@inproceedings{feng-etal-2026-arm,
title = "{ARM}: Role-Conditioned Neuron Transplantation for Training-Free Generalist {LLM} Agent Merging",
author = "Feng, Zhuoka and
Chen, Kang and
Zhao, Sihan and
Xiong, Kai and
Wang, Yaoning and
Minshen, Yu and
Nian, Junjie and
Xiao, Changyi and
Cao, Yixin and
Jiang, Yu-Gang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2115/",
pages = "48104--48118",
ISBN = "979-8-89176-459-0",
abstract = "Interactive large language model agents are often specialized to a single environment and transfer poorly across others. We study training-free consolidation of such specialists into a single generalist checkpoint. A key difficulty in this setting is that, in multi-turn agents, small merge errors on role-critical outputs can trigger long-horizon failure cascades. We therefore propose Agent-Role Merging (ARM), a neuron-level training-free framework that selects a merged backbone with an answer-free activation-overlap signal and then repairs residual gaps through conflict-aware transplantation of a small set of role-salient neurons. Our analyses further show that role-conditioned tracing yields more benchmark-specific neuron sets, enabling more localized repair. Across Qwen3-8B and Qwen2.5-7B expert pools, ARM produces the strongest balanced merged generalist among compared training-free baselines, improving both average performance and worst-suite robustness while preserving out-of-domain generalization. These results suggest that targeted neuron-level edits offer a practical route to robust multi-turn agent merging without end-to-end retraining."
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<abstract>Interactive large language model agents are often specialized to a single environment and transfer poorly across others. We study training-free consolidation of such specialists into a single generalist checkpoint. A key difficulty in this setting is that, in multi-turn agents, small merge errors on role-critical outputs can trigger long-horizon failure cascades. We therefore propose Agent-Role Merging (ARM), a neuron-level training-free framework that selects a merged backbone with an answer-free activation-overlap signal and then repairs residual gaps through conflict-aware transplantation of a small set of role-salient neurons. Our analyses further show that role-conditioned tracing yields more benchmark-specific neuron sets, enabling more localized repair. Across Qwen3-8B and Qwen2.5-7B expert pools, ARM produces the strongest balanced merged generalist among compared training-free baselines, improving both average performance and worst-suite robustness while preserving out-of-domain generalization. These results suggest that targeted neuron-level edits offer a practical route to robust multi-turn agent merging without end-to-end retraining.</abstract>
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%0 Conference Proceedings
%T ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging
%A Feng, Zhuoka
%A Chen, Kang
%A Zhao, Sihan
%A Xiong, Kai
%A Wang, Yaoning
%A Minshen, Yu
%A Nian, Junjie
%A Xiao, Changyi
%A Cao, Yixin
%A Jiang, Yu-Gang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F feng-etal-2026-arm
%X Interactive large language model agents are often specialized to a single environment and transfer poorly across others. We study training-free consolidation of such specialists into a single generalist checkpoint. A key difficulty in this setting is that, in multi-turn agents, small merge errors on role-critical outputs can trigger long-horizon failure cascades. We therefore propose Agent-Role Merging (ARM), a neuron-level training-free framework that selects a merged backbone with an answer-free activation-overlap signal and then repairs residual gaps through conflict-aware transplantation of a small set of role-salient neurons. Our analyses further show that role-conditioned tracing yields more benchmark-specific neuron sets, enabling more localized repair. Across Qwen3-8B and Qwen2.5-7B expert pools, ARM produces the strongest balanced merged generalist among compared training-free baselines, improving both average performance and worst-suite robustness while preserving out-of-domain generalization. These results suggest that targeted neuron-level edits offer a practical route to robust multi-turn agent merging without end-to-end retraining.
%U https://aclanthology.org/2026.emnlp-main.2115/
%P 48104-48118
Markdown (Informal)
[ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging](https://aclanthology.org/2026.emnlp-main.2115/) (Feng et al., EMNLP 2026)
ACL
- Zhuoka Feng, Kang Chen, Sihan Zhao, Kai Xiong, Yaoning Wang, Yu Minshen, Junjie Nian, Changyi Xiao, Yixin Cao, and Yu-Gang Jiang. 2026. ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 48104–48118, Budapest, Hungary. Association for Computational Linguistics.