Prediction Is Not Protection: What Models Forget During Context Compaction

Long-running LLM agents replace old conversations with compact memory. Protecting selected facts can improve overall recall while making information left unprotected less reliable. We study this tension through a paired evaluation that links each fact's pre-compaction forecast, untreated omission, and outcome after key-only protection. With supplied importance labels and controlled fact budgets, gains in overall important-fact recall coexist with losses on the same unprotected facts under self-selected, random, and cue-based policies. At the widest memory-loop budget, external protection retains 76–93% of raw-log recall at 10–29% of its storage, but a simple repetition rule remains competitive; self-ranking is model-dependent. Naturalistic tests qualify these findings. LoCoMo establishes neither general cue superiority nor a best selector. On 12 Multi-Session Chat dialogues at approximately matched measured storage, total point gains occur without detectable unprotected-fact loss. Additional key-free controls do not establish a benefit from stronger wording; self-selection retains a nominal gain over plain compaction. These limited-source results do not establish the absence of collateral cost or a universal selector advantage. Prediction accuracy, overall recall, and reliability on the same unprotected facts therefore require separate evaluation.