arXiv subjects

Xiang Li

Publications and source records attributed to Xiang Li.

At least 379 records · Page 21Linked to original sources

arXiv2025

Kinematics-Aware Diffusion Policy with Consistent 3D Observation and Action Space for Whole-Arm Robotic Manipulation

Whole-body control of robotic manipulators with awareness of full-arm kinematics is crucial for many manipulation scenarios involving body collision avoidance or body-object interactions, which makes it insufficient to consider only the end-effector poses in policy learning. The typical approach for whole-arm manipulation is to learn actions in the robot's joint space. However, the unalignment between the joint space and actual task space (i.e., 3D space) increases the complexity of policy learning, as generalization in task space requires the policy to intrinsically understand the non-linear arm kinematics, which is difficult to learn from limited demonstrations. To address this issue, this letter proposes a kinematics-aware imitation learning framework with consistent task, observation, and action spaces, all represented in the same 3D space. Specifically, we represent both robot states and actions using a set of 3D points on the arm body, naturally aligned with the 3D point cloud observations. This spatially consistent representation improves the policy's sample efficiency and spatial generalizability while enabling full-body control. Built upon the diffusion policy, we further incorporate kinematics priors into the diffusion processes to guarantee the kinematic feasibility of output actions. The joint angle commands are finally calculated through an optimization-based whole-body inverse kinematics solver for execution. Simulation and real-world experimental results demonstrate higher success rates and stronger spatial generalizability of our approach compared to existing methods in body-aware manipulation policy learning.

cs.RO↗

arXiv2025

UniStateDLO: Unified Generative State Estimation and Tracking of Deformable Linear Objects Under Occlusion for Constrained Manipulation

Perception of deformable linear objects (DLOs), such as cables, ropes, and wires, is the cornerstone for successful downstream manipulation. Although vision-based methods have been extensively explored, they remain highly vulnerable to occlusions that commonly arise in constrained manipulation environments due to surrounding obstacles, large and varying deformations, and limited viewpoints. Moreover, the high dimensionality of the state space, the lack of distinctive visual features, and the presence of sensor noises further compound the challenges of reliable DLO perception. To address these open issues, this paper presents UniStateDLO, the first complete DLO perception pipeline with deep-learning methods that achieves robust performance under severe occlusion, covering both single-frame state estimation and cross-frame state tracking from partial point clouds. Both tasks are formulated as conditional generative problems, leveraging the strong capability of diffusion models to capture the complex mapping between highly partial observations and high-dimensional DLO states. UniStateDLO effectively handles a wide range of occlusion patterns, including initial occlusion, self-occlusion, and occlusion caused by multiple objects. In addition, it exhibits strong data efficiency as the entire network is trained solely on a large-scale synthetic dataset, enabling zero-shot sim-to-real generalization without any real-world training data. Comprehensive simulation and real-world experiments demonstrate that UniStateDLO outperforms all state-of-the-art baselines in both estimation and tracking, producing globally smooth yet locally precise DLO state predictions in real time, even under substantial occlusions. Its integration as the front-end module in a closed-loop DLO manipulation system further demonstrates its ability to support stable feedback control in complex, constrained 3-D environments.

cs.RO↗

arXiv2025

ChartAgent: A Chart Understanding Framework with Tool Integrated Reasoning

With their high information density and intuitive readability, charts have become the de facto medium for data analysis and communication across disciplines. Recent multimodal large language models (MLLMs) have made notable progress in automated chart understanding, yet they remain heavily dependent on explicit textual annotations and the performance degrades markedly when key numerals are absent. To address this limitation, we introduce ChartAgent, a chart understanding framework grounded in Tool-Integrated Reasoning (TIR). Inspired by human cognition, ChartAgent decomposes complex chart analysis into a sequence of observable, replayable steps. Supporting this architecture is an extensible, modular tool library comprising more than a dozen core tools, such as keyelement detection, instance segmentation, and optical character recognition (OCR), which the agent dynamically orchestrates to achieve systematic visual parsing across diverse chart types. Leveraging TIRs transparency and verifiability, ChartAgent moves beyond the black box paradigm by standardizing and consolidating intermediate outputs into a structured Evidence Package, providing traceable and reproducible support for final conclusions. Experiments show that ChartAgent substantially improves robustness under sparse annotation settings, offering a practical path toward trustworthy and extensible systems for chart understanding.

cs.CV↗

arXiv2025

Sequence of Expert: Boosting Imitation Planners for Autonomous Driving through Temporal Alternation

