AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Competent agents need precise and verifiable environments, such as sandboxes that are resumable at any stage and evolve from observable evidence. However, most long-horizon work exposes how rare these are: for example, an agent in medical research must ground a finding, classify it, and write a report over dozens of de...
Long-horizon agents are beginning to automate complete workflows that produce code, reports, and research artifacts. Medical imaging workflows are multi-stage and data-sensitive, while expert trajectories remain scarce and difficult to share. Structured benchmarks can localize failures through stage-level rubrics, but...
The NV-Reason-CT model, a generative vision--language model for chest and abdominal CT combining native 3D visual encoding with radiologist-guided reasoning, and the model and training code are released to support reproducible research on explainable AI for volumetric medical imaging.
TokAG, a zero-shot affordance grounding framework that exploits the token-level semantic-spatial signals in LVLMs to localize action-relevant regions without external supervision, and introduces a spatial-aware token-selection mechanism to systematically evaluate each output token.
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