From Recurrence to Consequence: Risk-Aware One-Shot Memory Consolidation for Long-Term LLM Agents

Long-term memory is essential for LLM agents operating across multi-session interactions, but existing memory systems primarily optimize for information that is frequent, salient, or generally useful. This leaves a critical failure mode: an interaction may appear only once, yet forgetting it can lead to unsafe, non-compliant, or irreversible downstream actions. We study this setting as rare-but-critical one-shot memory, where the value of a memory is determined not by recurrence, but by the consequence of forgetting. We first show that recurrence-driven systems exhibit a substantial one-shot consolidation gap, while broad-admission methods improve recall at the cost of false promotion, over-triggering, and large operational overhead. To address this problem, we propose ROMC, a risk-aware memory consolidation framework that promotes one-shot information into critical memory when its omission may cause high-impact future failures. ROMC estimates consequence-aware risk using structured risk dimensions and counterfactual future probes, and distills this teacher judgment into lightweight student scorers for efficient write-side promotion and read-side activation. Unlike standard memory QA, we further introduce an action-oriented downstream evaluation that requires agents to generate safe operational advice or alternatives under remembered constraints. Experiments show that ROMC achieves the best overall downstream correctness among deployable methods, while substantially reducing over-triggering, injected constraints, and token cost. These results suggest that long-term agent memory should move beyond remembering what recurs, toward consolidating what would be costly to forget.