arXiv subjects

Hao Wang

Publications and source records attributed to Hao Wang.

At least 667 records · Page 37Linked to original sources

arXiv2025

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers

The growing integration of renewable energy sources in modern power systems has introduced significant operational challenges due to their intermittent and uncertain outputs. In recent years, mobile energy storage systems (ESSs) have emerged as a popular flexible resource for mitigating these challenges. Compared to stationary ESSs, mobile ESSs offer additional spatial flexibility, enabling cost-effective energy delivery through the transportation network. However, the widespread deployment of mobile ESSs is often hindered by the high investment cost, which has motivated researchers to investigate utilising more readily available alternatives, such as electric vehicles (EVs) as mobile energy storage units instead. Hence, we explore this opportunity with a MIP-based day-ahead electric vehicle joint routing and scheduling problem in this work. However, solving the problem in a practical setting can often be computationally intractable since the existence of binary variables makes it combinatorial challenging. Therefore, we proposed to simplify the problem's solution process for a MIP solver by pruning the solution search space with a transformer-based deep learning (DL) model. This is done by training the model to rapidly predict the optimal binary solutions. In addition, unlike many existing DL approaches that assume fixed problem structures, the proposed model is designed to accommodate problems with EV fleets of any sizes. This flexibility is essential since frequent re-training can introduce significant computational overhead. We evaluated the approach with simulations on the IEEE 33-bus system coupled with the Nguyen-Dupuis transportation network.

eess.SY↗

arXiv2025

An Uncertainty-aware DETR Enhancement Framework for Object Detection

This paper investigates the problem of object detection with a focus on improving both the localization accuracy of bounding boxes and explicitly modeling prediction uncertainty. Conventional detectors rely on deterministic bounding box regression, ignoring uncertainty in predictions and limiting model robustness. In this paper, we propose an uncertainty-aware enhancement framework for DETR-based object detectors. We model bounding boxes as multivariate Gaussian distributions and incorporate the Gromov-Wasserstein distance into the loss function to better align the predicted and ground-truth distributions. Building on this, we derive a Bayes Risk formulation to filter high-risk information and improve detection reliability. We also propose a simple algorithm to quantify localization uncertainty via confidence intervals. Experiments on the COCO benchmark show that our method can be effectively integrated into existing DETR variants, enhancing their performance. We further extend our framework to leukocyte detection tasks, achieving state-of-the-art results on the LISC and WBCDD datasets. These results confirm the scalability of our framework across both general and domain-specific detection tasks. Code page: https://github.com/ParadiseforAndaChen/An-Uncertainty-aware-DETR-Enhancement-Framework-for-Object-Detection.

cs.CV↗

arXiv2025

RegGS: Unposed Sparse Views Gaussian Splatting with 3DGS Registration

3D Gaussian Splatting (3DGS) has demonstrated its potential in reconstructing scenes from unposed images. However, optimization-based 3DGS methods struggle with sparse views due to limited prior knowledge. Meanwhile, feed-forward Gaussian approaches are constrained by input formats, making it challenging to incorporate more input views. To address these challenges, we propose RegGS, a 3D Gaussian registration-based framework for reconstructing unposed sparse views. RegGS aligns local 3D Gaussians generated by a feed-forward network into a globally consistent 3D Gaussian representation. Technically, we implement an entropy-regularized Sinkhorn algorithm to efficiently solve the optimal transport Mixture 2-Wasserstein $(\text{MW}_2)$ distance, which serves as an alignment metric for Gaussian mixture models (GMMs) in $\mathrm{Sim}(3)$ space. Furthermore, we design a joint 3DGS registration module that integrates the $\text{MW}_2$ distance, photometric consistency, and depth geometry. This enables a coarse-to-fine registration process while accurately estimating camera poses and aligning the scene. Experiments on the RE10K and ACID datasets demonstrate that RegGS effectively registers local Gaussians with high fidelity, achieving precise pose estimation and high-quality novel-view synthesis. Project page: https://3dagentworld.github.io/reggs/.

