February 2026

Anthropic announced a $30 billion funding round on February 12, which valued the company at $380 billion. But the same day, the company made another move that reveals a deeper question about its identity: a $20 million contribution to Public First Action, a bipartisan group supporting policymakers who want federal guardrails around AI. The announcements capture a tension that may define the company’s trajectory. Anthropic was founded in 2021 by former OpenAI executives who left specifically over concerns that commercialization was […]

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Your Computer System Just Became Your Universal AI Agent Toolkit For Everything! :::tip What if you could talk to your computer the way you talk to a colleague — and it just did the work? ::: Not autocomplete. Not a chatbot that pastes code snippets. Not a suggestion engine that highlights the next three words. A system that takes your intent and orchestrates your entire machine — files, browser, terminal, internet — to deliver the result. That is exactly […]

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The Thermodynamic Ceiling The accelerating AI revolution, pushing computational demand beyond Moore’s Law into the “AI Supercycle,” is straining terrestrial infrastructure. Hyperscale data centers, once symbols of progress, are now viewed as “thermodynamic parasites” due to their unsustainable consumption of gigawatt-level power and millions of gallons of potable water, issues the current grid and aquifers cannot support. By 2025, the AI industry’s energy crisis became the key technological constraint. Training and interference for massive LLMs demands energy comparable […]

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The long-tail distribution, where a few head labels dominate while rare tail labels abound, poses a persistent challenge for large-scale Multi-Label Classification (MLC) in real-world data mining applications. Existing resampling and reweighting strategies often disrupt inter-label dependencies or require brittle hyperparameter tuning, especially as the label space expands to tens of thousands of labels. To address this issue, we propose Curiosity-Driven Game-Theoretic Multi-Label Learning (CD-GTMLL), a scalable cooperative framework that recasts long-tail MLC as a multi-player game – […]

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Causal structure learning, also known as causal discovery, aims to estimate causal relationships between variables as a form of a causal directed acyclic graph (DAG) from observational data. One of the major frameworks is the order-based approach that first estimates a topological order of the underlying DAG and then prunes spurious edges from the fully-connected DAG induced by the estimated topological order. Previous studies often focus on the former ordering step because it can dramatically reduce the search […]

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Collaborative clinical decision support is often constrained by governance and privacy rules that prevent pooling patient-level records across institutions. We present a hybrid privacy-preserving framework that combines Federated Learning (FL) and Split Learning (SL) to support decision-oriented healthcare modeling without raw-data sharing. The approach keeps feature-extraction trunks on clients while hosting prediction heads on a coordinating server, enabling shared representation learning and exposing an explicit collaboration boundary where privacy controls can be applied. Rather than assuming distributed training […]

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Virtual screening aims to efficiently identify active ligands from massive chemical libraries for a given target pocket. Recent CLIP-style models such as DrugCLIP enable scalable virtual screening by embedding pockets and ligands into a shared space. However, our analyses indicate that such representations can be insensitive to fine-grained binding interactions and may rely on shortcut correlations in training data, limiting their ability to rank ligands by true binding compatibility. To address these issues, we propose BindCLIP, a unified […]

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