Imitation learning (IL) has emerged as a central paradigm in autonomous driving. While IL excels in matching expert behavior in open-loop settings by minimizing per-step prediction errors, its performance degrades unexpectedly in closed-loop due to the gradual accumulation of small, often imperceptible errors over time.Over successive planning cycles, these errors compound, potentially resulting in severe failures.Current research efforts predominantly rely on increasingly sophisticated network architectures or high-fidelity training datasets to enhance the robustness of IL planners against error accumulation, focusing on the state-level robustness at a single time point. However, autonomous driving is inherently a continuous-time process, and leveraging the temporal scale to enhance robustness may provide a new perspective for addressing this issue.To this end, we propose a method termed Sequence of Experts (SoE), a temporal alternation policy that enhances closed-loop performance without increasing model size or data requirements. Our experiments on large-scale autonomous driving benchmarks nuPlan demonstrate that SoE method consistently and significantly improves the performance of all the evaluated models, and achieves state-of-the-art performance.This module may provide a key and widely applicable support for improving the training efficiency of autonomous driving models.

cs.RO↗

arXiv2025

MAC: A Multi-Agent Framework for Interactive User Clarification in Multi-turn Conversations

Conversational agents often encounter ambiguous user requests, requiring an effective clarification to successfully complete tasks. While recent advancements in real-world applications favor multi-agent architectures to manage complex conversational scenarios efficiently, ambiguity resolution remains a critical and underexplored challenge--particularly due to the difficulty of determining which agent should initiate a clarification and how agents should coordinate their actions when faced with uncertain or incomplete user input. The fundamental questions of when to interrupt a user and how to formulate the optimal clarification query within the most optimal multi-agent settings remain open. In this paper, we propose MAC (Multi-Agent Clarification), an interactive multi-agent framework specifically optimized to resolve user ambiguities by strategically managing clarification dialogues. We first introduce a novel taxonomy categorizing user ambiguities to systematically guide clarification strategies. Then, we present MAC that autonomously coordinates multiple agents to interact synergistically with users. Empirical evaluations on MultiWOZ 2.4 demonstrate that enabling clarification at both levels increases task success rate 7.8\% (54.5 to 62.3) and reduces the average number of dialogue turns (6.53 to 4.86) by eliciting all required user information up front and minimizing repetition. Our findings highlight the importance of active user interaction and role-aware clarification for more reliable human-agent communication.

cs.AI↗

arXiv2025

SpeakRL: Synergizing Reasoning, Speaking, and Acting in Language Models with Reinforcement Learning

Effective human-agent collaboration is increasingly prevalent in real-world applications. Current trends in such collaborations are predominantly unidirectional, with users providing instructions or posing questions to agents, where agents respond directly without seeking necessary clarifications or confirmations. However, the evolving capabilities of these agents require more proactive engagement, where agents should dynamically participate in conversations to clarify user intents, resolve ambiguities, and adapt to changing circumstances. Existing prior work under-utilize the conversational capabilities of language models (LMs), thereby optimizing agents as better followers rather than effective speakers. In this work, we introduce SpeakRL, a reinforcement learning (RL) method that enhances agents' conversational capabilities by rewarding proactive interactions with users, such as asking right clarification questions when necessary. To support this, we curate SpeakER, a synthetic dataset that includes diverse scenarios from task-oriented dialogues, where tasks are resolved through interactive clarification questions. We present a systematic analysis of reward design for conversational proactivity and propose a principled reward formulation for teaching agents to balance asking with acting. Empirical evaluations demonstrate that our approach achieves a 20.14% absolute improvement in task completion over base models without increasing conversation turns even surpassing even much larger proprietary models, demonstrating the promise of clarification-centric user-agent interactions.

cs.AI↗

arXiv2025

AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge

While current AI-driven methods excel at deriving empirical models from individual experiments, a significant challenge remains in uncovering the common fundamental physics that underlie these models -- a task at which human physicists are adept. To bridge this gap, we introduce AI-Newton, a novel framework for concept-driven scientific discovery. Our system autonomously derives general physical laws directly from raw, multi-experiment data, operating without supervision or prior physical knowledge. Its core innovations are twofold: (1) proposing interpretable physical concepts to construct laws, and (2) progressively generalizing these laws to broader domains. Applied to a large, noisy dataset of mechanics experiments, AI-Newton successfully rediscovers foundational and universal laws, such as Newton's second law, the conservation of energy, and the universal gravitation. This work represents a significant advance toward autonomous, human-like scientific discovery.

