arXiv subjects

Wenjie Wu

Publications and source records attributed to Wenjie Wu.

At least 19 recordsLinked to original sources

arXiv2026

An eightfold equivalence-preserving speedup of the JUNO OMILREC vertex and energy reconstruction

The Jiangmen Underground Neutrino Observatory (JUNO) reconstructs each event's vertex and energy with OMILREC, a maximum-likelihood fit that scans all $17{,}612$ large photomultiplier tubes (LPMTs) in every Minuit function evaluation, about $470$ times per event. This inner loop dominates the reconstruction CPU cost. Profiling shows that the production algorithm is latency-bound, sustaining only $9.9%$ of scalar floating-point peak because of virtual-function dispatch, ROOT-histogram pointer chasing, and repeated computation. We apply staged \emph{equivalence-preserving} optimizations: flattened data layouts, vectorizable geometry, hoisting of Minuit-invariant work, per-event precomputation, fit-phase loop splitting and indexing, and reduced-precision fast paths. Each stage is checked against a frozen reference from the unmodified code. The optimized implementation achieves single-thread speedups of $8.06\times$ ($1524.8 \rightarrow 189.2$~ms/event) on an Intel Xeon Platinum~8358P and $5.22\times$ ($705.1 \rightarrow 134.9$~ms/event) on an AMD~EPYC~9654, increasing to $8.6\times$ ($177.7$~ms/event) after further optimization. The likelihood remains bit-identical through the first seven releases and later agrees within a relative drift of $1.3\times10^{-14}$, below the $10^{-13}$ contract. For typical events, reconstructed vertex and energy agree with the baseline within $4$~mm and $7$~keV; a few boundary cases reach different valid minima owing to an improved minimizer seed. An eight-metric physics-acceptance test also passes on about $861{,}000$ $^{68}$Ge calibration events. Developed with assistance from an AI coding agent operating under these verification gates, this workflow offers a transferable template for accelerating likelihood-based reconstruction in large neutrino and collider detectors without changing physics output.

physics.ins-det↗

arXiv2026

A Bioinspired Underwater Robot with a Latch-Mediated Soft Bistable Mechanism

Underwater robotics has advanced significantly over recent decades. however, the development of miniaturized underwater robots remains limited by low energy densities of traditional power sources. Nature offers compelling solutions-organisms like mantis shrimps and fleas utilize latch-mediated spring actuation (LaMSA) systems that achieve rapid movements through a decoupled energy storage and release mechanism. Despite extensive studies of LaMSA, replicating such rapid, asymmetric actuation within simple, compact structures remains challenging. In this work, we introduce a bioinspired, soft bistable actuator with an integrated latch mechanism that enables asymmetric energy input and release using a single motor. Coupled with fin structures, this design facilitates efficient underwater propulsion and maneuverability. Experimental results demonstrate stable periodic flapping, precise steering, and a maximum thrust of 0.528 N, impulse of 0.147 Ns, and vertical displacement of 30 mm. By modulating fin angles, the robot achieves versatile motions, including vertical ascent, diagonal forward movement, and lateral translation. This study presents a novel, energy-efficient approach for controlling motion in compact underwater robots, paving the way for advanced biomimetic designs with potential applications in exploration, environmental monitoring, and inspection.

cs.RO↗

arXiv2026

From Flat Logs to Causal Graphs: Hierarchical Failure Attribution for LLM-based Multi-Agent Systems

LLM-powered Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in complex domains but suffer from inherent fragility and opaque failure mechanisms. Existing failure attribution methods, whether relying on direct prompting, costly replays, or supervised fine-tuning, typically treat execution logs as flat sequences. This linear perspective fails to disentangle the intricate causal links inherent to MAS, leading to weak observability and ambiguous responsibility boundaries. To address these challenges, we propose CHIEF, a novel framework that transforms chaotic trajectories into a structured hierarchical causal graph. It then employs hierarchical oracle-guided backtracking to efficiently prune the search space via sybthesized virtual oracles. Finally, it implements counterfactual attribution via a progressive causal screening strategy to rigorously distinguish true root causes from propagated symptoms. Experiments on Who&When benchmark show that CHIEF outperforms eight strong and state-of-the-art baselines on both agent- and step-level accuracy. Ablation studies further confirm the critical role of each proposed module.

cs.AI↗

arXiv2026

S-GRec: Personalized Semantic-Aware Generative Recommendation with Asymmetric Advantage

