AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,183 papers
The PACE is presented, a framework for retrieval-augmented dialogue serving that formalizes Perceived Time-to-First-Response (PTFR) as a QoE objective and minimizes it under quality/cost constraints and is the first quantification of filler-answer conflict risk in deployed services.
Automatic Guided Vehicles (AGVs) substantially boost warehouse throughput, but routing large-scale AGV fleets remains challenging. Classical Multi-Agent Pathfinding solvers suffer from exploding combinatorial complexity and super-quadratic runtime, while relying on idealized grid or piecewise-linear motion models that...
Chain-of-Thought (CoT) prompting enables LLMs to perform explicit, step-by-step reasoning, creating opportunities for sophisticated autonomous robots. However, recent research reveals that reasoning models verbalize their actual decision processes only 25-39% of the time, with faithfulness degrading 44% on complex task...
A 652,157-parameter action-conditioned visuotactile world model with matched behavior cloning, policy learning in imagination, independent reactive implicit Q-learning, and model-assisted force feedback is studied, finding that accurate tactile forecasts need not improve force-constrained control.
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Nine metrics that make a verifier claim checkable, and coordinates for the verifiers still to be built, are closed with nine metrics that make a verifier claim checkable.
Results show no reliable advantage from training-time geometric alignment under this recipe and illustrate the gap between coordinate invariance and physical-layout generalization.
It is proved that the LLM paired with the BSE is a sound Markov policy on the belief MDP induced by the underlying POMDP, and inherits the Bellman optimality guarantees of classical POMDP theory, provided the LLM is never exposed to the raw history.
Time-Frequency Geometric Cross-Attention (TFGCA), a drop-in module repairing both blind spots in vision-language-action policies, uses a per-dimension learnable stationary wavelet transform to decompose the action chunk into time-frequency tokens, and each time token retrieves information from them via a cross-attentio...
This paper studies unmanned aerial vehicle (UAV)-mouted reconfigurable intelligent surface (RIS) communication with stochastic link activation with stochastic link activation using deep reinforcement learning and a Decision Transformer trained on expert trajectories from multiple scenarios.
This work outlines a pre-registered prospective test under a shared loss and logging standard, requiring clarified measurements and records, a defined process decision and relevant outputs, credible economic inputs, and validation at the intended scale.
This work aims to advance the development of socially desirable embodied agents in open-domain environments by extending the concept of proactive assistance to Social Proactive Intelligence (SPI), characterized by diverse scenarios, social understanding, and robot-centric behaviors.
As robots become increasingly integrated into various industries, understanding how humans respond to robotic failures is critical. This study systematically examines trust dynamics and system design by analyzing human reactions to robot failures. We conducted a four-stage survey to explore how trust evolves throughout...
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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