MedMemoryBench is a bilingual (Chinese/English) benchmark for evaluating long-term memory capabilities of LLM-based agents in realistic healthcare scenarios. It simulates longitudinal patient-doctor interactions spanning 12 months of chronic disease management, where an AI agent must accumulate, retain, and recall clinical information across extended multi-session dialogues.
Key Features
20 diverse patient personas covering chronic diseases (diabetes, hypertension, heart failure, COPD, sleep apnea, Parkinson's, Alzheimer's, etc.)
101 medical consultation sessions per persona with realistic multi-turn dialogues
Noise injection: family health consultations and unrelated health discussions to test memory robustness
6 query types for comprehensive memory evaluation:
Entity Exact Match
Temporal Localization
State Update Tracking
Inference & Generation
Multiple Choice
Multi-hop Clinical Deduction
Trap events that test whether the agent correctly handles medication allergies, contraindications, and critical safety information
Bilingual: Full Chinese and English versions with aligned content
The benchmark evaluates memory systems through a two-phase protocol:
Memorization Phase: The agent reads all dialogue sessions for a persona sequentially, building its memory store.
Query Phase: The agent answers evaluation queries that require recalling specific facts, tracking state changes, performing temporal reasoning, or making multi-hop clinical deductions.
Query Type Distribution (per persona)
Query Type
Count
Description
Entity Exact Match
20
Recall specific medical facts
Temporal Localization
20
Identify when events occurred
State Update
10
Track how conditions evolved
Inference & Generation
20
Derive conclusions from memory
Multiple Choice
~19
Select correct option from choices
Multi-hop Clinical Deduction
~8
Chain multiple facts for reasoning
Dataset Creation
Generation Pipeline
Persona Design: 20 diverse patient profiles covering major chronic disease categories, each with detailed demographics, lifestyle, and medical history.
Dialogue Simulation: Multi-turn patient-doctor conversations generated by specialized LLM agents (patient agent + doctor agent), grounded in the event timeline.
Noise Injection: Family health consultations and unrelated health discussions inserted between medical sessions to test memory selectivity.
Query Generation: Evaluation questions targeting specific memory capabilities, with expert-reviewed ground truth answers.
Quality Assurance: Human expert review and deduplication of queries.
Source Data
All data is synthetically generated using large language models with medical domain expertise, guided by clinical guidelines (e.g., Chinese Diabetes Prevention Guidelines 2024, ADA Standards of Care, GOLD COPD Strategy, ESC Heart Failure Guidelines).
Considerations
Ethical Considerations
This dataset contains synthetic medical dialogues and does not include real patient data.
All patient personas are fictional constructions designed for evaluation purposes.
The dataset should NOT be used for actual clinical decision-making or medical advice.
Generated medical content follows established clinical guidelines but may not reflect all real-world complexities.
Limitations
Dialogues are generated by LLMs and may not capture the full variability of real patient-doctor interactions.
Disease progression follows simplified models; real clinical trajectories are more heterogeneous.
The benchmark focuses on factual memory and does not evaluate empathy, communication style, or clinical judgment quality.
Licensing
This dataset is released under CC BY-NC-SA 4.0. It may be used for research purposes only. Commercial use is prohibited.
Citation
@inproceedings{medmemorybench2026,
title={MedMemoryBench: A Bilingual Benchmark for Evaluating Long-Term Memory in Medical LLM Agents},
author={},
booktitle={NeurIPS 2026 Datasets and Benchmarks Track},
year={2026}
}
