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.
Nex- n1: Agentic models trained via a unified ecosystem for large-scale environment construction, December 2025
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
LiteCoder-Terminal-Gen creates synthetic terminal datasets that, after SFT and DMPO on Qwen models, yield 29.06%, 18.54%, and 34.00% pass@1 on Terminal Bench 1.0, 2.0, and Pro.
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.
citing papers explorer
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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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LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agents
LiteCoder-Terminal-Gen creates synthetic terminal datasets that, after SFT and DMPO on Qwen models, yield 29.06%, 18.54%, and 34.00% pass@1 on Terminal Bench 1.0, 2.0, and Pro.
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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.