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About this paper
Ziming Wang
The paper introduces TOKI, a bitemporal operator algebra, to formalize contradiction resolution in LLM agent persistent memory by systematically defining production heuristics with explicit isolation preconditions and provenance. This framework provides provably sound defenses against three novel LLM-specific write-time anomalies—replay inconsistency (N1), belief-drift skew (N2), and audit erasure (N3)—consistently demonstrating exclusion of these anomalies where existing baselines fail, while maintaining sub-linear latency scaling.
Key insights
- Robust LLM agent persistent memory requires adapting advanced database concepts like bitemporal data models and concurrency control to manage inherent write-heavy, versioned belief updates.
- Formalizing contradiction resolution through typed operators with explicit transactional properties is essential for reliable agent system design and predictable behavior.
- Maintaining a full audit trail of overwritten facts via a dual-row schema and K-semiring provenance is critical for accountability, debugging, and defending against LLM-specific data integrity issues.
Summary by alphaXiv.
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