HarnessOpt-Bench evaluates LLM harness optimization
The arXiv benchmark tests how frontier LLMs improve agent prompts, tools, control flow and memory.
Why it matters
The work reflects a shift in agent evaluation from model weights alone to the surrounding prompts, tools, memory, control flow and orchestration code. A common protocol could make claims about automated agent self-improvement easier to compare and audit.
The key points
- 1.Benchmarks end-to-end LLM-driven harness optimization.
- 2.Scores gains over a seed harness on held-out tests.
- 3.Trusted execution meters resources and preserves audit versions.
Researchers introduced HarnessOpt-Bench, a benchmark for evaluating end-to-end harness optimization in LLM agent systems. In the protocol, an LLM optimizer receives a target agent's seed harness, graded evaluation feedback and a fixed target-evaluation budget, then edits the harness and nominates a final candidate. The final score measures normalized gain over the seed on an inaccessible held-out test partition, with a trusted execution environment enforcing evaluation boundaries, metering resource use and preserving versions for audit.
⚡ Try this today
Use held-out evaluations and versioned audit trails when testing automated prompt, tool or agent-harness optimization.
Sources & original reporting
This brief summarizes and links to reporting from the publishers below.
- arXiv cs.AIHarnessOpt-Bench: Evaluating LLMs at Harness OptimizationAug 7, 12:00 PM↗
- arXiv cs.LGHarnessOpt-Bench: Evaluating LLMs at Harness OptimizationAug 7, 12:00 PM↗
- arXiv cs.CLHarnessOpt-Bench: Evaluating LLMs at Harness OptimizationAug 7, 12:00 PM↗
- arXiv cs.LGEvolveNet: Collaborative Harness Evolution for Agent Self-ImprovementAug 6, 12:00 PM↗
- HF Daily PapersHarnessOpt-Bench: Evaluating LLMs at Harness OptimizationAug 6, 4:00 AM↗
- arXiv cs.AIWeClawArena: An Auditable Sandbox and Benchmark for Cross-User Agents Collaboration and Security in Human-Centered Agent NetworksAug 5, 12:00 PM↗
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