Causal Memory Policy: Making Memory Utility Identifiable by Intervening on Retrieval
Memory-augmented large language models must decide which memories to retain, and recent systems do so by estimating each memory's effect on task performance. However, these estimates rely entirely on retrieved memories. When a memory is never retrieved, store-level interventions produce identical outcomes, leaving its utility unidentified. This is a retrieval-level positivity violation, invisible to diagnostics that examine only memory operations. We introduce Causal Memory Policy (CMP), a causal framework that restores identification by intervening on retrieval itself, reserving a fixed number of context slots for memories sampled with known propensities. CMP estimates memory utility by ...
| Authors | Arman Behnam, Binghui Wang |
|---|---|
| Primary category | cs.AI |
| Published | 1 October 2026 |
| Last updated at source | 8 October 2026 |
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