When AI Memory Trusts Stale Facts
π‘Larger models may trust stale memory moreβsee which safeguards actually restore accuracy.
β‘ 30-Second TL;DR
What Changed
In the Benefit suite, models returned stale stored values 92β100% of the time across all Qwen3 sizes.
Why It Matters
The findings challenge the assumption that stronger models automatically make safer use of memory. Developers of personalized agents should treat memory provenance, recency, and conflict resolution as safety-critical controls rather than interface details.
What To Do Next
Add a memory-conflict evaluation that contrasts stale notes with authoritative tool results, and require timestamp/source checks plus pre-resolution before deployment.
Key Points
- β’In the Benefit suite, models returned stale stored values 92β100% of the time across all Qwen3 sizes.
- β’In the Safety suite, larger models suffered the greatest accuracy collapse when stale notes were made to look current.
- β’Removing memory labels increased over-trust at every scale, while newer-looking dates particularly misled larger models.
- β’Metadata improved accuracy for capable models, but only pre-resolving conflicts restored accuracy for the two smaller checkpoints.
- β’The scale-dependent pattern was replicated with Llama-Instruct models and on the RGB and MisBench datasets.
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Original source: ArXiv AI β
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