Generative recommendation models sequence generation to produce items end-to-end, but training from behavioral logs often provides weak supervision on underlying user intent. Although Large Language Models (LLMs) offer rich semantic priors that could supply such supervision, direct adoption in industrial recommendation is hindered by two obstacles: semantic signals can conflict with platform business objectives, and LLM inference is prohibitively expensive at scale. This paper presents S-GRec, a semantic-aware framework that decouples an online lightweight generator from an offline LLM-based semantic judge for train-time supervision. S-GRec introduces a two-stage Personalized Semantic Judge (PSJ) that produces interpretable aspect evidence and learns user-conditional aggregation from pairwise feedback, yielding stable semantic rewards. To prevent semantic supervision from deviating from business goals, Asymmetric Advantage Policy Optimization (A2PO) anchors optimization on business rewards (e.g., eCPM) and injects semantic advantages only when they are consistent. Extensive experiments on public benchmarks and a large-scale production system validate both effectiveness and scalability, including statistically significant gains in CTR and a 1.19\% lift in GMV in online A/B tests, without requiring real-time LLM inference.

cs.IR↗

arXiv2026

Statistical study for binary star evolution in dense embedded clusters

Context: The dynamical evolution of binary populations in embedded star clusters shapes the statistical properties of binaries observed in the Galactic field. Accurately modelling this process requires resolving both early cluster dynamics and binary interactions. Aims: We aim to characterize the early dynamical evolution of primordial binaries in embedded clusters and identify the key parameters that govern binary survival and disruption. Methods: We perform a set of direct $N$-body simulations starting from 100\% primordial binaries in a time-varying gas potential of a gas-embedded cluster. To describe the evolution of binary orbital properties, we define empirical dynamical operators for period, binding energy, and mass ratio, and calibrate them across the simulated ensemble. Results:The binding energy and orbital period evolve in a consistent, sigmoidal fashion. Their dynamical operators reveal that hard binaries heat the cluster and suppress wide binary formation, while a small residual population of soft binaries survives. The evolution of the mass-ratio distribution is less directly linked to dynamical processing and more shaped by internal processes such as stellar physics process in the pre-main-sequence phase. High-$q$ systems tend to be enhanced, while low-$q$ systems are prone to disruption. Conclusions: The binary evolution in clusters is primarily governed by binding energy and orbital period. Our model improves over previous parameterizations of the dynamical operator by allowing for the existence of wide binaries and incorporating the embedded cluster phase. For individual clusters, direct $N$-body modelling remains the only reliable approach. On Galactic scales, population synthesis methods based on the stellar dynamical operator approach developed here remain essential.

astro-ph.GA↗

arXiv2026

AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning

Robust anatomical segmentation of chest X-rays (CXRs) remains challenging due to the scarcity of comprehensive annotations and the substantial variability of real-world acquisition conditions. We propose AnyCXR, a unified framework that enables generalizable multi-organ segmentation across arbitrary CXR projection angles using only synthetic supervision. The method combines a Multi-stage Domain Randomization (MSDR) engine, which generates over 100,000 anatomically faithful and highly diverse synthetic radiographs from 3D CT volumes, with a Conditional Joint Annotation Regularization (CAR) learning strategy that leverages partial and imperfect labels by enforcing anatomical consistency in a latent space. Trained entirely on synthetic data, AnyCXR achieves strong zero-shot generalization on multiple real-world datasets, providing accurate delineation of 54 anatomical structures in PA, lateral, and oblique views. The resulting segmentation maps support downstream clinical tasks, including automated cardiothoracic ratio estimation, spine curvature assessment, and disease classification, where the incorporation of anatomical priors improves diagnostic performance. These results demonstrate that AnyCXR establishes a scalable and reliable foundation for anatomy-aware CXR analysis and offers a practical pathway toward reducing annotation burdens while improving robustness across diverse imaging conditions.

cs.CV↗

arXiv2025

Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects

Quantum computing is a promising paradigm that may overcome the current computational power bottlenecks. The increasing maturity of quantum processors provides more possibilities for the development and implementation of quantum algorithms. As the crucial stages for quantum algorithm implementation, the logic circuit design and quantum compiling have also received significant attention, which covers key technologies, e.g., quantum logic circuit synthesis (also widely known as quantum architecture search) and optimization, as well as qubit mapping and routing. Recent studies suggest that the scale and precision of related algorithms are steadily increasing, especially with the integration of artificial intelligence methods. In this survey, we systematically review and summarize a vast body of literature, exploring the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware, combining the steps of logic circuit design and compilation optimization. Leveraging the exceptional cognitive and learning capabilities of AI algorithms, it becomes more possible to reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.

