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Source note2 min read

HarnessOpt-Bench: Evaluating LLMs at Harness Optimization

Source

arXiv

arxiv.org/abs/2608.06301v1

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Plain-Language Brief

Researchers introduce HarnessOpt-Bench, a benchmark for evaluating the ability of Large Language Models (LLMs) to optimize their own systems. The study compares five frontier LLMs across four tasks and finds that they are more effective at optimization than coding harnesses they act through, but native harnesses do not consistently outperform, and gains vary significantly across tasks.

What the Document Says

  • Researchers introduce a benchmark called HarnessOpt-Bench for evaluating LLMs' performance in harness optimization.
  • Harness optimization is the iterative improvement of a harness by an AI system using prompts, tools, control flow, memory, and orchestration code surrounding the LLM.
  • The benchmark uses an optimizer (LLM paired with coding harness), graded evaluation feedback, seed harness, target-evaluation budget, and a held-out test partition.
  • Results are based on experiments involving five frontier LLMs, four tasks, and 111 scored runs.
  • The study finds that optimizer models outperform coding harnesses they act through.
  • Native harnesses do not consistently outperform.
  • Gains vary significantly across tasks and seed regimes.

Key Concepts Mentioned

  • HarnessOpt-Bench
  • LLMs
  • harness optimization
  • coding harness
  • graduated evaluation feedback
  • target-evaluation budget
  • held-out test partition
  • optimizer models
  • native harnesses

Claims Made by the Document

  • HarnessOpt-Bench is a benchmark for evaluating LLMs' performance in harness optimization.
  • Optimizer models outperform coding harnesses they act through.
  • Native harnesses do not consistently outperform.

Evidence Used

  • Introducing HarnessOpt-Bench for end-to-end harness optimization under expensive and stochastic evaluation
  • Experiment results show that optimizer models separate more than the coding harnesses they act through

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

As LLMs are increasingly deployed within agentic systems, their capabilities depend not only on the model weights but also on the harness: the prompts, tools, control flow, memory, and orchestration code surrounding them. This makes automated harness optimization -- the iterative and evaluation-guided improvement of a harness by an AI system -- both an important route to improving AI systems and a demanding capability for AI systems themselves. Yet the community lacks a common protocol for measuring how well frontier LLMs perform at this task. We introduce HarnessOpt-Bench, a benchmark for end-to-end harness optimization under expensive and stochastic evaluation. An optimizer, an LLM paired with a coding harness, receives a target agent's seed harness, graded evaluation feedback, and a fixed target-evaluation budget. It edits the harness and nominates a final candidate, which is scored by its normalized gain over the seed on a held-out test partition that remains inaccessible throughout search. A trusted execution environment enforces the evaluation boundary, meters target-agent resource use, and preserves candidate versions for audit. We evaluate 5 frontier LLMs as optimizers both under a shared coding harness and under their native harnesses across 4 downstream tasks, over 111 scored runs. Experiment results show that optimizer models separate more than the coding harnesses they act through, native harnesses are not consistently superior, and gains vary substantially across tasks and seed regimes. These results establish harness optimization as a measurable and discriminative capability with large space for improvement.