TEPA: Revoking Stale Memories for Conflict-Robust Language Agents

Language agents with long-term memory face a "memory pollution" problem: outdated facts remain retrievable even after the real-world situation changes, corrupting downstream reasoning. TEPA addresses this by treating memory validity as an explicit, revocable state, automatically invalidating stale precedents when contradicting evidence appears while preserving history for audit purposes. This is applicable to any long-running AI agent system needing to track evolving facts, preferences, or environment states reliably—such as personal assistants, enterprise knowledge agents, or monitoring systems—where TEPA substantially outperformed append-only and last-write-wins memory approaches during simulated real-world drift and reversal scenarios. Paper: https://arxiv.org/abs/2608.07429