When Old Facts Return: Re-Reads, Reverts, and the Limits of Temporal Memory
Source
arxiv.org
Author
Neeraj Yadav (Called It Inc.)
Date
Why it matters
AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → memory systems can restore obsolete facts when an old source is re-read, cutting accuracy to 10.8% in the test. A reactivation guard fixes this but needs change provenance to tell re-reads from true reverts.
Key takeaways · AI-distilled
The core ambiguity: re-reading an old source and a genuine revert can produce the same observed sequence of values, yet they require opposite current answers.
The study uses 130 extractor-selected atomic transitions derived from software fixes; without a re-read, identity-based temporal memory reached 98.5% model-judged accuracy with zero observed stale-value errors.
The verbatim re-read pushed the stale-value rate to 88.5%; the guard against reactivating retired values cut it to 0.8% in that constructed condition.
The author's design takeaway is to separate observing a value from evidence that the value changed, and notes limits: selected inputs, aggregate-only answer records, related-family judges and a guard evaluated after the failure was found.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
articleTemporal Validity on Real Software Histories: Eliminating Stale-Fact Errors in Code-Assistant Memory over GitHub FixesNeeraj Yadav
articleThe Memory Trust Gap: Capability-Dependent Failures in Persistent-Memory AgentsJundong Hu, Shekar Ramachandran
articleWhen Evidence Changes: Evaluating Memory Repair and Re-reading in Language-Model AgentsWenhui Chu (University at Albany, State University of New York)