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Prime Intellect Releases Verifiers v1: Composable Tasksets, Harnesses, and Runtimes for Agentic RL Training and Evaluations

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Prime Intellect launched verifiers 0.2.0. It previews a rewritten core, shipped under the new verifiers.v1 namespace. Modern evaluations now run coding agents with tools, compaction, and subagents. Accordingly, v1 rebuilds environments to run these agentic workloads at scale. What is verifiers v1? First, consider what verifiers is: Prime Intellect’s environment stack for agentic reinforcement learning […]

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Meet NeuroVFM: A New Neuroimaging Foundation Model Trained With Vol-JEPA on Uncurated Clinical MRI and CT Volumes

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Frontier models learn mostly from public internet data. However, clinical neuroimaging rarely appears there, because MRI and CT scans contain identifiable facial features. Consequently, general models underperform on brain-imaging tasks. A University of Michigan research team addresses this gap with NeuroVFM, published in Nature Medicine. What is NeuroVFM? At its core, NeuroVFM is a generalist

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Guide to Loop Engineering: How ‘autoresearch’ and ‘Bilevel Autoresearch’ Turn AI Agents Into Autonomous Machine Learning ML Research Loops

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Most people still use AI like a 2015 search box. You type, you read, you type again. A newer pattern replaces that manual back-and-forth with a loop. This guide explains loop engineering using two verified artifacts. The sources are Andrej Karpathy’s autoresearch repository and the Bilevel Autoresearch paper. The framing follows a write-up by @0xCodila.

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Mira Murati’s Thinking Machines Lab Makes The Technical Case For Human-Centered AI Built On Customizable Model Weights

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Thinking Machines Lab published a report to build AI that extends human will and judgment. Most AI in use today is trained in a handful of places, then frozen. The report argues that this design excludes the people a model serves. Instead, the Thinking Machines lab researchers want AI that is distributed, customizable, and shaped

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Ant Group’s Robbyant Unveils LingBot-VA 2.0: A Causal Video-Action Model Built Natively for Physical AI

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Robbyant, the embodied AI unit inside Ant Group, has released the LingBot-VA 2.0.The first embodied-native foundation model. It describes a video-action foundation model for generalist robot manipulation. The research team pretrains the whole stack for embodiment instead of fine-tuning a video generator. What is LingBot-VA 2.0? Most video-action models reuse two components built for digital

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Kyutai Releases MuScriptor: An Open-Weight Decoder-Only Transformer for Multi-Instrument Music Transcription to MIDI

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Automatic Music Transcription (AMT) converts an audio recording into symbolic notes, usually MIDI. Single-instrument transcription already works reasonably well. However, transcribing a full multi-instrument mix stays difficult. Kyutai and Mirelo team now release MuScriptor to close that gap. It is an open-weight model trained on real, multi-instrument recordings across many genres. This article explains how

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Meet LingBot-World-Infinity: An Open Causal World Model With An Agentic Harness

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Robbyant, Ant Group’s embodied-intelligence unit, has released LingBot-World-Infinity (LingBot-World 2.0). It is a causal video generation model that behaves as an interactive world simulator. It is how the team attacks two failure modes: long-horizon drift and interactive latency. What is LingBot-World-Infinity? An interactive world model generates video frame by frame, conditioned on a stream of

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Meta Superintelligence Labs Releases Muse Spark 1.1: A Multimodal Reasoning Model for Agentic Tasks on Meta Model API

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Today, Meta Superintelligence Labs released Muse Spark 1.1. Alongside it, Meta opened a public preview of the Meta Model API. That second part is the structural change. Meta’s models previously reached developers mainly as open weights. Muse Spark 1.1 is closed, hosted, and metered per token. So the question is narrow. Where does it belong

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OpenAI Releases GPT-5.6 (Sol, Terra, Luna): A Three-Tier Model Family With Programmatic Tool Calling in the Responses API

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OpenAI just moved the GPT-5.6 family to general availability, following a limited preview. The release ships three models rather than one. Sol is the flagship, Terra is the balanced everyday tier, and Luna is the most cost-efficient. To sum up GPT-5.6 ships three tiers — Sol, Terra, Luna — priced from $1/$6 to $5/$30 per

OpenAI Releases GPT-5.6 (Sol, Terra, Luna): A Three-Tier Model Family With Programmatic Tool Calling in the Responses API Read More »

Meet Nemotron Labs 3 Puzzle 75B A9B: A Compressed Hybrid MoE LLM Delivering 2.03x Server Throughput

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Large hybrid MoE models like Nemotron-3-Super are accurate but expensive to serve. Their active parameters, KV cache, and Mamba state cap how many users a node can hold at a given per-user token rate. NVIDIA AI team has released Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super. The parent model has 120.7B total and 12.8B active parameters.

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