AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,183 papers
A World Model-based Risk-aware Mixture-of-Experts (WM-RMoE) framework is proposed, which uniquely exhibits the ability to generate foresighted and semantically distinct overtaking maneuvers across diverse traffic densities.
To improve deployable small VLMs, KPA is proposed, a knowledge-preserving adaptation framework that combines structured perception-to-decision prompting, SLERP-based expert merging, and RegMoE, a regime-aware LoRA mixture-of-experts module that preserves the pretrained model's open-world knowledge while allocating ligh...
This work introduces a dual-process architecture that combines the strengths of robust reasoning and learning in a neuro-symbolic perspective on nonlinear motion planning and suggests that tightly coupling learning with structured reasoning offers a scalable path toward more capable and adaptive robotic systems.
IMPACT is introduced, a scalable Interaction-aware Model training framework with Prior-guided Attention Calibration and Targeting, which consistently outperforms the corresponding MSE-trained baselines, improving interaction fidelity, physical plausibility, and visual quality.
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FAIRY is developed to execute and evaluate agentic agronomic operations on full-season spatiotemporal workflows that span ridge preparation, planting, irrigation, fertilization, pest and disease treatment, harvest, grain handling, drying, and storage, and an evaluation suite that combines agentic success, full-path spa...
A matrix of 51 models on 12 physical AI benchmarks, selected from a registry of 51 benchmarks and 152 models by reporting density is constructed, combining scores from model cards and benchmark papers with their own evaluation runs under each benchmark's official protocol.
This paper proposes the memory-native NTN (Mem-NTN) paradigm that leverages long-horizon contexts for memory-augmented system optimization and establishes a dual-memory architecture that distinguishes between physical memory representing the state of the world and digital memory encoding historical network experience.
From YAML-first specifications that decouple contents, placement, behavior, and agent exposure, MagicSim constructs diverse executable worlds spanning task families, interaction regimes, physics, layouts, sensors, avatars, and robot embodiments in one reset-and-step loop.
This work introduces TRAjectory-routed Causal Evidence (TRACE), a memory framework for visuomotor imitation policies that stores task-relevant visual and robot-state evidence in a fixed-size latent memory that remains bounded over long episodes.
PhyGile performs physics-prefix-guided robot-native motion generation at inference time, directly generating robot-native motions in a 262-dimensional skeletal space with physics-guided prefixes, thereby eliminating inference-time retargeting artifacts and reducing generation-execution discrepancies.
Scale-Plan is presented, a scalable LLM-assisted framework that generates compact, task-relevant problem representations from natural language instructions that outperforms pure LLM and hybrid LLM-PDDL baselines across all metrics, improving scalability and reliability.
Experimental results demonstrate that Phys4D substantially improves fine-grained spatiotemporal and physical consistency compared to appearance-driven baselines, while maintaining strong generative performance.
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research.
A Blog post by Amazon on Hugging Face
Director of CSAIL and MIT professor honored for her contributions to robotics, artificial intelligence, and autonomous systems.
Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic applications.
From feet to fingertips — we are teaching robots intelligent whole-body control, fine dexterity, and teamwork to complete a broad range of complex tasks.
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