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About this paper
Xiaoxuan Wang, Haixin Wang, Alexander Taylor, Jason Cong, Yizhou Sun, Wei Wang
HarnessBridge, developed by UCLA researchers, introduces a learnable bidirectional controller that optimizes the interface between large language model agents and their environments. It achieves substantial reductions in token usage, such as 90.7% for GPT-5.4-Nano, and improves task success rates, including an 11.2 percentage point gain on Terminal-Bench 2.0 for Qwen3.5-35B-A3B, by intelligently distilling context and regulating agent actions.
Key insights
- The interface between an LLM agent and its environment can be optimized as a learnable, dynamic component rather than fixed, static infrastructure.
- Intelligently distilling context and providing a persistent "active-state index" significantly reduces noise and token cost while preserving decision-critical information.
- Regulating proposed agent actions and providing grounded feedback prevents inefficient environment steps, guiding the agent toward more effective task completion.
Summary by alphaXiv.
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