pith:4GRNZWZP
Useful Memories Become Faulty When Continuously Updated by LLMs
Consolidated memories from LLMs degrade over repeated updates and can perform worse than using no memory at all.
arxiv:2605.12978 v1 · 2026-05-13 · cs.AI
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Claims
Consolidated memories produced by today's LLMs are often faulty even when derived from useful experiences. As consolidation proceeds, memory utility first rises, then degrades, and can fall below the no-memory baseline. More surprisingly, even when consolidating from ground-truth solutions, GPT-5.4 fails on 54% of a set of ARC-AGI problems it had previously solved without memory.
That the observed degradation is caused by the consolidation step itself rather than by limitations specific to the tested models, tasks, or update schedules, and that the ARC-AGI Stream environment sufficiently represents real-world agent memory use.
LLM-consolidated memories in agents degrade over continuous updates even from useful experiences, causing up to 54% failure on previously solved ARC-AGI problems, while episodic retention preserves accuracy.
References
Receipt and verification
| First computed | 2026-05-18T03:09:08.679222Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e1a2dcdb2f634443eaa4c6069cafeea53f8cc8a6f9e73c1fcbd16f4da22d3000
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· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/4GRNZWZPMNCEH2VEYYDJZL7OUU \
| jq -c '.canonical_record' \
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# expect: e1a2dcdb2f634443eaa4c6069cafeea53f8cc8a6f9e73c1fcbd16f4da22d3000
Canonical record JSON
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