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    <abstract>Large language models are increasingly deployed as agents for long-horizon tasks, yet their performance is shaped not only by model capability and environment design, but also by the harness that mediates agent–environment interaction. Existing harnesses are largely manually engineered, making them difficult to scale as trajectories grow longer and interactions become more complex. In this work, we ask whether harness engineering can be formulated as an end-to-end learnable generation problem. We introduce HarnessBridge, a lightweight learnable harness policy that parameterizes the agent–environment interface as a bidirectional projection. HarnessBridge learns two bidirectional projections: observation projection, which distills raw trajectories into compact, decision-relevant states, and action projection, which converts proposed actions into executable transitions or trajectory-grounded rejections. We train HarnessBridge on a harness supervision dataset via unified instruction tuning. On Terminal-Bench 2.0 and SWE-bench Verified, HarnessBridge matches or surpasses strong specialized harnesses while substantially reducing token usage and trajectory length, and generalizes from smaller generators to larger commercial models. Our code is released at: https://anonymous.4open.science/r/HarnessBridge-6CDC.</abstract>
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%0 Conference Proceedings
%T HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness
%A Wang, Xiaoxuan
%A Wang, Haixin
%A Taylor, Alexander K.
%A Cong, Jason
%A Sun, Yizhou
%A Wang, Wei
%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 wang-etal-2026-harnessbridge
%X Large language models are increasingly deployed as agents for long-horizon tasks, yet their performance is shaped not only by model capability and environment design, but also by the harness that mediates agent–environment interaction. Existing harnesses are largely manually engineered, making them difficult to scale as trajectories grow longer and interactions become more complex. In this work, we ask whether harness engineering can be formulated as an end-to-end learnable generation problem. We introduce HarnessBridge, a lightweight learnable harness policy that parameterizes the agent–environment interface as a bidirectional projection. HarnessBridge learns two bidirectional projections: observation projection, which distills raw trajectories into compact, decision-relevant states, and action projection, which converts proposed actions into executable transitions or trajectory-grounded rejections. We train HarnessBridge on a harness supervision dataset via unified instruction tuning. On Terminal-Bench 2.0 and SWE-bench Verified, HarnessBridge matches or surpasses strong specialized harnesses while substantially reducing token usage and trajectory length, and generalizes from smaller generators to larger commercial models. Our code is released at: https://anonymous.4open.science/r/HarnessBridge-6CDC.
%U https://aclanthology.org/2026.findings-emnlp.484/
%P 10141-10167

Markdown (Informal)

[HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness](https://aclanthology.org/2026.findings-emnlp.484/) (Wang et al., Findings 2026)

• HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness (Wang et al., Findings 2026)

ACL

• Xiaoxuan Wang, Haixin Wang, Alexander K Taylor, Jason Cong, Yizhou Sun, and Wei Wang. 2026. HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 10141–10167, Budapest, Hungary. Association for Computational Linguistics.