TOKI: A Bitemporal Operator Algebra for Contradiction Resolution in LLM-Agent Persistent Memory
arXiv:2606.06240v1 Announce Type: cross Abstract: Persistent memory for an LLM agent is a write-heavy substrate: every belief update is a versioned write, and a new claim may contradict a stored one. Production systems use four resolution heuristics (last-writer-wins, evidence-weighted merge, await-confirmation, per-rule policy), yet none declares the isolation level it assumes or the write-time anomalies it admits. We show that contradiction resolution is write-time concurrency control and make the missing contract explicit. TOKI types the four heuristics as one family of bitemporal operators
The proliferation of LLM agents and their increasing reliance on persistent memory highlights a critical need for robust contradiction resolution as these systems scale.
This work addresses a foundational challenge in developing reliable and performant LLM agents by formalizing how they manage conflicting information.
The explicit typing of contradiction resolution heuristics as bitemporal operators provides a more rigorous framework for designing and evaluating LLM-agent memory systems.
- · LLM agent developers
- · AI research community
- · AI-powered enterprise software
- · Developers ignoring concurrency control in AI agents
Improved reliability and consistency of LLM agents in production environments due to better memory management.
Reduced incidence of hallucination and logical inconsistencies in agent behavior as their memories become more robust.
Acceleration of complex, multi-agent system development as a foundational memory problem is better understood and solved.
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Read at arXiv cs.AI