When to Remember, When to Abstain: Category-Conditioned Retention for Reliable Agent Memory
Quick Answer
The study proposes a category-conditioned retention strategy for agent memory, improving reliability by adapting retention thresholds based on assertion types.
Quick Take
This method reduced unsupported value assertions from 6.2% to 4.0%, while maintaining higher coverage compared to a global threshold, demonstrating that retention decisions should consider assertion categories rather than relying solely on confidence levels.
Key Points
- Retention decisions should be based on assertion categories, not a single global threshold.
- A stricter threshold for value assertions led to a 36% relative reduction in unsupported claims.
- The study evaluated 4,715 candidate assertions across 100 synthetic personas.
- Category-conditioned thresholds preserved 13 percentage points more coverage than global thresholds.
- The research highlights the importance of confidence levels in conjunction with assertion types.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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 ${\approx}36\%$ 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.
| Comments: | 4 pages, 1 Figure, Accepted to NeurIPS 2026 Social Agent Workshop (this https URL) |
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.07100 [cs.AI] |
| (or arXiv:2610.07100v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07100 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Olukunle Owolabi [view email]
[v1]
Mon, 5 Oct 2026 14:36:51 UTC (313 KB)
— Originally published at arxiv.org
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