Beyond Retrieval: Bi-Temporal State Arbitration for Longitudinal Healthcare Agents

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

A new framework, "Beyond Retrieval: Bi-Temporal State Arbitration for Longitudinal Healthcare Agents," addresses the limitations of retrieval-centric memory in Large Language Models (LLMs) for dynamic healthcare scenarios like chronic disease management. Developed by Zhao, Zhi, and Yu, this state-centric memory system, presented at KnowFM 2026, introduces a bi-temporal state representation that separates event time from ingestion time and tracks temporal validity. It incorporates an incremental state arbitration mechanism with four operators (SUPPORT, REFINE, SUPERSEDE, BRANCH-CONFLICT) to manage evolving medical facts without destructive overwriting. Additionally, a confidence-thresholded evidence escalation layer ensures robust memory access. Evaluated on a longitudinal diabetes management task, the method achieved a Unique-F1 of 0.85 and Conflict-F1 of 0.98, significantly outperforming long-context LLMs (0.38 / 0.89) and standard vector memory (0.30 / 0.60).

Key takeaway

For AI Scientists and Machine Learning Engineers building longitudinal healthcare agents, relying on traditional retrieval-centric LLM memory is insufficient for dynamic patient states. You should consider implementing a state-centric memory architecture that incorporates bi-temporal representations and incremental arbitration mechanisms. This approach ensures accurate, evidence-grounded state tracking, crucial for managing evolving medical facts in chronic disease management and similar applications.

Key insights

Longitudinal healthcare agents require state-centric memory with temporal arbitration, not just retrieval, for evolving patient data.

Principles

• Decouple event time from ingestion time for temporal validity.
• Implement non-destructive state arbitration for evolving facts.
• Continuous, evidence-grounded arbitration is crucial for agentic AI.

Method

The framework uses a bi-temporal state representation, an incremental arbitration mechanism with SUPPORT, REFINE, SUPERSEDE, and BRANCH-CONFLICT operators, and a confidence-thresholded evidence escalation layer.

In practice

• Apply bi-temporal tracking to patient health records.
• Utilize arbitration operators for medical fact updates.
• Integrate confidence thresholds for memory access.

Topics

• Longitudinal Healthcare Agents
• LLM Memory Architectures
• State Tracking
• Bi-temporal Representation
• State Arbitration
• Chronic Disease Management

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.