TEPA: Revoking Stale Memories for Conflict-Robust Language Agents
A recent paper published on arXiv (2608.07429) presents TEPA, a mechanism for revocable evidence-memory aimed at mitigating memory pollution in language agents. The researchers highlight a key issue with long-term memory systems: outdated memories can persist and affect prompts when the environment evolves, leading to performance decline. TEPA utilizes keyed precedents to represent observations and revokes these precedents when new evidence contradicts them, ensuring retrieval reflects current information while maintaining a history for auditing purposes. The mechanism was evaluated through various scenarios, including controlled hidden-regime drift and preference-update streams, showing that revocation effectively eliminates stale memories from the retrieval set. The study demonstrates significant enhancements in conflict-robustness across 50 seeds. This work advances AI and machine learning by enhancing the reliability of memory systems in language agents.
Key facts
- Paper arXiv:2608.07429 introduces TEPA, a revocable evidence-memory mechanism.
- TEPA addresses memory pollution caused by stale memories in language agents.
- Observations are represented as keyed precedents.
- Active precedents are revoked when fresh evidence contradicts them under the same key.
- Revoked history is preserved for audit.
- Tested on controlled hidden-regime drift, real file-backed executable drift, and preference-update streams.
- Revocation prevents stale active memory from remaining in retrieval set after reversal.
- Controlled drift experiments conducted over 50 seeds.
Entities
Institutions
- arXiv