Physical Bounds
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Physical Bounds on Optical Micromanipulation: Maximal Stiffness in the Dipole Regime
Announce Type: new Abstract: Optical trapping and micromanipulation rely on carefully shaped electromagnetic fields to exert precise forces and torques on microscopic particles. Despite their widespread application in biology and nanotechnology, the absolute physical limits of trapping performance, specifically the maximum achievable optical force and trap stiffness, have not yet been rigorously quantified. This work establishes a general theoretical framework to determine these fundamental...
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.
Taming the Loss Landscape of PINNs with Noisy Feynman-Kac Supervision: Operator Preconditioning and Non-Asymptotic Error Bounds
arXiv:2606.00643v1 Announce Type: cross Abstract: Physics-Informed Neural Networks (PINNs) often train slowly or fail to converge on challenging partial differential equations (PDEs), a behavior recently linked to severely ill-conditioned loss landscapes inherited from the underlying differential operator. We study PINNs augmented with a pointwise data-fidelity term, added at a few points in the domain to the standard residual and boundary losses. We show that this supervision term acts as...
Reducing Arbitrary Metric Temporal Formulas into Logic Programs under Answer Set Semantics
arXiv:2605.30618v1 Announce Type: new Abstract: Metric temporal equilibrium logic (\MEL) extends temporal equilibrium logic (\TEL) by incorporating quantitative timing constraints, enabling the specification and analysis of deadlines and durations. \MEL\ is particularly suited for domains where time-bound properties are crucial, such as embedded systems, cyber-physical systems, and real-time software.
Physics-Informed Modeling and Control of Emergent Behaviors in Robot Swarms
arXiv:2606.01597v1 Announce Type: new Abstract: Robot swarms can exhibit coherent collective behaviors through local perception, limited communication and decentralized decision-making, yet modeling and controlling such emergence remains challenging when behaviors unfold over multiple phases. Here we introduce PhySwarm, a physics-informed micro--macro framework that represents multi-stage swarm emergence as physically constrained density-field evolution coupled to executable robot motion. At...
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...
veriFIRE: an Industrial Case Study in Verifying Consistency Properties for a DNN-Based Wildfire Detection System
Announce Type: new Abstract: We present our ongoing work on the veriFIRE project: a collaboration between industry and academia, aimed at applying verification to increase the reliability of a real-world, safety-critical system. Specifically, we target an airborne platform for wildfire detection, which incorporates two deep neural networks. We present an end-to-end methodology for verifying \textit{consistency properties} in this system.
Near-Optimal Mixed Strategy for Zero-Sum Linear-Quadratic Differential Games
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No-Harm Physics-Informed Inverse Learning with Residual-Calibrated Uncertainty
arXiv:2606.07153v1 Announce Type: new Abstract: Physics-informed learning is increasingly used for partial differential equation (PDE)-governed inverse problems, but its reliability remains difficult to certify. This paper develops a no-harm certification-and-selection framework for physics-informed inverse learning. A learned reconstruction is accepted only when its residual-calibrated radius is no worse than the baseline radius, namely when $$R_{\mathrm{learn}}\le...
FewBodyToolkit.jl: a Julia package for solving quantum few-body problems
arXiv:2510.04447v2 Announce Type: replace-cross Abstract: Few-body physics explores quantum systems of a small number of particles, bridging the gap between single-particle and many-body regimes. To provide an accessible tool for such studies, we present FewBodyToolkit.jl, a Julia package for quantum few-body simulations. The package supports general two- and three-body systems in various spatial dimensions with arbitrary pair-interactions, and allows to calculate bound and resonant states.