AHE automates coding-agent harness evolution via component, experience, and decision observability, raising Terminal-Bench 2 pass@1 from 69.7% to 77.0% with cross-benchmark and cross-model transfer.
Codex cli, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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The Binding Constraint Thesis states that harness configuration governs performance variance more than model choice in long-horizon agent tasks, leading to misattribution in evaluations.
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Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses
AHE automates coding-agent harness evolution via component, experience, and decision observability, raising Terminal-Bench 2 pass@1 from 69.7% to 77.0% with cross-benchmark and cross-model transfer.
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Stop Comparing LLM Agents Without Disclosing the Harness
The Binding Constraint Thesis states that harness configuration governs performance variance more than model choice in long-horizon agent tasks, leading to misattribution in evaluations.