When to Remember, When to Abstain: Category-Conditioned Retention for Reliable Agent Memory
cs.AI, cs.MA
Submitted: 2026-10-05
Updated: 2026-10-05
Comments: 4 pages, 1 Figure, Accepted to NeurIPS 2026 Social Agent Workshop (https://openreview.net/group?id=NeurIPS.cc%2F2026%2FWorkshop%2FSocialAgent#tab-your-consoles)
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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.
Terminology
Abstract
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 about 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.
Sources
- Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
- Not Wrong, But Untrue: LLM Overconfidence in Document-Based Queries
- Manufactured Confidence: How Memory Consolidation Turns Hearsay into Confident Facts
- MemGPT: Towards LLMs as Operating Systems
- The Personalization Mirage: How LLMs Fabricate User Profiles, and Why Self-Monitoring Misleads
- A-MEM: Agentic Memory for LLM Agents
- Adaptive Memory Admission Control for LLM Agents
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