Testing Memory Placement Against Compaction Cliff

A University of Passau study found that repeated AI-agent context compaction can sharply reduce retention of safety constraints, motivating an experiment on whether rule placement matters more than compaction quality. Using a synthetic payments repository and ten rules, the authors compared rules placed only in working context, stored in a CLAUDE.md file, or retrieved through Mem0; after excluding three flawed quiz items, rules in working context declined from perfect initial performance to three or four correct answers out of seven after three compactions, while file-based and retrieval-based memory largely retained the rules. In this setup, most compactions failed to produce intended summaries and instead discarded context, so the results reproduced the outcome of rule loss rather than the gradual paraphrasing mechanism described in prior research. Both external-memory approaches consistently preserved a critical fact absent from the codebase—that staging shares production credentials—whereas agents without such memory repeatedly judged the configuration broadly safe. The experiment found that a simple CLAUDE.md matched retrieval for a small, stable set of always-relevant rules, while retrieval may become preferable for larger, scoped, or cross-tool knowledge bases; the authors note that broader tests are needed to identify when retrieval failures and file-context costs emerge.

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