arXiv:2605.21516v1 Announce Type: new Abstract: Harness engineering has emerged as an important inference-time technique for large language model (LLM) agents, aiming to improve long-term performance through task decomposition and guided execution. However, more elaborate harnesses are not uniformly better: increasing decomposition or guidance can sometimes improve execution, but can also reduce final task success. We study harness design through the lens of inference-time trajectory alignment. This perspective separates harness into two mechanisms: task decomposition, which structures a task
The rapid advancement of large language models is driving the necessity for more sophisticated inference-time control mechanisms to improve reliability and performance.
Improving the control and reliability of LLM agents through harness engineering is critical for their adoption in complex, real-world applications across various sectors.
This research provides a more formal and nuanced understanding of how to design effective 'harnesses' for LLM agents, moving beyond simple decomposition to trajectory alignment.
- · AI developers
- · Businesses adopting LLM agents
- · AI-as-a-Service providers
- · Companies with unreliable or poorly structured LLM agent deployments
Improved performance and reliability of AI agents in task execution.
Accelerated deployment of autonomous AI agents in sensitive and high-value workflows.
Enhanced automation of white-collar tasks, leading to efficiency gains and potential workforce restructuring.
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Read at arXiv cs.LG