Agentic prompts grow unbounded, tripling over repository lifetimes
Source published ·Modelwire updated
Original coverage: arXiv cs.LG ↗·How Modelwire adds context
The development
Researchers have identified a structural problem in how AI agents maintain instruction files: prompts like CLAUDE.md grow unbounded across repository lifetimes because deleting old instructions risks breaking undocumented dependencies, while appending new ones is cheap. The study of 247,694 instructions across 1,867 repos shows agentic prompts triple in size over time, gaining 4.9 net instructions per commit. This 'catastrophic remembering' mirrors but inverts the continual learning problem, revealing a fundamental tension in how deployed agents accumulate context. The finding matters for production AI systems where prompt bloat degrades performance and maintainability without clear recovery paths.
Modelwire’s AI-generated summary of coverage from arXiv cs.LG.
Modelwire analysis
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The paper identifies a one-way ratchet: agents accumulate instructions because deletion is risky (undocumented dependencies), but addition is cheap. This isn't just technical debt; it's a structural incentive problem baked into how agentic systems are deployed and maintained.
This connects directly to the self-evolving GUI agents work from earlier this month, which proposed continuous adaptation without human supervision. That framework assumed agents could internalize improvements, but this study reveals the flip side: in practice, deployed agents don't clean up after themselves. They append. The TrustNLP workshop analysis also noted a shift from post-hoc interpretability toward active control of generative systems, but catastrophic remembering suggests that control breaks down once systems start self-modifying in production. The gap between what we want (clean, auditable reasoning paths) and what we get (bloated, opaque instruction files) mirrors the cross-lingual policy retention problem: we measure final outputs while ignoring the actual traces agents leave behind.
If teams using agentic coding systems report that prompt compression or instruction pruning tools gain adoption in the next 6 months, that signals the finding has moved from observation to operational pain. Conversely, if instruction file sizes continue to grow unchecked in public repos through 2027, it suggests the field hasn't internalized the maintenance cost yet.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.CL
GUI agents learn to improve themselves after deployment without human feedback
Researchers propose a self-improving framework for GUI agents that adapts after deployment without human supervision. The system creates a feedback loop where agents explore interfaces, evaluate their own outputs using multimodal language models, reflect on failures, and internalize improvements. This addresses a critical gap in production AI systems: most deployed models remain static despite encountering…
MentionsCLAUDE.md · arXiv
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Why Does CLAUDE.md Keep Growing? Catastrophic Remembering in Agentic Coding”. 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.