February 2026

Meta AI Open Sources GCM for Better GPU Cluster Monitoring to Ensure High Performance AI Training and Hardware Reliability

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While the tech folks obsesses over the latest Llama checkpoints, a much grittier battle is being fought in the basements of data centers. As AI models scale to trillions of parameters, the clusters required to train them have become some of the most complex—and fragile—machines on the planet. Meta AI Research team just released GCM (GPU Cluster Monitoring), a specialized toolkit designed to solve the ‘silent killer’ of AI progress: hardware instability at scale. GCM is a blueprint […]

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Liquid AI’s New LFM2-24B-A2B Hybrid Architecture Blends Attention with Convolutions to Solve the Scaling Bottlenecks of Modern LLMs

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The generative AI race has long been a game of ‘bigger is better.’ But as the industry hits the limits of power consumption and memory bottlenecks, the conversation is shifting from raw parameter counts to architectural efficiency. Liquid AI team is leading this charge with the release of LFM2-24B-A2B, a 24-billion parameter model that redefines what we should expect from edge-capable AI. https://www.liquid.ai/blog/lfm2-24b-a2b The ‘A2B’ Architecture: A 1:3 Ratio for Efficiency The ‘A2B’ in the model’s name stands […]

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When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering

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Policy steering is an emerging way to adapt robot behaviors at deployment-time: a learned verifier analyzes low-level action samples proposed by a pre-trained policy (e.g., diffusion policy) and selects only those aligned with the task. While Vision-Language Models (VLMs) are promising general-purpose verifiers due to their reasoning capabilities, existing frameworks often assume these models are well-calibrated. In practice, the overconfident judgment from VLM can degrade the steering performance under both high-level semantic uncertainty in task specifications and low-level […]

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New novel MARL-SMT collab w/Gemini 3 flash (& I know nothing)

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Executive Summary & Motivation Project Title: Hamilton-SMT: A Formalized Population-Based Training Framework for Verified Multi-Agent Evolution Category: Foundational ML & Algorithms / Computing Systems and Parallel AI Keywords: MARL, PBT, SMT-Solving, Lean 4, JAX, Formal Verification submitted by /u/Regular_Run3923 [link] [comments]

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