cs.CV↗

arXiv2025

Learning Deformable Body Interactions With Adaptive Spatial Tokenization

Simulating interactions between deformable bodies is vital in fields like material science, mechanical design, and robotics. While learning-based methods with Graph Neural Networks (GNNs) are effective at solving complex physical systems, they encounter scalability issues when modeling deformable body interactions. To model interactions between objects, pairwise global edges have to be created dynamically, which is computationally intensive and impractical for large-scale meshes. To overcome these challenges, drawing on insights from geometric representations, we propose an Adaptive Spatial Tokenization (AST) method for efficient representation of physical states. By dividing the simulation space into a grid of cells and mapping unstructured meshes onto this structured grid, our approach naturally groups adjacent mesh nodes. We then apply a cross-attention module to map the sparse cells into a compact, fixed-length embedding, serving as tokens for the entire physical state. Self-attention modules are employed to predict the next state over these tokens in latent space. This framework leverages the efficiency of tokenization and the expressive power of attention mechanisms to achieve accurate and scalable simulation results. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches in modeling deformable body interactions. Notably, it remains effective on large-scale simulations with meshes exceeding 100,000 nodes, where existing methods are hindered by computational limitations. Additionally, we contribute a novel large-scale dataset encompassing a wide range of deformable body interactions to support future research in this area.

cs.LG↗

arXiv2025

In-flight Characteristics and Modelling of the Instrumental Background of EP/FXT

The in-flight instrumental background of the Follow-up X-ray Telescope (FXT) onboard Einstein Probe (EP) mission is analysed in this work by utilizing observations collected during Performance Verification phase and subsequent dedicated filter wheel closed observations. The instrumental backgrounds of the two FXT modules are consistent with each other, with an average rate of $\sim 4\times10^{-2}$\,counts/s/keV in the 0.5--10\,keV band for each module. The background is nearly uniformly distributed across the detector area, with a minor increase ($<8\%$) observed along rows. The spatial distribution shows significant modulation by the geomagnetic field. The spectral shapes remain unchanged in 0.5--10\,keV at different rates. The long-term temporal variation indicates a periodic change associated with the orbital precession ($\sim 57$ days). The innovative design of FXT full-frame readout mode enables simultaneous recording of events in both the imaging area (IMG) and the frame store area (FSA) of the pnCCD. FSA event rates show a strong linear correlation with the IMG, based on which the IMG instrumental background modelling is established.

astro-ph.IM↗

arXiv2025

Impact Analysis of Optimal EV Bi-directional Charging with Spatial-temporal Constraints

The growth in Electric Vehicle (EV) market share is expected to increase power demand on distribution networks. Uncoordinated residential EV charging, based on driving routines, creates peak demand at various zone substations depending on location and time. Leveraging smart charge scheduling and Vehicle-to-Grid (V2G) technologies offers opportunities to adjust charge schedules, allowing for load shifting and grid support, which can reduce both charging costs and grid stress. In this work, we develop a charge scheduling optimization method that can be used to assess the impact of spatial power capacity constraints and real-time price profiles. We formulate a mixed-integer linear programming problem to minimize overall charging costs, taking into account factors such as time-varying EV locations, EV charging requirements, and local power demands across different zones. Our analysis uses real data for pricing signals and local power demands, combined with simulated data for EV driving plans. Four metrics are introduced to assess impacts from the perspectives of both EV users and zones. Results indicate that overall EV charging costs are only minimally affected under extreme power capacity constraints.

math.OC↗

arXiv2025

Precise Measurement of $^{216}$Po Half-life with Exact Parent-daughter Pairing in PandaX-4T

We report a precise measurement of $^{216}\rm Po$ half-life using the PandaX-4T liquid xenon time projection chamber (TPC). $^{220}\rm Rn $, emanating from a $^{228}\rm Th $ calibration source, is injected to the detector and undergoes successive $α$ decays, first to $^{216}\rm Po$ and then to $^{212}\rm Pb$. PandaX-4T detector measures the 5-dimensional (5D) information of each decay, including time, energy, and 3-dimensional positions. Therefore, we can identify the $^{220}\rm Rn $ and $^{216}\rm Po$ decay events and pair them exactly to extract the lifetime of each $^{216}\rm Po$. With a large data set and high-precision $^{220}\rm $Rn-$^{216}\rm $Po pairing technique, we measure the $^{216}\rm Po$ half-life to be $143.7\pm0.5$ ms, which is the most precise result to date and agrees with previously published values. The leading precision of this measurement demonstrates the power of 5D calorimeter and the potential of exact parent-daughter pairing in the xenon TPC.

