In this tutorial, we reconstruct the VideoAgent workflow as a runnable, API-key-free multi-agent pipeline. We build an intent parser, an agent library, a tool router, a graph planner, and a textual-gradient optimizer that repairs the execution graph. We wire these planning components to FFmpeg, Whisper transcription, scene detection, keyframe sampling, captioning, cross-modal indexing, and beat-synced editing. By the end, we have a system that answers questions about a video, summarizes it, and produces edited artifacts from natural-language instructions.

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MORPHEUS from Skyfall AI is a persistent enterprise simulation platform for continual reinforcement learning. It runs worlds that never reset, using parameterisable regime shifts and a six-metric evaluation protocol. Across the platform, PPO, HER, EWC, and LCM all remain far below the theoretical upper bound.

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Prime Intellect launched verifiers 0.2.0, previewing a rewritten “v1” core under the verifiers.v1 namespace. It splits an environment into a taskset (what), a harness (how), and a runtime (where), with an interception server that proxies requests and records training-ready traces. Any taskset runs under any compatible harness, with full prime-rl training support at launch.

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Agentic LLMs keep failing the same way because they lack specific, reusable capabilities. Stanford’s TRACE diagnoses those gaps from an agent’s own trajectories, synthesizes one verifiable training environment per capability, trains a LoRA adapter for each, and routes tokens across experts—improving τ²-Bench by +15.3 points and reaching 73.2% Pass@1 on SWE-bench Verified.

The post Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment appeared first on MarkTechPost.

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. […]

The post Guide to Loop Engineering: How ‘autoresearch’ and ‘Bilevel Autoresearch’ Turn AI Agents Into Autonomous Machine Learning ML Research Loops appeared first on MarkTechPost.

NeuroVFM is a generalist neuroimaging foundation model from the University of Michigan, trained on 5.24M clinical MRI and CT volumes. Its Vol-JEPA base extends I-JEPA and V-JEPA to volumetric medical imaging, learning brain anatomy and pathology without radiology-report labels.

The post Meet NeuroVFM: A New Neuroimaging Foundation Model Trained With Vol-JEPA on Uncurated Clinical MRI and CT Volumes appeared first on MarkTechPost.