quant-ph↗

arXiv2025

Lifetime study of the ColdADC for the Deep Underground Neutrino Experiment

ColdADC is a custom ASIC digitizer implemented in 65 nm CMOS technology using specialized techniques for long-term reliability in cryogenic environments. ColdADC was developed for use in the DUNE Far Detector complex, which will consist of four liquid argon time projection chambers. Each contains 17 kilotons liquid argon as the target material in order to measure neutrino oscillations. Approximately 40,000 ColdADC ASICs will be installed for DUNE in the first two large detectors and will be operated at cryogenic temperatures during the experiment without replacement. The lifetime of the ColdADC is a critical parameter affecting the data quality and physics sensitivity of the experiment. A measurement of the lifetime of the ColdADC was carried out, and the results shown in this paper assure orders of magnitude longer lifetime of the ColdADC than the planned operation time of the detectors.

physics.ins-det↗

arXiv2025

Tidal Tails of Nearby Open Clusters. II. A Review of Simulated Properties and the Reliability of Observational Catalogues

Context: Recent studies using Gaia data have reported tidal tail detections for tens to hundreds of open clusters. However, a comprehensive assessment of the reliability & completeness of these detections is lacking. Aims: This work aims to summarise the expected properties of tidal tails based on N-body simulations, review the reliability of tidal tail detections in the literature, and grade them according to a set of diagnostic tests. Methods: We used a grid of 68-20000 Msun simulated clusters & analysed the formation & evolution of the tails. We compiled 122 catalogues (58 unique clusters) from literature, within 500 pc of the Sun. We employed tests based on photometric, morphological & dynamical signatures, & comparisons with simulations, to grade their tails. Results: Based on the simulations, we analysed the complex morphology of the tails & their properties (e.g., size, span, stellar types, number density, & mass function) at various cluster masses & ages. During the first 200 Myr of evolution, the tails typically form a characteristic S shape, with an amplitude that scales with cluster mass. The tail span increases at a rate of 4 times the initial velocity dispersion, & the near-tail is predominantly populated by recent escapees. Conclusions: In the 122 published tail catalogues, we found that 15 gold & 55 silver catalogues passed the majority of the tests. The remaining 51 were graded as bronze; care should be taken before using these for further analysis. The age, metallicity, binary fraction, & mass function of stars in the tails were generally consistent with those of their parent clusters. The gold/silver catalogues (69 catalogues of 40 clusters) represent reliable samples for detailed analyses of tails. Future data will be essential for further validation & for leveraging tidal tails as tracers of cluster dissolution & the Galactic potential.

astro-ph.GA↗

arXiv2025

BSL: A Unified and Generalizable Multitask Learning Platform for Virtual Drug Discovery from Design to Synthesis

Drug discovery is of great social significance in safeguarding human health, prolonging life, and addressing the challenges of major diseases. In recent years, artificial intelligence has demonstrated remarkable advantages in key tasks across bioinformatics and pharmacology, owing to its efficient data processing and data representation capabilities. However, most existing computational platforms cover only a subset of core tasks, leading to fragmented workflows and low efficiency. In addition, they often lack algorithmic innovation and show poor generalization to out-of-distribution (OOD) data, which greatly hinders the progress of drug discovery. To address these limitations, we propose Baishenglai (BSL), a deep learning-enhanced, open-access platform designed for virtual drug discovery. BSL integrates seven core tasks within a unified and modular framework, incorporating advanced technologies such as generative models and graph neural networks. In addition to achieving state-of-the-art (SOTA) performance on multiple benchmark datasets, the platform emphasizes evaluation mechanisms that focus on generalization to OOD molecular structures. Comparative experiments with existing platforms and baseline methods demonstrate that BSL provides a comprehensive, scalable, and effective solution for virtual drug discovery, offering both algorithmic innovation and high-precision prediction for real-world pharmaceutical research. In addition, BSL demonstrated its practical utility by discovering novel modulators of the GluN1/GluN3A NMDA receptor, successfully identifying three compounds with clear bioactivity in in-vitro electrophysiological assays. These results highlight BSL as a promising and comprehensive platform for accelerating biomedical research and drug discovery. The platform is accessible at https://www.baishenglai.net.

cs.LG↗

arXiv2025

Science and Project Planning for the Forward Physics Facility in Preparation for the 2024-2026 European Particle Physics Strategy Update

