Probe-and-Refine Tuning of Repository Guidance for Coding Agents
Conflicting evidence on whether repository guidance helps LLM coding agents has stalled adoption, but new research isolates the real variable: guidance quality itself. Probe-and-refine tuning uses synthetic bug-fix scenarios to diagnose and iteratively improve AGENTS.md files through lightweight LLM calls, bypassing expensive agent loops. This shifts the bottleneck from agent architecture to guidance authoring, making repository context a tunable lever rather than a static artifact. For teams deploying coding agents at scale, the finding reframes guidance as a first-class optimization target.
Modelwire context
ExplainerThe paper isolates guidance quality as a tunable variable separate from agent architecture itself, but the real novelty is the probe-and-refine method: using cheap synthetic diagnostics to iteratively improve repository context without running full agent loops. This flips the cost structure of agent optimization.
This work sits alongside two other agent infrastructure papers from the same week. LedgerAgent (cs.CL, June 18) tackled implicit state management as a reliability bottleneck in multi-turn deployments. Sovereign Execution Brokers (cs.LG, June 18) addressed authorization drift in mutation operations. Together, these three papers signal a shift in how the field thinks about production agents: not as monolithic reasoning systems, but as compositions of separable, tunable layers (state, authorization, guidance). Probe-and-refine tuning treats guidance authoring as a first-class optimization target, much as LedgerAgent treats state transparency and Sovereign Brokers treat execution boundaries.
If teams adopting this method report that probe-and-refine tuning reduces the iteration cycles needed to ship a coding agent by more than 50% compared to trial-and-error guidance authoring, the finding has real adoption legs. If the same teams report that guidance quality plateaus after 3-5 refinement cycles (suggesting diminishing returns), that's the practical ceiling to watch for.
This analysis is generated by Modelwire’s editorial layer from our archive and the summary above. It is not a substitute for the original reporting. How we write it.
MentionsLLM-based coding agents · AGENTS.md · probe-and-refine tuning
Modelwire Editorial
This synthesis and analysis was prepared by the Modelwire editorial team. We use advanced language models to read, ground, and connect the day’s most significant AI developments, providing original strategic context that helps practitioners and leaders stay ahead of the frontier.
Modelwire summarizes, we don’t republish. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.