Minimum Bayes risk (MBR) decoding is a decision rule of text generation, which selects the hypothesis that maximizes the expected utility and robustly generates higher-quality texts than maximum a posteriori (MAP) decoding. However, it depends on sample texts drawn from the text generation model; thus, it is difficult to find a hypothesis that correctly captures the knowledge or information of out-of-domain. To tackle this issue, we propose case-based decision-theoretic (CBDT) decoding, another method to estimate the expected utility using examples of domain data. CBDT decoding not only generates higher-quality texts than MAP decoding, but also the combination of MBR and CBDT decoding outperformed MBR decoding in seven domain De--En and Ja$\leftrightarrow$En translation tasks and image captioning tasks on MSCOCO and nocaps datasets.

Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis Yuan-Zheng Lei a, Yaobang Gong a, Dianwei Chen a, Yao Cheng b, Xianfeng Terry Yang* a a University of Maryland, College Park, MD 20742, United States b Florida Atlantic University, Boca Raton, FL 33431, United StatesAbstract This study investigates why physics-informed machine learning (PIML) may fail when it comes to macroscopic traffic flow modeling. We define failure as the case where a PIML model underperforms both its purely data-driven and purely physics-based counterparts by a given threshold. Our analysis shows that physics residuals themselves do not inherently hinder the optimization of the loss function, which is a main reason responsible for the failure of the PIML model in other fields. Instead, successful parameter updates require both machine-learning and physics gradients to form acute angles with the true gradient. Our experiment shows that this condition may be hard to achieve for PIML under a general low-resolution loop dataset. In particular, when the traffic data resolution is low, a neural network cannot accurately approximate density and speed, causing the constructed physics residuals, already affected by discrete sampling and temporal averaging, to lose their ability to reflect the actual PDE dynamics. This degradation can directly lead to PIML failure. From a theoretical standpoint, we show that although the exact solutions of the LWR and ARZ models are weak solutions, for piecewise C k initial data and under mild conditions, the solutions remain C k on the complement of the shock set over finite time, with only finitely many shock waves, where C k refers to k times continuously differentiable. Since the shock set has Lebesgue measure zero, the probability of a detector measurement or auxiliary collocation point lying exactly on a discontinuity is essentially zero; asymptotically, every auxiliary point admits a sufficiently small smooth neighborhood where the physics residual is well-defined and valid. Consequently, the well-known limitation that MLPs cannot exactly represent non-smooth functions does not materially affect our setting, as the residual evaluation almost always occurs in smooth regions. We also investigate the error lower bounds of the MSE of physics residuals for PIML models under high-resolution data. We prove that higher-order models like ARZ possess strictly larger consistency error lower bounds than lower-order models like LWR under mild conditions.

We revisit hierarchical bracketing encodings from a practical perspective in the context of dependency graph parsing. The approach encodes graphs as sequences, enabling linear-time parsing with $n$ tagging actions, and still representing reentrancies, cycles, and empty nodes. Compared to existing graph linearizations, this representation substantially reduces the label space while preserving structural information. We evaluate it on a multilingual and multi-formalism benchmark, showing competitive results and consistent improvements over other methods in exact match accuracy.

A fundamental requirement for full autonomy in mobile robots is accurate navigation even in situations where satellite navigation or cameras are unavailable. In such practical situations, relying only on inertial sensors will result in navigation solution drift due to the sensors' inherent noise and error terms. One of the emerging solutions to mitigate drift is to maneuver the robot in a snake-like slithering motion to increase the inertial signal-to-noise ratio, allowing the regression of the mobile robot position. In this work, we propose MoRPI-PINN as a physics-informed neural network framework for accurate inertial-based mobile robot navigation. By embedding physical laws and constraints into the training process, MoRPI-PINN is capable of providing an accurate and robust navigation solution. Using real-world experiments, we show accuracy improvements of over 85% compared to other approaches. MoRPI-PINN is a lightweight approach that can be implemented even on edge devices and used in any typical mobile robot application.

Distinguishing correlation from causation is a fundamental challenge in machine intelligence, often representing a critical barrier to building robust and trustworthy systems. While Pearl's $\mathcal{DO}$-calculus provides a rigorous framework for causal inference, a parallel challenge lies in its physical implementation. Here, we apply and experimentally validate a quantum algorithmic framework for performing causal interventions. Our approach maps causal networks onto quantum circuits where probabilistic links are encoded by controlled-rotation gates, and interventions are realized by a structural remodeling of the circuit -- a physical analogue to Pearl's ``graph surgery''. We demonstrate the method's efficacy by resolving Simpson's Paradox in a 3-qubit model, and show its scalability by quantifying confounding bias in a 10-qubit healthcare simulation. Critically, we provide a proof-of-principle experimental validation on an IonQ Aria quantum computer, successfully reproducing the paradox and its resolution in the presence of real-world noise. This work establishes a practical pathway for quantum causal inference, offering a new computational tool to address deep-rooted challenges in algorithmic fairness and explainable AI (XAI).

