Score-based diffusion models have achieved incredible performance in generating realistic images, audio, and video data. While these models produce high-quality samples with impressive details, they often introduce unrealistic artifacts, such as distorted fingers or hallucinated texts with no meaning. This paper focuses on textual hallucinations, where diffusion models correctly generate individual symbols but assemble them in a nonsensical manner. Through experimental probing, we consistently observe that such phenomenon is attributed it to the network's local generation bias. Denoising networks tend to produce outputs that rely heavily on highly correlated local regions, particularly when different dimensions of the data distribution are nearly pairwise independent. This behavior leads to a generation process that decomposes the global distribution into separate, independent distributions for each symbol, ultimately failing to capture the global structure, including underlying grammar. Intriguingly, this bias persists across various denoising network architectures including MLP and transformers which have the structure to model global dependency. These findings also provide insights into understanding other types of hallucinations, extending beyond text, as a result of implicit biases in the denoising models. Additionally, we theoretically analyze the training dynamics for a specific case involving a two-layer MLP learning parity points on a hypercube, offering an explanation of its underlying mechanism. Inspired by the diffusion process in physics (Sohl-Dickstein et al., 2015), diffusion models learn to generate samples from a specific data distribution by fitting its score function, gradually transforming pure Gaussian noise into desired samples. However, despite the impressively realistic details produced, diffusion models consistently exhibit artifacts in their outputs. One common issue is the generation of plausible low-level features or local details while failing to accurately model complex 3D objects or the underlying semantics (Borji, 2023; Liu et al., 2023).

Machine learning and optimisation techniques (MLOPT) hold significant potential to accelerate the decarbonisation of industrial systems by enabling data-driven operational improvements. However, the practical application of MLOPT in industrial settings is often hindered by a lack of domain compliance and system-specific consistency, resulting in suboptimal solutions with limited real-world applicability. To address this challenge, we propose a novel human-in-the-loop (HITL) constraint-based optimisation framework that integrates domain expertise with data-driven methods, ensuring solutions are both technically sound and operationally feasible. We demonstrate the efficacy of this framework through a case study focused on enhancing the thermal efficiency and reducing the turbine heat rate of a 660 MW supercritical coal-fired power plant. By embedding domain knowledge as constraints within the optimisation process, our approach yields solutions that align with the plant's operational patterns and are seamlessly integrated into its control systems. Empirical validation confirms a mean improvement in thermal efficiency of 0.64\% and a mean reduction in turbine heat rate of 93 kJ/kWh. Scaling our analysis to 59 global coal power plants with comparable capacity and fuel type, we estimate a cumulative lifetime reduction of 156.4 million tons of carbon emissions. These results underscore the transformative potential of our HITL-MLOPT framework in delivering domain-compliant, implementable solutions for industrial decarbonisation, offering a scalable pathway to mitigate the environmental impact of coal-based power generation worldwide.

The dataset includes more than 6400 videos across 22 real-world object categories, interacting with robot arms and motors, and exhibits Humans detect real-world object anomalies by perceiving, 47 types of anomalies. Anomaly detection in Phys-AD requires interacting, and reasoning based on object-conditioned physical visual reasoning, combining both physical knowledge knowledge. The long-term goal of Industrial Anomaly and video content to determine object abnormality. We benchmark Detection (IAD) is to enable machines to autonomously replicate state-of-the-art anomaly detection methods under three this skill. However, current IAD algorithms are largely settings: unsupervised AD, weakly-supervised AD, and videounderstanding developed and tested on static, semantically simple datasets, AD, highlighting their limitations in handling which diverge from real-world scenarios where physical physics-grounded anomalies. Additionally, we introduce the understanding and reasoning are essential. To bridge this Physics Anomaly Explanation (PAEval) metric, designed to gap, we introduce the Physics Anomaly Detection (Phys-AD) assess the ability of visual-language foundation models to not dataset, the first large-scale, real-world, physics-grounded only detect anomalies but also provide accurate explanations video dataset for industrial anomaly detection.

Unmanned Surface Vehicles (USVs) are increasingly applied to water operations such as environmental monitoring and river-map modeling. It faces a significant challenge in achieving precise autonomous docking at ports or stations, still relying on remote human control or external positioning systems for accuracy and safety which limits the full potential of human-out-of-loop deployment for USVs.This paper introduces a novel supervised learning pipeline with the auto-labeling technique for USVs autonomous visual docking. Firstly, we designed an auto-labeling data collection pipeline that appends relative pose and image pair to the dataset. This step does not require conventional manual labeling for supervised learning. Secondly, the Neural Dock Pose Estimator (NDPE) is proposed to achieve relative dock pose prediction without the need for hand-crafted feature engineering, camera calibration, and peripheral markers. Moreover, The NDPE can accurately predict the relative dock pose in real-world water environments, facilitating the implementation of Position-Based Visual Servo (PBVS) and low-level motion controllers for efficient and autonomous docking.Experiments show that the NDPE is robust to the disturbance of the distance and the USV velocity. The effectiveness of our proposed solution is tested and validated in real-world water environments, reflecting its capability to handle real-world autonomous docking tasks.