nucl-ex↗

arXiv2025

SeqCSIST: Sequential Closely-Spaced Infrared Small Target Unmixing

Due to the limitation of the optical lens focal length and the resolution of the infrared detector, distant Closely-Spaced Infrared Small Target (CSIST) groups typically appear as mixing spots in the infrared image. In this paper, we propose a novel task, Sequential CSIST Unmixing, namely detecting all targets in the form of sub-pixel localization from a highly dense CSIST group. However, achieving such precise detection is an extremely difficult challenge. In addition, the lack of high-quality public datasets has also restricted the research progress. To this end, firstly, we contribute an open-source ecosystem, including SeqCSIST, a sequential benchmark dataset, and a toolkit that provides objective evaluation metrics for this special task, along with the implementation of 23 relevant methods. Furthermore, we propose the Deformable Refinement Network (DeRefNet), a model-driven deep learning framework that introduces a Temporal Deformable Feature Alignment (TDFA) module enabling adaptive inter-frame information aggregation. To the best of our knowledge, this work is the first endeavor to address the CSIST Unmixing task within a multi-frame paradigm. Experiments on the SeqCSIST dataset demonstrate that our method outperforms the state-of-the-art approaches with mean Average Precision (mAP) metric improved by 5.3\%. Our dataset and toolkit are available from https://github.com/GrokCV/SeqCSIST.

cs.CV↗

arXiv2025

Sensitive infrared surface photovoltage in quasi-equilibrium in a layered semiconductor at low-intensity low-temperature condition

Benefit to layer-dependent bandgap, van der Waals materials with surface photovoltaic effect (SPV) enable photodetection over a tunable wavelength range with low power consumption. However, sensitive SPV in the infrared region, especially in a quasi-steady illumination condition, is still elusive in layered semiconductors. Here, using angle-resolved photoemission spectroscopy, we report a sensitive SPV in quasi-equilibrium in NbSi0.5Te2, with photoresponsivity up to 2.4*10^6 V/(W*cm^(-2)) at low intensity low temperature condition (LILT). The sensitive SPV is further confirmed by observing the Dember effect, where the photogenerated carrier density is high enough and diffusion currents suppress SPV. Temperature-dependent measurements indicate that intrinsic carriers freezing at low temperature leads to the ultrahigh photoresponse, while a small amount of photon-generated carriers in quasi-equilibrium dominate the system. Our work not only provides a promising layered semiconductor for Infrared optoelectronic devices with strong infrared SPV at LILT, which has application potential in fields such as quantum information and deep-space exploration, but also paves a novel way to enhance light-matter interaction effect by freezing bulk carriers.

cond-mat.mtrl-sci↗

arXiv2025

From Language to Logic: A Bi-Level Framework for Structured Reasoning

Structured reasoning over natural language inputs remains a core challenge in artificial intelligence, as it requires bridging the gap between unstructured linguistic expressions and formal logical representations. In this paper, we propose a novel \textbf{bi-level framework} that maps language to logic through a two-stage process: high-level task abstraction and low-level logic generation. At the upper level, a large language model (LLM) parses natural language queries into intermediate structured representations specifying the problem type, objectives, decision variables, and symbolic constraints. At the lower level, the LLM uses these representations to generate symbolic workflows or executable reasoning programs for accurate and interpretable decision making. The framework supports modular reasoning, enforces explicit constraints, and generalizes across domains such as mathematical problem solving, question answering, and logical inference. We further optimize the framework with an end-to-end {bi-level} optimization approach that jointly refines both the high-level abstraction and low-level logic generation stages. Experiments on multiple realistic reasoning benchmarks demonstrate that our approach significantly outperforms existing baselines in accuracy, with accuracy gains reaching as high as 40\%. Moreover, the bi-level design enhances transparency and error traceability, offering a promising step toward trustworthy and systematic reasoning with LLMs.

