction-tuned SLMs, constrained decoding, and SLMs trained with large-scale supervised fine-tuning or reinforcement learning methods.</abstract>
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%0 Conference Proceedings
%T Edit4Agency: Efficiently Enhancing Agent Sub-Capabilities of Small Language Models via Model Editing
%A Peng, Jianxiang
%A Chen, Yue
%A Li, Zhigen
%A Wang, Yanmeng
%A Cheng, Ning
%A Wang, Shaojun
%A Ren, Yuqi
%A Xiong, Deyi
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F peng-etal-2026-edit4agency
%X Currently, most agentic systems are driven by mainstream closed-source large language models (LLMs), such as GPT-5 and Gemini 3, resulting in high operational costs. As a result, deploying customized small language models (SLMs) to replace LLMs in agent components has attracted increasing attention. However, the limited capabilities of SLMs often render them inadequate for agent tasks. To address this, we propose Edit4Agency, a novel framework that utilizes model editing to efficiently and effectively enhance agent sub-capabilities of SLMs. By formalizing tasks as a sequence generation problem triggered by specific conditions, the framework embeds task triggers into system prompts and performs model editing through localized parameter updates, eliminating the need for large-scale retraining. Extensive experiments across multiple datasets and case studies in agent systems demonstrate that model editing, with only 50 data samples, enables a base model without instruction tuning to acquire new skills such as tool calling while maintaining its general capabilities. The results show that Edit4Agency outperforms instruction-tuned SLMs, constrained decoding, and SLMs trained with large-scale supervised fine-tuning or reinforcement learning methods.
%U https://aclanthology.org/2026.emnlp-industry.127/
%P 2183-2196
Markdown (Informal)
[Edit4Agency: Efficiently Enhancing Agent Sub-Capabilities of Small Language Models via Model Editing](https://aclanthology.org/2026.emnlp-industry.127/) (Peng et al., EMNLP 2026)
ACL
- Jianxiang Peng, Yue Chen, Zhigen Li, Yanmeng Wang, Ning Cheng, Shaojun Wang, Yuqi Ren, and Deyi Xiong. 2026. Edit4Agency: Efficiently Enhancing Agent Sub-Capabilities of Small Language Models via Model Editing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2183–2196, Budapest, Hungary. Association for Computational Linguistics.