Agentic Physical AI
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Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control
arXiv:2512.23292v5 Announce Type: replace Abstract: The prevailing paradigm in AI for physical systems: scaling general-purpose foundation models toward universal multimodal reasoning, confronts a barrier at the control interface. Frontier vision-language models achieve only 50-53% accuracy on basic quantitative physics tasks, behaving as approximate guessers that preserve semantic plausibility while violating physical constraints. Safety-critical control demands outcome-space guarantees...
Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control
arXiv:2512.23292v4 Announce Type: replace Abstract: The prevailing paradigm in AI for physical systems: scaling general-purpose foundation models toward universal multimodal reasoning, confronts a barrier at the control interface. Frontier vision-language models achieve only 50-53% accuracy on basic quantitative physics tasks, behaving as approximate guessers that preserve semantic plausibility while violating physical constraints. Safety-critical control demands outcome-space guarantees...
VASO: Formally Verifiable Self-Evolving Skills for Physical AI Agents
arXiv:2606.05395v1 Announce Type: new Abstract: Reusable robot skills are becoming the basic units through which embodied agents turn open-ended instructions into long-horizon physical behavior. We argue that, while foundation models have collapsed the cost of creating these skills, the cost of trusting them has not. Existing skill-evolution loops refine skills through execution feedback, unit tests, environment reward, or LLM self-critique, but these signals provide only trace-level...
Stability Without Safety: Gain Manipulation Attacks on Agentic Cyber-Physical Systems
arXiv:2606.07803v1 Announce Type: new Abstract: Agentic cyber-physical systems (CPS), where autonomous AI agents participate in runtime control decision-making, introduce agent-driven parameter-update pathways absent from conventional feedback architectures. These pathways form a parameter channel structurally distinct from classical sensor and actuator channels. Among these parameters, feedback gains are the highest-leverage target: a single gain matrix determines closed-loop eigenvalue...
Insurance of Agentic AI
Announce Type: new Abstract: Agentic artificial intelligence (AI) systems are transforming the risk landscape by extending beyond information generation to autonomous planning, tool invocation, decision execution, and persistent modification of digital and physical environments. These capabilities introduce novel exposures that do not fit neatly within traditional insurance categories such as cyber, professional liability, product liability, or directors and officers coverage. This paper...
Memory-Bound but Not Bandwidth-Limited: The Physical AI Inference Gap in Batch-1 LLM Decode
arXiv:2605.30571v1 Announce Type: new Abstract: Physical AI systems, including robots, autonomous vehicles, embodied agents and edge copilots, often run a different inference workload from cloud LLM serving: single-stream, batch-1 autoregressive decode, where one robot, camera feed or user session waits on the next token. This workload is usually described as memory-bandwidth-bound. Each decode step streams model weights and the active KV cache, so latency should scale with peak HBM bandwidth.
Meta is trying to sell AI agents to businesses in latest effort to diversify away from ads
Meta's effort to push beyond ads and to make money from artificial intelligence is now targeting the red-hot market of AI agents. The company said Wednesday that a new Meta Business Agent feature can be used across apps like WhatsApp, Messenger and Instagram to respond to customer inquiries, recommend products and book appointments. It will be included in a business-focused subscription tier for Meta One, which the company introduced last week as a way to package premium services for...
Agentic AI for Remote Sensing: Technical Challenges and Research Directions
arXiv:2604.24919v3 Announce Type: replace Abstract: Earth Observation (EO) is moving beyond static prediction toward multi-step analytical workflows that require coordinated reasoning over data, tools, and geospatial state. While foundation models and vision-language models have advanced representation learning and language-grounded interaction in remote sensing, and agentic AI has shown strong potential for long-horizon reasoning and tool use, EO is not a straightforward extension of...
First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope
arXiv:2605.28916v2 Announce Type: replace-cross Abstract: We report a comparison of two state-of-the-art agentic AI systems, Claude Code (Anthropic) and Codex (OpenAI), tasked with autonomously executing a simple end-to-end gravitational wave data analysis pipeline on a shared computing infrastructure without human intervention. The pipeline comprises power spectral density estimation from raw Einstein Telescope simulated noise, geometric template bank generation, matched filter recovery of...
Harness Engineering for Physical AI: Robot Middleware Is the Harness Layer
arXiv:2606.09416v1 Announce Type: new Abstract: Robot middleware faces a new role in the era of Physical AI. Learned policies, planners, and vision-language-action (VLA) models now enter deployed robots as causal participants on the control path, but the layer that integrates them with timing, scheduling, and network has not been named. Recent language-agent work names this layer the harness, the external system that mediates tools, manages state, bounds resources, and records execution.