cs.AI↗

arXiv2025

Boundary Learning by Using Weighted Propagation in Convolution Network

In material science, image segmentation is of great significance for quantitative analysis of microstructures. Here, we propose a novel Weighted Propagation Convolution Neural Network based on U-Net (WPU-Net) to detect boundary in poly-crystalline microscopic images. We introduce spatial consistency into network to eliminate the defects in raw microscopic image. And we customize adaptive boundary weight for each pixel in each grain, so that it leads the network to preserve grain's geometric and topological characteristics. Moreover, we provide our dataset with the goal of advancing the development of image processing in materials science. Experiments demonstrate that the proposed method achieves promising performance in both of objective and subjective assessment. In boundary detection task, it reduces the error rate by 7\%, which outperforms state-of-the-art methods by a large margin.

cs.CV↗

arXiv2025

A Survey on Latent Reasoning

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, especially when guided by explicit chain-of-thought (CoT) reasoning that verbalizes intermediate steps. While CoT improves both interpretability and accuracy, its dependence on natural language reasoning limits the model's expressive bandwidth. Latent reasoning tackles this bottleneck by performing multi-step inference entirely in the model's continuous hidden state, eliminating token-level supervision. To advance latent reasoning research, this survey provides a comprehensive overview of the emerging field of latent reasoning. We begin by examining the foundational role of neural network layers as the computational substrate for reasoning, highlighting how hierarchical representations support complex transformations. Next, we explore diverse latent reasoning methodologies, including activation-based recurrence, hidden state propagation, and fine-tuning strategies that compress or internalize explicit reasoning traces. Finally, we discuss advanced paradigms such as infinite-depth latent reasoning via masked diffusion models, which enable globally consistent and reversible reasoning processes. By unifying these perspectives, we aim to clarify the conceptual landscape of latent reasoning and chart future directions for research at the frontier of LLM cognition. An associated GitHub repository collecting the latest papers and repos is available at: https://github.com/multimodal-art-projection/LatentCoT-Horizon/.

cs.CL↗

arXiv2025

Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis

While multiple instance learning (MIL) has shown to be a promising approach for histopathological whole slide image (WSI) analysis, its reliance on permutation invariance significantly limits its capacity to effectively uncover semantic correlations between instances within WSIs. Based on our empirical and theoretical investigations, we argue that approaches that are not permutation-invariant but better capture spatial correlations between instances can offer more effective solutions. In light of these findings, we propose a novel alternative to existing MIL for WSI analysis by learning to restore the order of instances from their randomly shuffled arrangement. We term this task as cracking an instance jigsaw puzzle problem, where semantic correlations between instances are uncovered. To tackle the instance jigsaw puzzles, we propose a novel Siamese network solution, which is theoretically justified by optimal transport theory. We validate the proposed method on WSI classification and survival prediction tasks, where the proposed method outperforms the recent state-of-the-art MIL competitors. The code is available at https://github.com/xiwenc1/MIL-JigsawPuzzles.

eess.IV↗

arXiv2025

Derailer-Rerailer: Adaptive Verification for Efficient and Reliable Language Model Reasoning

Large Language Models (LLMs) have shown impressive reasoning capabilities, yet existing prompting methods face a critical trade-off: simple approaches often struggle with complex tasks and reasoning stability, while more sophisticated methods require multiple inferences and substantial computational resources, limiting their practical deployment. To address this challenge, we propose Derailer-Rerailer, a novel framework that adaptively balances reasoning accuracy and computational efficiency. At its core, our framework employs a lightweight Derailer mechanism to assess reasoning stability and selectively triggers an advanced Rerailer verification process only when necessary, thereby optimizing computational resource usage. Extensive evaluation across both open and closed-source models on more than 20 categories of mathematical, symbolic, and commonsense reasoning tasks demonstrates our framework's effectiveness: Derailer-Rerailer achieves significant accuracy improvements (8-11\% across various reasoning tasks) while maintaining 2-3 times better efficiency than existing verification methods, with particularly strong performance in mathematical and symbolic reasoning, offering a practical solution for enhancing LLM reasoning reliability while significantly reducing computational overhead.

cs.CL↗

arXiv2025

Cosmos World Foundation Model Platform for Physical AI

Physical AI needs to be trained digitally first. It needs a digital twin of itself, the policy model, and a digital twin of the world, the world model. In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups. We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications. Our platform covers a video curation pipeline, pre-trained world foundation models, examples of post-training of pre-trained world foundation models, and video tokenizers. To help Physical AI builders solve the most critical problems of our society, we make Cosmos open-source and our models open-weight with permissive licenses available via https://github.com/nvidia-cosmos/cosmos-predict1.