The ever-growing availability of unlabeled data presents both opportunities and challenges for training artificial intelligence systems. While self-supervised learning (SSL) has emerged as a powerful paradigm for extracting meaningful representations from vast amounts of unlabeled data, existing methods still struggle to adapt to the non-stationary, non-IID nature of real-world data streams without forgetting previously learned knowledge. Recent works have adopted a repeated cosine annealing schedule for large-scale continual pre-training; however, these schedules (1) inherently cause forgetting during the re-warming phase and (2) have not been systematically compared to existing continual SSL methods. In this work, we systematically compare the widely used cosine schedule with the recently proposed infinite learning rate schedule and empirically find the latter to be a more effective alternative. Our extensive empirical evaluation across diverse image and language datasets demonstrates that the infinite learning rate schedule consistently enhances continual pre-training performance compared to a repeated cosine decay without being restricted to a fixed iteration budget. For instance, in a small-scale MAE pre-training setup, it outperforms several strong baselines from the literature. Our results show that the infinite learning rate schedule remains effective at scale, surpassing repeated cosine decay for both MAE pre-training and zero-shot LM benchmarks. These models are known for their massive parameter counts and extensive training on vast amounts of data, often developing impressive general-purpose capabilities unexpectedly during pre-training (Brown et al., 2020; Wei et al., 2022). While foundation models have demonstrated remarkable success on static tasks, adapting them to evolving data--such as the continuous influx of new textual information (Soldaini et al., 2024; Li et al., 2024; Abadji et al., 2022; Kocetkov et al., 2022) and the emergence of novel visual concepts (Prabhu et al., 2023; Seo et al., 2024)--remains a major challenge.

For example, realistic Time-series Generation (TSG) is a prominent synthetic medical electrocardiogram (ECG) patterns research area with broad applications in simulations, can be used to train medical residents (Hong & Chun, 2023), data augmentation, and counterfactual while simulating regional electricity usage can be used to analysis. While existing methods have shown stress test the power grid (Westgaard et al., 2021). Although promise in unconditional single-domain TSG, some remarkable works (Huang & Deng, 2023; Bao et al., real-world applications demand for cross-domain 2024) have been done for TSG, showing promising results approaches capable of controlled generation tailored in generating realistic and coherent time series (TS), most to domain-specific constraints and instancelevel of them focus on the basic setting--unconditional single requirements. In this paper, we argue that domain generation. However, in real application scenarios, text can provide semantic insights, domain information there are specific constraints or requirements for the generated and instance-specific temporal patterns, TS to be met, such as specifying domain-specific characteristics, to guide and improve TSG. We introduce "Text-incorporating prior knowledge (Yuan & Qiao, Controlled TSG", a task focused on generating realistic 2024), or satisfying operational constraints (Coletta et al., time series by incorporating textual descriptions.

The Bellman equation and its continuous-time counterpart, the Hamilton-Jacobi-Bellman (HJB) equation, serve as necessary conditions for optimality in reinforcement learning and optimal control. While the value function is known to be the unique solution to the Bellman equation in tabular settings, we demonstrate that this uniqueness fails to hold in continuous state spaces. Specifically, for linear dynamical systems, we prove the Bellman equation admits at least $\binom{2n}{n}$ solutions, where $n$ is the state dimension. Crucially, only one of these solutions yields both an optimal policy and a stable closed-loop system. We then demonstrate a common failure mode in value-based methods: convergence to unstable solutions due to the exponential imbalance between admissible and inadmissible solutions. Finally, we introduce a positive-definite neural architecture that guarantees convergence to the stable solution by construction to address this issue.

Quantifying the uncertainty of machine learning models is crucial for numerous applications, particularly in large-scale real-world scenarios where prediction sets, rather than point predictions, enable more flexible and informed decision making. Uncertainty quantification (UQ) methods are essential for characterizing the unpredictibility arising in various real-world problems across science and engineering. Initially proposed by Vovk et al. [2005], CP is a popular distribution-free method for UQ, largely due to its ability to provide finite-sample coverage guarantees and its computational efficiency. Most studies in CP focus on constructing prediction intervals based on a fitted mean model. This work introduces a novel setting where the value of interest is estimated using only a pair of valid upper and lower bounds, instead of a mean model. While valid bounds themselves provide perfect coverage by definition, they can sometimes be overly conservative. By slightly reducing the coverage level, these bounds can be tightened, resulting in significantly smaller intervals with theoretical guarantees and greater utility for decision making.

High-precision manipulation has always been a developmental goal for aerial manipulators. This paper investigates the kinematic coordinate control issue in aerial manipulators. We propose a predictive kinematic coordinate control method, which includes a learning-based modified kinematic model and a model predictive control (MPC) scheme based on weight allocation. Compared to existing methods, our proposed approach offers several attractive features. First, the kinematic model incorporates closed-loop dynamics characteristics and online residual learning. Compared to methods that do not consider closed-loop dynamics and residuals, our proposed method has improved accuracy by 59.6$\%$. Second, a MPC scheme that considers weight allocation has been proposed, which can coordinate the motion strategies of quadcopters and manipulators. Compared to methods that do not consider weight allocation, the proposed method can meet the requirements of more tasks. The proposed approach is verified through complex trajectory tracking and moving target tracking experiments. The results validate the effectiveness of the proposed method.

Reactor physics is the study of neutron properties, focusing on using models to examine the interactions between neutrons and materials in nuclear reactors. Artificial intelligence (AI) has made significant contributions to reactor physics, e.g., in operational simulations, safety design, real-time monitoring, core management and maintenance. This paper presents a comprehensive review of AI approaches in reactor physics, especially considering the category of Machine Learning (ML), with the aim of describing the application scenarios, frontier topics, unsolved challenges and future research directions. From equation solving and state parameter prediction to nuclear industry applications, this paper provides a step-by-step overview of ML methods applied to steady-state, transient and combustion problems. Most literature works achieve industry-demanded models by enhancing the efficiency of deterministic methods or correcting uncertainty methods, which leads to successful applications. However, research on ML methods in reactor physics is somewhat fragmented, and the ability to generalize models needs to be strengthened. Progress is still possible, especially in addressing theoretical challenges and enhancing industrial applications such as building surrogate models and digital twins.