The recent direct detection of neutrinos at the LHC has opened a new window on high-energy particle physics and highlighted the potential of forward physics for groundbreaking discoveries. In the last year, the physics case for forward physics has continued to grow, and there has been extensive work on defining the Forward Physics Facility and its experiments to realize this physics potential in a timely and cost-effective manner. Following a 2-page Executive Summary, we present the status of the FPF, beginning with the FPF's unique potential to shed light on dark matter, new particles, neutrino physics, QCD, and astroparticle physics. We summarize the current designs for the Facility and its experiments, FASER2, FASER$ν$2, FORMOSA, and FLArE, and conclude by discussing international partnerships and organization, and the FPF's schedule, budget, and technical coordination.

hep-ex↗

arXiv2025

On Reducing the Execution Latency of Superconducting Quantum Processors via Quantum Job Scheduling

Quantum computing has gained considerable attention, especially after the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era. Quantum processors and cloud services have been made world-wide increasingly available. Unfortunately, jobs on existing quantum processors are often executed in series, and the workload could be heavy to the processor. Typically, one has to wait for hours or even longer to obtain the result of a single quantum job on public quantum cloud due to long queue time. In fact, as the scale grows, the qubit utilization rate of the serial execution mode will further diminish, causing the waste of quantum resources. In this paper, to our best knowledge for the first time, the Quantum Job Scheduling Problem (QJSP) is formulated and introduced, and we accordingly aim to improve the utility efficiency of quantum resources. Specifically, a noise-aware quantum job scheduler (NAQJS) concerning the circuit width, number of measurement shots, and submission time of quantum jobs is proposed to reduce the execution latency. We conduct extensive experiments on a simulated Qiskit noise model, as well as on the Xiaohong (from QuantumCTek) superconducting quantum processor. Numerical results show the effectiveness in both the QPU time and turnaround time.

cs.AR↗

arXiv2025

Particle Hit Clustering and Identification Using Point Set Transformers in Liquid Argon Time Projection Chambers

Liquid argon time projection chambers are often used in neutrino physics and dark-matter searches because of their high spatial resolution. The images generated by these detectors are extremely sparse, as the energy values detected by most of the detector are equal to 0, meaning that despite their high resolution, most of the detector is unused in a particular interaction. Instead of representing all of the empty detections, the interaction is usually stored as a sparse matrix, a list of detection locations paired with their energy values. Traditional machine learning methods that have been applied to particle reconstruction such as convolutional neural networks (CNNs), however, cannot operate over data stored in this way and therefore must have the matrix fully instantiated as a dense matrix. Operating on dense matrices requires a lot of memory and computation time, in contrast to directly operating on the sparse matrix. We propose a machine learning model using a point set neural network that operates over a sparse matrix, greatly improving both processing speed and accuracy over methods that instantiate the dense matrix, as well as over other methods that operate over sparse matrices. Compared to competing state-of-the-art methods, our method improves classification performance by 14%, segmentation performance by more than 22%, while taking 80% less time and using 66% less memory. Compared to state-of-the-art CNN methods, our method improves classification performance by more than 86%, segmentation performance by more than 71%, while reducing runtime by 91% and reducing memory usage by 61%.

hep-ex↗

arXiv2025

The Forward Physics Facility at the Large Hadron Collider

The Forward Physics Facility (FPF) is a proposal developed to exploit the unique scientific potential made possible by the intense hadron beams produced in the far-forward direction at the high luminosity LHC (HL-LHC). Housed in a well-shielded cavern 627 m from the LHC interactions, the facility will enable a broad and deep scientific programme which will greatly extend the physics capability of the HL-LHC. Instrumented with a suite of four complementary detectors -- FLArE, FASER$ν$2, FASER2 and FORMOSA -- the FPF has unique potential to shed light on neutrino physics, QCD, astroparticle physics, and to search for dark matter and other new particles. This contribution describes some of the key scientific drivers for the facility, the engineering and technical studies that have been made in preparation for it, the design of its four complementary experiments, and the status of the project's partnerships and planning.

