February 2026

Large-scale foundation models for scientific machine learning adapt to physical settings unseen during training, such as zero-shot transfer between turbulent scales. This phenomenon, in-context learning, challenges conventional understanding of learning and adaptation in physical systems. Here, we study in-context learning of dynamical systems in a minimal setting: we train a small two-layer, single-head transformer to forecast one dynamical system, and then evaluate its ability to forecast a different dynamical system without retraining. We discover an early tradeoff in […]

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Traditional data platforms rely on centralized monoliths and complex engineering pipelines that often create bottlenecks for business decision-makers. This article introduces Da2a, an open-source prototype that proposes a decentralized, agentic architecture where specialized Domain Agents (e.g., Marketing, E-commerce) collaborate through a Root Orchestrator using the Agent-to-Agent (A2A) protocol. By treating data sources as autonomous experts that communicate via standardized “agent cards” rather than static tables, Da2a demonstrates how multi-agent systems can intuitively solve complex cross-domain problems, offering a […]

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Andrej Karpathy talks about “Claws” Andrej Karpathy tweeted a mini-essay about buying a Mac Mini (“The apple store person told me they are selling like hotcakes and everyone is confused”) to tinker with Claws: I’m definitely a bit sus’d to run OpenClaw specifically […] But I do love the concept and I think that just like LLM agents were a new layer on top of LLMs, Claws are now a new layer on top of LLM agents, taking […]

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Mechanical thrombectomy (MT) is typically the optimal treatment for acute ischemic stroke involving large vessel occlusions, but access is limited due to geographic and logistical barriers. Reinforcement learning (RL) shows promise in autonomous endovascular navigation, but generalization across ‘long’ navigation tasks remains challenging. We propose a Hierarchical Modular Multi-Agent Reinforcement Learning (HM-MARL) framework for autonomous two-device navigation in vitro, enabling efficient and generalizable navigation. HM-MARL was developed to autonomously navigate a guide catheter and guidewire from the femoral […]

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RL/ML papers love equations before intuition. This post attempts to flip it: each idea appears only when the previous approach breaks, and every concept shows up exactly when it’s needed to fix what just broke. Reinforcement Learning for LLMs “made easy” submitted by /u/zephyr770 [link] [comments]

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