cs.AI↗

arXiv2025

DISTA-Net: Dynamic Closely-Spaced Infrared Small Target Unmixing

Resolving closely-spaced small targets in dense clusters presents a significant challenge in infrared imaging, as the overlapping signals hinder precise determination of their quantity, sub-pixel positions, and radiation intensities. While deep learning has advanced the field of infrared small target detection, its application to closely-spaced infrared small targets has not yet been explored. This gap exists primarily due to the complexity of separating superimposed characteristics and the lack of an open-source infrastructure. In this work, we propose the Dynamic Iterative Shrinkage Thresholding Network (DISTA-Net), which reconceptualizes traditional sparse reconstruction within a dynamic framework. DISTA-Net adaptively generates convolution weights and thresholding parameters to tailor the reconstruction process in real time. To the best of our knowledge, DISTA-Net is the first deep learning model designed specifically for the unmixing of closely-spaced infrared small targets, achieving superior sub-pixel detection accuracy. Moreover, we have established the first open-source ecosystem to foster further research in this field. This ecosystem comprises three key components: (1) CSIST-100K, a publicly available benchmark dataset; (2) CSO-mAP, a custom evaluation metric for sub-pixel detection; and (3) GrokCSO, an open-source toolkit featuring DISTA-Net and other models. Our code and dataset are available at https://github.com/GrokCV/GrokCSO.

cs.CV↗

arXiv2025

Halogen Chains with One-Dimensional Semi-Metallic Electronic Structure and Peierls Physics in Polymorphs of Na4X5 (X = I, Br, Cl) Compounds

Since the pioneering works of Peierls, one-dimensional materials have attracted great attention. Still, the synthesis of truly monoatomic chains remains elusive. In this study, we explore a novel path of experimental synthesis of monoatomic one-dimensional chains by their chemical stabilization in ionic compounds. We demonstrate that in synthesized at high pressure sodium halides Na4X5 (X = I, Br, Cl) with hP18 Ga4Ti5-type structures, transfer of valence electrons from cations to anions leads to the formation of halogen chains connected with other atoms only by ionic interaction and having one-dimensional electronic structure. The Peierls physics in the systems is confirmed by theoretical calculations, newly synthesized incommensurately modulated i-hP18-Na4X5 (X = I, Br, Cl) compounds, as well as by the discovered hP36 phases of Na4Cl5 and Na4Br5.

cond-mat.mtrl-sci↗

arXiv2025

Disorder-mediated linear and nonlinear magnetotransport in the charge-density-wave material ${\rm Ta_2NiSe_7 }$

We report disorder-mediated first-order linear and higher-order nonlinear (magneto-)transport of Ta$_2$NiSe$_7$ (TNS) in the charge-density-wave (CDW) regime. CDW transition temperature ($T_{CDW}$) and carrier density are proportional and inversely proportional to residual resistance ratio of samples, respectively. Such relation helps to understand the unique CDW order therein. High-$T_{CDW}$ TNS exhibits negative first-harmonic magnetoresistance (MR$^{1ω}$) under a magnetic field ($B$) parallel to the direction of alternating current ($I^ω$), which may arise from the anomalous velocity induced by the Berry curvature of three-dimensional topological bands near the Fermi level. As $T_{CDW}$ drops, a positive-to-negative MR$^{1ω}$ transition is observed with decreasing perpendicular $B$, which is likely due to the contribution of Zeeman effect on current pathways in the disordered system. Moreover, interestingly, the second-harmonic nonlinear signals are suppressed, while the third-harmonic signals are significant and sensitive to both $B$ and $T_{CDW}$. Such observations, together with scaling analysis, suggest the quantum geometry quadrupole at play and the modulation of disorder on third-order nonlinearity. Our results pave an avenue for tailoring distinct-order magnetoresistive phases in disordered topological materials.

cond-mat.mtrl-sci↗

arXiv2025

Enhancing Interpretability of AR-SSVEP-Based Motor Intention Recognition via CNN-BiLSTM and SHAP Analysis on EEG Data

Patients with motor dysfunction show low subjective engagement in rehabilitation training. Traditional SSVEP-based brain-computer interface (BCI) systems rely heavily on external visual stimulus equipment, limiting their practicality in real-world settings. This study proposes an augmented reality steady-state visually evoked potential (AR-SSVEP) system to address the lack of patient initiative and the high workload on therapists. Firstly, we design four HoloLens 2-based EEG classes and collect EEG data from seven healthy subjects for analysis. Secondly, we build upon the conventional CNN-BiLSTM architecture by integrating a multi-head attention mechanism (MACNN-BiLSTM). We extract ten temporal-spectral EEG features and feed them into a CNN to learn high-level representations. Then, we use BiLSTM to model sequential dependencies and apply a multi-head attention mechanism to highlight motor-intention-related patterns. Finally, the SHAP (SHapley Additive exPlanations) method is applied to visualize EEG feature contributions to the neural network's decision-making process, enhancing the model's interpretability. These findings enhance real-time motor intention recognition and support recovery in patients with motor impairments.

