HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness
Summary
Introduces HarnessBridge, a learnable bidirectional controller that parameterizes the agent-environment interface for LLM agents, achieving performance comparable to specialized harnesses with reduced computational overhead on Terminal-Bench and SWE-bench.
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Paper page - HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness
Source: https://huggingface.co/papers/2606.12882
Abstract
Learnable harness controller called HarnessBridge is introduced to parameterize agent-environment interfaces through bidirectional projections, achieving performance comparable to specialized harnesses with reduced computational overhead.
Large language modelsare 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 can be generated by a learnable plug-in module that can be trained in an end-to-end fashion. We introduce HarnessBridge, a lightweightlearnable harness controllerthat parameterizes the agent--environment interface as abidirectional projection. HarnessBridge learns twobidirectional projections:observation projection, which distills raw trajectories into compact, decision-relevant states, andaction projection, which converts proposed actions into executable transitions or trajectory-grounded rejections. We train HarnessBridge on a harness supervision dataset via unifiedinstruction tuning. OnTerminal-Bench~2.0 andSWE-benchVerified, HarnessBridge matches or surpasses strong specialized harnesses while substantially reducing token usage and trajectory length, and generalizes from smaller generators to larger commercial models.
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