Clinical care changes every day. Can the AI agents supporting it change with it? That question sits at the heart of self-evolving LLM agents in healthcare. These systems aim to do more than run a fixed workflow. They update memory, tools, prompts or coordination strategies based on experience, so future tasks benefit from what came before. Promising as that sounds, the hard part is comparing these systems across papers. One system stores reflections for later use. Another revises its prompts. A third changes how multiple agents work together. Each reflects a different kind of adaptation, yet much of it still gets grouped under the same term. To bring more clarity to that space, our work 𝐁𝐞𝐲𝐨𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐜 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐀 𝐒𝐢𝐱-𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥 𝐓𝐚𝐱𝐨𝐧𝐨𝐦𝐲 𝐚𝐧𝐝 𝐒𝐮𝐫𝐯𝐞𝐲 𝐨𝐟 𝐒𝐞𝐥𝐟-𝐄𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐋𝐋𝐌 𝐀𝐠𝐞𝐧𝐭𝐬 𝐟𝐨𝐫 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 introduces a structured framework that organizes self-evolution across six dimensions: Tools, Memory, Context, Policy, Multi-Agent Coordination and Reasoning. Each dimension includes decision trees with graded levels, from static behavior to deeper persistent change. That makes it easier to see not just whether a system adapts but where and how strongly. We applied this framework to 18 representative healthcare agent papers. Most systems show some form of adaptation but it tends to concentrate in memory rather than deeper changes to tools, policy or reasoning. Memory evolution appeared most often. Prompt self-revision showed up in roughly a third of systems. Tool-selection evolution and learned coordination remained uncommon. Formal policy optimization and reasoning-mechanism evolution did not appear in the reviewed set. The takeaway is not that the field lacks ambition, but that most adaptation today still lives in a narrow slice of what self-evolution could mean. We hope this taxonomy helps researchers and practitioners compare systems more clearly and build adaptive clinical AI with greater rigor. Read the paper here: lnkd.in/eWDDg2HT If you are building or evaluating healthcare agents, I would love your feedback and am open to collaboration. Drop a comment or reach out. #HealthcareAI #LLMAgents #ClinicalAI #AIResearch #SelfEvolvingAgents

Production ML is less about fancy architectures and more about judgment, trade-offs, and building systems that survive messy data. Really enjoyed this conversation with Data Tinkerer on what it actually takes to run recommendation systems at scale, and how the role evolves as you grow into senior ML ownership. datatinkerer.io/p/how-to-bui…

Excited to share our latest blog on how @Instacart uses machine learning to recommend replacements for out-of-stock items, ensuring a seamless shopping experience for our users! tech.instacart.com/how-insta…