arXiv Computer Vision
MSM-Mem: A Universal Medical Structured Multimodal Memory Framework for Medical AI Agents
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Sep 15
EMR: Self-Evolving Medical Multi-Agent System via Experience Mining and Reuse
arXiv:2609.15161v1 Announce Type: cross Abstract: Large language model (LLM) driven multi-agent systems have shown promise in complex clinical reasoning, yet existing approaches rely on static strate...
arXiv AI
Jun 9
Experience Makes Skillful: Enabling Generalizable Medical Agent Reasoning via Self-Evolving Skill Memory
arXiv:2606. 09365v1 Announce Type: new Abstract: Medical agent systems are increasingly expected to support interactive clinical decision making rather than only static question answering.
arXiv Machine Learning
Sep 1
MedCache: Efficient and Temporally Valid Memory for Longitudinal Clinical Agents
arXiv:2608.29528v1 Announce Type: new Abstract: Longitudinal clinical agents must maintain an evolving patient state from evidence distributed across visits, time points, and specialties. However, ho...
arXiv AI
Jun 3
MemVerse: Multimodal Memory for Lifelong Learning Agents
arXiv:2512. 03627v2 Announce Type: replace Abstract: Despite rapid progress in large-scale language and vision models, AI agents still suffer from a fundamental limitation: they cannot remember.
Hugging Face Trending Papers
Jun 8
Experience Makes Skillful: Enabling Generalizable Medical Agent Reasoning via Self-Evolving Skill Memory
Medical agent systems are increasingly expected to support interactive clinical decision making rather than only static question answering. In such settings, effective agents must reuse prior experience across evolving cases, yet existing memory mechanisms often retain raw historical traces that are redundant, noisy, and difficult to govern.
arXiv AI
Jun 19
MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization
arXiv:2606. 20164v1 Announce Type: cross Abstract: Real-world clinical decision support requires reasoning over heterogeneous and longitudinal patient information rather than answering isolated medical questions.