hep-ex↗

arXiv2025

Benchmarking Reasoning Robustness in Large Language Models

Despite the recent success of large language models (LLMs) in reasoning such as DeepSeek, we for the first time identify a key dilemma in reasoning robustness and generalization: significant performance degradation on novel or incomplete data, suggesting a reliance on memorized patterns rather than systematic reasoning. Our closer examination reveals four key unique limitations underlying this issue:(1) Positional bias--models favor earlier queries in multi-query inputs but answering the wrong one in the latter (e.g., GPT-4o's accuracy drops from 75.8 percent to 72.8 percent); (2) Instruction sensitivity--performance declines by 5.0 to 7.5 percent in the Qwen2.5 Series and by 5.0 percent in DeepSeek-V3 with auxiliary guidance; (3) Numerical fragility--value substitution sharply reduces accuracy (e.g., GPT-4o drops from 97.5 percent to 82.5 percent, GPT-o1-mini drops from 97.5 percent to 92.5 percent); and (4) Memory dependence--models resort to guesswork when missing critical data. These findings further highlight the reliance on heuristic recall over rigorous logical inference, demonstrating challenges in reasoning robustness. To comprehensively investigate these robustness challenges, this paper introduces a novel benchmark, termed as Math-RoB, that exploits hallucinations triggered by missing information to expose reasoning gaps. This is achieved by an instruction-based approach to generate diverse datasets that closely resemble training distributions, facilitating a holistic robustness assessment and advancing the development of more robust reasoning frameworks. Bad character(s) in field Abstract.

cs.AI↗

arXiv2025

Graph-Augmented Reasoning: Evolving Step-by-Step Knowledge Graph Retrieval for LLM Reasoning

Recent large language model (LLM) reasoning, despite its success, suffers from limited domain knowledge, susceptibility to hallucinations, and constrained reasoning depth, particularly in small-scale models deployed in resource-constrained environments. This paper presents the first investigation into integrating step-wise knowledge graph retrieval with step-wise reasoning to address these challenges, introducing a novel paradigm termed as graph-augmented reasoning. Our goal is to enable frozen, small-scale LLMs to retrieve and process relevant mathematical knowledge in a step-wise manner, enhancing their problem-solving abilities without additional training. To this end, we propose KG-RAR, a framework centered on process-oriented knowledge graph construction, a hierarchical retrieval strategy, and a universal post-retrieval processing and reward model (PRP-RM) that refines retrieved information and evaluates each reasoning step. Experiments on the Math500 and GSM8K benchmarks across six models demonstrate that KG-RAR yields encouraging results, achieving a 20.73\% relative improvement with Llama-3B on Math500.

cs.AI↗

arXiv2025

Random Forest-of-Thoughts: Uncertainty-aware Reasoning for Computational Social Science

Social surveys in computational social science are well-designed by elaborate domain theories that can effectively reflect the interviewee's deep thoughts without concealing their true feelings. The candidate questionnaire options highly depend on the interviewee's previous answer, which results in the complexity of social survey analysis, the time, and the expertise required. The ability of large language models (LLMs) to perform complex reasoning is well-enhanced by prompting learning such as Chain-of-thought (CoT) but still confined to left-to-right decision-making processes or limited paths during inference. This means they can fall short in problems that require exploration and uncertainty searching. In response, a novel large language model prompting method, called Random Forest of Thoughts (RFoT), is proposed for generating uncertainty reasoning to fit the area of computational social science. The RFoT allows LLMs to perform deliberate decision-making by generating diverse thought space and randomly selecting the sub-thoughts to build the forest of thoughts. It can extend the exploration and prediction of overall performance, benefiting from the extensive research space of response. The method is applied to optimize computational social science analysis on two datasets covering a spectrum of social survey analysis problems. Our experiments show that RFoT significantly enhances language models' abilities on two novel social survey analysis problems requiring non-trivial reasoning.

cs.CL↗

arXiv2025

GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training

Despite their proficiency in general tasks, Multi-modal Large Language Models (MLLMs) struggle with automatic Geometry Problem Solving (GPS), which demands understanding diagrams, interpreting symbols, and performing complex reasoning. This limitation arises from their pre-training on natural images and texts, along with the lack of automated verification in the problem-solving process. Besides, current geometric specialists are limited by their task-specific designs, making them less effective for broader geometric problems. To this end, we present GeoX, a multi-modal large model focusing on geometric understanding and reasoning tasks. Given the significant differences between geometric diagram-symbol and natural image-text, we introduce unimodal pre-training to develop a diagram encoder and symbol decoder, enhancing the understanding of geometric images and corpora. Furthermore, we introduce geometry-language alignment, an effective pre-training paradigm that bridges the modality gap between unimodal geometric experts. We propose a Generator-And-Sampler Transformer (GS-Former) to generate discriminative queries and eliminate uninformative representations from unevenly distributed geometric signals. Finally, GeoX benefits from visual instruction tuning, empowering it to take geometric images and questions as input and generate verifiable solutions. Experiments show that GeoX outperforms both generalists and geometric specialists on publicly recognized benchmarks, such as GeoQA, UniGeo, Geometry3K, and PGPS9k.

cs.CV↗