Why Does CLAUDE.md Keep Growing? Catastrophic Remembering in Agentic Coding
On the map: LLM agents and tool use → Agent memory systems
Abstract (arXiv)
Agentic coding READMEs like CLAUDE.md grow without bound in real repositories, stopping only when the repository retires or someone rewrites the file wholesale. We trace this to imperfect recall: appending an instruction is always cheap, but once an instruction's rationale is gone, deleting it without risking a correctness regression costs O(2^|D|) in a prompt of |D| instructions. We name the resulting divergence catastrophic remembering, the inverse of catastrophic forgetting around which continual learning is organized. First, we characterize this phenomenon across 247,694 instruction lifetimes in 1,867 repositories: agentic prompts grow without bound, more than tripling over their lifetime (+226%), gaining +4.9 net instructions every commit; further, the older an instruction gets, the less likely it is to be deleted (log-hazard -0.032/commit). Then, we show that prompt comments can halt the growth: inverting IFEval yields verifiable worlds whose optimal prompts are known, and there comments encoding latent reasoning remove 99.3% of excess instructions (+211.3% to +1.4%). Finally, applying the same inversion to WildIFEval, we show that prompt comments can improve real-world agentic instruction-following by up to 23.1%. If English is the new code, why don't we have comments yet?
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 15 of its 43 references that are in the map. First posted on arXiv 11 Aug 2026.
Builds on
15 of 43 references.
Closest ancestors · 2
Same direction, newest first
- 2026FSFM: A Biologically-Inspired Framework for Selective Forgetting of Agent Memory22 Apr
- 2025Zep: A Temporal Knowledge Graph Architecture for Agent Memory20 Jan
Other references · 13
- 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
- 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
- 2021Learning to Prompt for Continual Learningcited by 938
- 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
- 2022DualPrompt: Complementary Prompting for Rehearsal-free Continual Learningcited by 635
- 2022CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learningcited by 446
- 2020Learning to Update Natural Language Comments Based on Code Changescited by 30
- 2025Agent READMEs: An Empirical Study of Context Files for Agentic Codingcited by 21
- 2020Deep Just-In-Time Inconsistency Detection Between Comments and Source Codecited by 20
- 2022What Makes a Good Commit Message?cited by 19
- 2024Prompting in the Wild: An Empirical Study of Prompt Evolution in Software Repositoriescited by 16
- 2026On the Impact of AGENTS.md Files on the Efficiency of AI Coding Agentscited by 16
1 more
- 2025WildIFEval: Instruction Following in the Wildcited by 10
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Citing papers per year: 2026: 2.
2 papers.
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Close in wording, no citation either way
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