AI & ML
When Instruction Files Go Stale: Documentation Debt and Hidden Cost in Coding Agents
arXiv cs.SE0 views
arXiv:2611.09802v1 Announce Type: new Abstract: LLM-based coding agents such as Claude Code are guided by repository instruction files (also called context files or agent configuration files) like AGENTS.md and CLAUDE.md, which are read in every agent session. As any documentation artifact, these files can fall behind the code and become stale (e.g., a test folder moves, but the file still points to the old one). In technical debt terms, such a stale instruction is documentation debt, and its interest is the cost it adds to every later agent session. Evaluations that check only task success can miss this cost, and the stale file can persist. In this study, we ask what leads agents to act on a stale instruction, how it affects their behavior and at what cost, and how they recover. We study three real-world stale instructions from active open-source repositories in depth, each a different kind of guidance: a test command, a code location, and a procedure for adding a component. Agents fix 22 maintenance tasks under three versions of each instruction (left stale, corrected to the current path, and removed), with the code and the task held fixed, and we analyze 732 trajectories of two models at the level of individual tool calls. We find that agents follow stale guidance when it is relevant to their tasks, sometimes even when correct information is available elsewhere. On the stale test command, Claude Sonnet 5.5 needed about two extra tool calls per run, a third more than with the corrected command; in the other cases, we could not detect a difference. Although agents generally recovered from the immediate failures, none repaired the underlying instruction file. Correcting the instructions eliminated operations on obsolete paths, while removing them prevented most. Our findings show that task success alone can hide the costs of documentation debt, highlighting the need to evaluate agent behavior beyond final outcomes.
Source:https://arxiv.org/abs/2611.09802PreviousAnimWeb-Bench: Benchmarking Animated Webpage Code Generation from VideosNextAgents on the Patch Path: Mining AI-Associated Security Remediation in GitHub Security Advisories
Related posts
DeepRitzSplit: A Variational Neural Operator Framework for Gradient Flows with Application to Dendritic Growth
0arXiv cs.NA
Quantitative Universal Approximation for Noisy Quantum Neural Networks
0arXiv cs.NA
Vertex interpolation by bivariate $C^1$ cubic splines: counterexamples, geometric stability, and indecomposable defects
0arXiv cs.NA
Adaptive refinement based on local approximation power of hierarchical spline spaces
0arXiv cs.NA
Discrete hypocoercive estimates for discontinuous Galerkin methods: application to the Vlasov-Poisson-Fokker-Planck system
0arXiv cs.NA
Novel technique based on L\'eja Points Approximation for Log-determinant Estimation of Large matrices
0arXiv cs.NA