arXiv Machine Learning By Asa Shepard, Jeannie Albrecht

Probe-and-Refine Tuning of Repository Guidance for Coding Agents

Read the original on arXiv Machine Learning →

arXiv:2606. 20512v1 Announce Type: cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to run the test suite, which workflows have historically led to wrong fixes) that does not exist in the code itself.

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arXiv AI
Aug 28

Same Model, Different Harness: Different Coding-Agent Results

The paper investigates how altering the harness—specifically the way a coding agent manages context and tool outputs—affects performance when the underlying model and task remain unchanged. Two harness configurations were compared on three coding benchmarks: a control that preserves the full conversation in order, and a treatment that mechanically shortens older tool results to keep the context tight. Across all benchmarks, the treatment increased the mean per‑task fail‑to‑pass fraction and, in some cases, the number of complete solutions, demonstrating that the harness itself can significantly influence a frozen model’s effectiveness.

By Sydney Lewis