Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment

Agentic LLMs often fail the same way, again and again. A Stanford research team traced this to missing, reusable capabilities. Their system, TRACE, diagnoses those gaps and trains for them directly. TRACE stands for Turning Recurrent Agent failures into Capability-targeted training Environments. It was released open-source under an MIT license. What problem does TRACE solve?…

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

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

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

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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Scientists’ Facet Hustle? Utilizing AI and Quantum Computing to Generate New Peptides

Scientists’ Facet Hustle? Utilizing AI and Quantum Computing to Generate New Peptides

Scientists have efficiently proven a quantum pc can enhance the accuracy and attain of generative synthetic intelligence drug discovery fashions. They usually did it utilizing their spare money and time leftover from different tasks. The Technical College of Denmark workforce ran their generative AI mannequin for predicting proteins along with a printer-sized quantum pc constructed…

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

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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A Coding Guide to NVIDIA’s Tile-Based GPU Programming: From cuTile and Triton Kernels to Flash Attention

if BACKEND == “triton”: @triton.jit def _vadd_kernel(a_ptr, b_ptr, c_ptr, n, BLOCK: tl.constexpr): pid = tl.program_id(0) offs = pid * BLOCK + tl.arange(0, BLOCK) mask = offs < n a = tl.load(a_ptr + offs, mask=mask) b = tl.load(b_ptr + offs, mask=mask) tl.store(c_ptr + offs, a + b, mask=mask) @triton.jit def _fused_gelu_kernel(x_ptr, w_ptr, b_ptr, o_ptr, n, BLOCK:…

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