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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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Source

Plain-Language Brief

Researchers have introduced HarnessOpt-Bench, a benchmark to measure the performance of Large Language Models (LLMs) in optimizing their own harnesses (the prompts, tools, etc. surrounding them). The research shows that LLMs can separate themselves from their coding harnesses, but the results vary significantly across tasks and seed regimes.

What the Document Says

  • HarnessOpt-Bench is a benchmark for evaluating end-to-end harness optimization by AI systems.
  • An optimizer (LLM paired with a coding harness) receives a seed harness, graded evaluation feedback, and a fixed target-evaluation budget to edit the harness and nominate a final candidate.
  • The final candidate is scored by its normalized gain over the seed on a held-out test partition.
  • A trusted execution environment enforces the evaluation boundary, meters resource use, and preserves candidate versions for audit.
  • 5 frontier LLMs were evaluated as optimizers under a shared coding harness and under their native harnesses across 4 downstream tasks, over 111 scored runs.
  • Results show that optimizer models separate more than the coding harnesses they act through.
  • Native harnesses are not consistently superior.
  • Gains vary substantially across tasks and seed regimes.

Key Concepts Mentioned

  • Large Language Models (LLMs)
  • HarnessOpt-Bench
  • End-to-end harness optimization
  • Coding harness
  • Trusted execution environment
  • Evaluation budget
  • Normalized gain
  • Seed harness
  • Graded evaluation feedback

Claims Made by the Document

  • LLMs can separate themselves from their coding harnesses.
  • Native harnesses are not consistently superior to other harnesses.
  • Gains vary substantially across tasks and seed regimes.

Evidence Used

  • Results from the evaluation of 5 frontier LLMs as optimizers over 111 scored runs.

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.