We propose a general two-period model where electrical vehicles (EVs) can reserve charging sessions in the day-ahead market and swap them in the real-time market. Under the model, we explore several candidate mechanisms for running the two markets, compared using several normative properties such as incentive compatibility, efficiency, reservation awareness, and budget balance. Specifically, reservation awareness is the only property coupling the two markets and dictates that an EV will not get a lower utility by joining the real-time market. Focusing on the real-time market, we show that two variants of the classical Vickrey-Clarke-Groves (VCG) mechanism do not satisfy all the proposed properties; specifically, one is not reservation-aware, while the other is not budget-balanced. Moreover, we show that no mechanism satisfies some combinations of the properties. Then, we propose to use a posted-price mechanism to resolve the issue, which turns out to be the dynamic pricing mechanism adopted in many real-world systems. The proposed mechanism has no efficiency guarantee but satisfies all the other properties. To improve efficiency, we propose to use a VCG auction in the day-ahead market that guides the reserve prices in the real-time market. When EVs' valuations in the two markets are highly correlated, the proposed approach results in highly efficient outcomes.

Unified physics-based humanoid controllers are pivotal for robotics and character animation, yet models that excel on gentle, everyday motions still stumble on explosive actions, hampering real-world deployment. We bridge this gap with FARM (Frame-Accelerated Augmentation and Residual Mixture-of-Experts), an end-to-end framework composed of frame-accelerated augmentation, a robust base controller, and a residual mixture-of-experts (MoE). Frame-accelerated augmentation exposes the model to high-velocity pose changes by widening inter-frame gaps. The base controller reliably tracks everyday low-dynamic motions, while the residual MoE adaptively allocates additional network capacity to handle challenging high-dynamic actions, significantly enhancing tracking accuracy. In the absence of a public benchmark, we curate the High-Dynamic Humanoid Motion (HDHM) dataset, comprising 3593 physically plausible clips. On HDHM, FARM reduces the tracking failure rate by 42.8\% and lowers global mean per-joint position error by 14.6\% relative to the baseline, while preserving near-perfect accuracy on low-dynamic motions. These results establish FARM as a new baseline for high-dynamic humanoid control and introduce the first open benchmark dedicated to this challenge. The code and dataset will be released at https://github.com/Colin-Jing/FARM.

We present a hybrid surrogate model for electric vehicle parameter estimation and power consumption. We combine our novel architecture Spectral Parameter Operator built on a Fourier Neural Operator backbone for global context and a differentiable physics module in the forward pass. From speed and acceleration alone, it outputs time-varying motor and regenerative braking efficiencies, as well as aerodynamic drag, rolling resistance, effective mass, and auxiliary power. These parameters drive a physics-embedded estimate of battery power, eliminating any separate physics-residual loss. The modular design lets representations converge to physically meaningful parameters that reflect the current state and condition of the vehicle. We evaluate on real-world logs from a Tesla Model 3, Tesla Model S, and the Kia EV9. The surrogate achieves a mean absolute error of 0.2kW (about 1% of average traction power at highway speeds) for Tesla vehicles and about 0.8kW on the Kia EV9. The framework is interpretable, and it generalizes well to unseen conditions, and sampling rates, making it practical for path optimization, eco-routing, on-board diagnostics, and prognostics health management.

Physics - Based Explainable AI for ECG Segmentation: A Lightweight Model Muhammad Fathur Rohman Sidiq Department of Physics, Faculty of Mathematics and Science, Brawijaya University, Malang, Indonesia Abdurrouf Department of Physics, Faculty of Mathematics and Science, Brawijaya University, Malang, Indonesia Didik Rahadi Santoso * Department of Physics, Faculty of Mathematics and Science, Brawijaya University, Malang, Indonesia * Corresponding author. E - mail: dieks@ub.ac.id Abstract The heart's electrical activity, recorded through Electrocardiography (ECG), is essential for diagnosing various cardiovascular conditions. However, many existing ECG segmentation models rely on complex, multi - layered architectures such as BiLSTM, which ar e computationally intensive and inefficient. This study introduces a streamlined architecture that combines spectral analysis with probabilistic predictions for ECG signal segmentation. Additionally, an Explainable AI (XAI) approach is applied to enhance model interpretability by explaining how temporal and frequency - based features contribute to ECG segmentation. By i ncorporating principles from physics - based AI, this method provides a clear understanding of the decision - making process, ensuring reliability and transparency in ECG analysis.