AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Large Multi-modality Models (LMMs) have made significant progress in visual understanding and generation, but still face challenges in visual editing, particularly in following complex instructions, preserving appearance consistency, and supporting flexible input formats. To study this gap, we introduce RISEBench, the...
While In-Context Learning (ICL) enables models to adapt from exemplars without parameter updates, multimodal ICL remains largely underexplored, particularly regarding video demonstrations in interactive environments. For multimodal agents, learning from videos presents unique challenges: they must translate in-context...
TRWORLDBENCH is introduced, a benchmark for evaluating embodied world models through synchronized head, left-wrist, and right-wrist videos and uses 19 metrics to assess tri-view consistency, task alignment, physical and 3D coherence, motion quality, temporal consistency, and visual quality.
Bench2Dex is meant as a platform for studying visuo-tactile learning across dexterous hands and offers a shared setting for algorithm development while tactile hardware and simulation models are still evolving.
ACE-Brain-0.5 is presented, a unified embodied foundation model that organizes robot intelligence into five coupled functions: spatial perception, decision making, embodied interaction, self-monitoring, and self-improvement, and SSR+, which extends Scaffold-Specialize-Reconcile with a Reactivate stage after task-vector...
WorldScape Policy 2.0 is introduced, a controllable WAM with reasoning-augmented long short-term memory and fine-grained instruction following and in-context adaptation that demonstrates superior capabilities in long-horizon autonomous planning, fine-grained instruction following and in-context adaptation.
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