When to Remember, When to Abstain: Category-Conditioned Retention for Reliable Agent Memory
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Abstract (arXiv)
Persistent agent memory is only as reliable as its retention decision: an assertion weakly supported by its source can be stored and later reused as established fact. We study whether the retention decision should be governed by a confidence bar conditioned on the semantic category of the assertion rather than by a single global threshold, retaining well-evidenced categories liberally while abstaining more aggressively where inference is unreliable. We evaluate this in a deployed cold-start memory pipeline on 100 synthetic personas. The empirical evaluation is motivated by a sharp reliability asymmetry: across 4,715 candidate assertions, only 77.9% of value and belief assertions are supported by their source, versus 96.2% for all other categories. A global confidence threshold cannot separate these: it either admits unsupported value claims or discards well-evidenced ones. Conditioning the threshold on category resolves the tradeoff. In repeated held-out evaluation, a stricter bar on values alone reduces unsupported retentions from 6.2% to 4.0% (an ≈36% relative reduction, modest but consistent across folds) and, as corroborating evidence, preserves an estimated 13 percentage points more coverage (95% CI 9.8--16.0) than a global threshold at comparable retention. Our results suggest that reliable retention depends on the type of assertion, not on confidence alone, and that a category-conditioned threshold can act as a simple, effective form of selective prediction at the write boundary.
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References · 7
- 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
- 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
- 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memorycited by 779
- 2026Adaptive Memory Admission Control for LLM Agentscited by 23
- 2025Not Wrong, But Untrue: LLM Overconfidence in Document-Based Queriescited by 3
- 2026Manufactured Confidence: How Memory Consolidation Turns Hearsay into Confident Factscited by 3
- 2026The Personalization Mirage: How LLMs Fabricate User Profiles, and Why Self-Monitoring Misleadscited by 1
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