TEPA: Revoking Stale Memories for Conflict-Robust Language Agents

2026-08-07

AI Summary

This work addresses memory contamination in language agents caused by the retention of outdated information in long-term memory, which degrades decision accuracy. The authors propose TEPA, a novel mechanism that introduces revocable memory lifecycle management: observations are represented as keyed precedents, and conflicts between new evidence and existing memories are dynamically detected. Upon detecting such conflicts, TEPA revokes invalidated memories, ensuring retrieval is always grounded in the most current and valid knowledge. This approach enables dynamic falsification, auditability, and reactivation of memories. Evaluated across diverse memory drift scenarios, TEPA achieves an accuracy of 0.950, substantially outperforming conventional strategies that rely solely on append-only or overwrite-based memory updates.

Abstract

Long-term memory enables language agents to reuse past facts, preferences, and task experience. Persistence also creates a central falsifiability problem: when the world changes, stale memories can remain retrievable and pollute the prompt. We characterize this failure mode as memory pollution: degradation caused by active memories that newer conflicting evidence has superseded. We introduce TEPA, a revocable evidence-memory mechanism that makes validity an explicit state of memory. TEPA represents observations as keyed precedents and revokes active precedents when fresh evidence contradicts them under the same key, allowing retrieval to draw from current evidence while preserving revoked history for audit. Across controlled hidden-regime drift, real file-backed executable drift, and preference-update streams, revocation prevents stale active memory from remaining in the retrieval set after reversal. In controlled drift over 50 seeds, append-only and last-write-wins memory fell below no memory during full reversal (append-only and last-write-wins both 0.210, no memory 0.309, TEPA 0.950), and the same pattern reproduced under real file execution (append-only 0.203, no memory 0.298, TEPA 0.950). On clean MemoryAgentBench SH-6k, TEPA matches a strong last-write-wins cache, confirming that current-key replacement is the decisive operation for single-hop fact consolidation. Boundary tests on multi-hop and very long-context MemoryAgentBench settings expose retrieval-chain and context-selection bottlenecks beyond fact-level validity tracking. Together, these results establish lifecycle revocation as a core memory operation for agents that must falsify, audit, and later re-promote evolving knowledge.

Problem

• memory pollution
• stale memories
• conflict-robustness
• falsifiability
• language agents

Innovation

• memory revocation
• conflict-robust agents
• evidence-memory mechanism
• falsifiability in LLM agents
• dynamic memory lifecycle