cs.CV↗

arXiv2025

High-Level Surface Code Decoding via Parallel FFNNs on CIM Platforms

Due to the high sensitivity of qubits to environmental noise, which leads to decoherence and information loss, active quantum error correction(QEC) is essential. Surface codes represent one of the most promising fault-tolerant QEC schemes, but they require decoders that are accurate, fast, and scalable to large-scale quantum platforms. In all types of decoders, fully neural network-based high-level decoders offer decoding thresholds that surpass baseline decoder-Minimum Weight Perfect Matching (MWPM), and exhibit strong scalability, making them one of the ideal solutions for addressing surface code challenges. However, current fully neural network-based high-level decoders can only operate serially and do not meet the current latency requirements (below 440 ns). To address these challenges, we first propose a parallel fully feedforward neural network (FFNN) high-level surface code decoder, and comprehensively measure its decoding performance on a computing-in-memory (CIM) hardware simulation platform. With the currently available hardware specifications, our work achieves a decoding threshold of 14.22%, surpassing the MWPM baseline of 10.3%, and achieves high pseudo-thresholds of 10.4%, 11.3%, 12%, and 11.6% with decoding latencies of 197.03 ns, 234.87 ns, 243.73 ns, and 251.65 ns for distances of 3, 5, 7 and 9, respectively. The impact of hardware parameters and non-idealities on these results is discussed, and the hardware simulation results are extrapolated to a 4K quantum cryogenic environment.

cs.AR↗

arXiv2025

Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop

Embodied Artificial Intelligence (Embodied AI) is an emerging frontier in robotics, driven by the need for autonomous systems that can perceive, reason, and act in complex physical environments. While single-arm systems have shown strong task performance, collaborative dual-arm systems are essential for handling more intricate tasks involving rigid, deformable, and tactile-sensitive objects. To advance this goal, we launched the RoboTwin Dual-Arm Collaboration Challenge at the 2nd MEIS Workshop, CVPR 2025. Built on the RoboTwin Simulation platform (1.0 and 2.0) and the AgileX COBOT-Magic Robot platform, the competition consisted of three stages: Simulation Round 1, Simulation Round 2, and a final Real-World Round. Participants totally tackled 17 dual-arm manipulation tasks, covering rigid, deformable, and tactile-based scenarios. The challenge attracted 64 global teams and over 400 participants, producing top-performing solutions like SEM and AnchorDP3 and generating valuable insights into generalizable bimanual policy learning. This report outlines the competition setup, task design, evaluation methodology, key findings and future direction, aiming to support future research on robust and generalizable bimanual manipulation policies. The Challenge Webpage is available at https://robotwin-benchmark.github.io/cvpr-2025-challenge/.

cs.RO↗

arXiv2025

The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steering

Large Vision-Language Models (LVLMs) can reason effectively over both textual and visual inputs, but they tend to hallucinate syntactically coherent yet visually ungrounded contents. In this paper, we investigate the internal dynamics of hallucination by examining the tokens logits ranking throughout the generation process, revealing three key patterns in how LVLMs process information: (1) gradual visual information loss - visually grounded tokens gradually become less favored throughout generation, and (2) early excitation - semantically meaningful tokens achieve peak activation in the layers earlier than the final layer. (3) hidden genuine information - visually grounded tokens though not being eventually decoded still retain relatively high rankings at inference. Based on these insights, we propose VISTA (Visual Information Steering with Token-logit Augmentation), a training-free inference-time intervention framework that reduces hallucination while promoting genuine information. VISTA works by combining two complementary approaches: reinforcing visual information in activation space and leveraging early layer activations to promote semantically meaningful decoding. Compared to existing methods, VISTA requires no external supervision and is applicable to various decoding strategies. Extensive experiments show that VISTA on average reduces hallucination by about 40% on evaluated open-ended generation task, and it consistently outperforms existing methods on four benchmarks across four architectures under three decoding strategies. Code is available at https://github.com/LzVv123456/VISTA.

cs.CV↗