AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The introduction of OfficeTown, a generative simulation framework for professional organizations in which tasks and their context arise from continuing organizational work, highlights the value of organizational simulation in generating work-grounded data and environments for the continued development of generalist age...
Reinforcement learning with verifiable rewards (RLVR) relies on costly model-generated responses, motivating larger rollout batches and square-root learning-rate scaling. Reported to approximately preserve progress per sample with one optimizer step per rollout, this rule is less understood in practical multi-step trai...
Mobile manipulation requires precise navigation to a manipulation-ready pose followed by reliable object interaction. These two stages differ in action spaces and visual requirements, which complicates unified policy learning. In addition, collecting diverse real-world navigation data with explicit manipulation-ready p...
Building omni-modal models with physical intelligence requires fine-grained supervision that captures physical evidence such as contact, support, deformation, and state transitions. However, existing omni-modal captioners primarily model general audiovisual semantics and often overlook transient or spatially localized...
INSPIRE, a benchmark for evaluating agents that search prior literature to make progress on solution-redacted research problems, enables both end-to-end comparison and stage-resolved diagnosis in a setting where the agent must construct its own working criterion of relevance.
OmniFysics-Nano-V2, a compact omni-modal model for physical-world perception and understanding, is presented and experiments show that the proposed data and training strategy improves physical-world understanding while preserving broad omni-modal competence.
ReDeck is proposed, a step-level render-grounded refinement framework that decomposes slide revision into atomic edit actions and returns renderer-derived observations after each step, turning refinement into"one edit, one observation."
XL-DocBench is introduced, a fully human-verified benchmark for extra-long document understanding, with 1,519 retained questions from six professional domains and contexts up to 2,303 pages, and fills a gap left by prior single-page, short multi-page, or text-only long-context benchmarks.
DocAtlas is presented, a system that treats long-document understanding as a mutable-state information-seeking process and instantiate DocAtlas as a mutable document harness: an external environment that determines what document information is searched, read, stored, reviewed, and shown to the model at each step.
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