cs.LG↗

arXiv2025

A Survey of Operating System Kernel Fuzzing

The Operating System (OS) kernel is foundational in modern computing, especially with the proliferation of diverse computing devices. However, its development also comes with vulnerabilities that can lead to severe security breaches. Kernel fuzzing, a technique used to uncover these vulnerabilities, poses distinct challenges when compared to user-space fuzzing. These include the complexity of configuring the testing environment and addressing the statefulness inherent to both the kernel and the fuzzing process. Despite the significant interest from the community, a comprehensive understanding of kernel fuzzing remains lacking, hindering further progress in the field. In this paper, we present the first systematic study focused specifically on OS kernel fuzzing. We begin by outlining the unique challenges of kernel fuzzing, which distinguish it from those in user space. Following this, we summarize the progress of 107 academic studies from top-tier venues between 2017 and 2025. To structure this analysis, we introduce a stage-based fuzzing model and a novel fuzzing taxonomy that highlights nine core functionalities unique to kernel fuzzing. Each of these functionalities is examined in conjunction with the methodological approaches employed to address them. Finally, we identify remaining gaps in addressing challenges and outline promising directions to guide forthcoming research in kernel security.

cs.CR↗

arXiv2025

Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Training Framework

Computational pathology (CPath) digitizes pathology slides into whole slide images (WSIs), enabling analysis for critical healthcare tasks such as cancer diagnosis and prognosis. However, WSIs possess extremely long sequence lengths (up to 200K), significant length variations (from 200 to 200K), and limited supervision. These extreme variations in sequence length lead to high data heterogeneity and redundancy. Conventional methods often compromise on training efficiency and optimization to preserve such heterogeneity under limited supervision. To comprehensively address these challenges, we propose a pack-based MIL framework. It packs multiple sampled, variable-length feature sequences into fixed-length ones, enabling batched training while preserving data heterogeneity. Moreover, we introduce a residual branch that composes discarded features from multiple slides into a hyperslide which is trained with tailored labels. It offers multi-slide supervision while mitigating feature loss from sampling. Meanwhile, an attention-driven downsampler is introduced to compress features in both branches to reduce redundancy. By alleviating these challenges, our approach achieves an accuracy improvement of up to 8% while using only 12% of the training time in the PANDA(UNI). Extensive experiments demonstrate that focusing data challenges in CPath holds significant potential in the era of foundation models. The code is https://github.com/FangHeng/PackMIL

cs.CV↗

arXiv2025

Toward AI-Ready Medical Imaging Data

Medical imaging data plays a vital role in disease diagnosis, monitoring, and clinical research discovery. Biomedical data managers and clinical researchers must navigate a complex landscape of medical imaging infrastructure, input/output tools and data reliability workflow configurations taking months to operationalize. While standard formats exist for medical imaging data, standard operating procedures (SOPs) for data management are lacking. These data management SOPs are key for developing Findable, Accessible, Interoperable, and Reusable (FAIR) data, a prerequisite for AI-ready datasets. The National Institutes of Health (NIH) Bridge to Artificial Intelligence (Bridge2AI) Standards Working Group members and domain-expert stakeholders from the Bridge2AI Grand Challenges teams developed data management SOPs for the Digital Imaging and Communications in Medicine (DICOM) format. We describe novel SOPs applying to both static and cutting edge video imaging modalities. We emphasize steps required for centralized data aggregation, validation, and de-identification, including a review of new defacing methods for facial DICOM scans, anticipating adversarial AI/ML data re-identification methods. Data management vignettes based on Bridge2AI datasets include example parameters for efficient capture of a wide modality spectrum, including datasets from new ophthalmology retinal scans DICOM modalities.

q-bio.OT↗

arXiv2025

Unusual electronic ordering in the pseudogap phase of underdoped cuprate superconductors

The pseudogap phase of the underdoped cuprate superconductors harbours diverse manifestations of different ordered electronic-states, and then these ordered electronic-states coexist or compete with superconductivity. Here starting from the microscopic electron propagator, the nature of the ordered electronic-states in the pseudogap phase is investigated within the $T$-matrix approach. This $T$-matrix is derived in terms of the inverse of matrix for various kinds of a single impurity, and then is used to evaluate the local density of states (LDOS) by the involvement of all the quasiparticle excitations and scattering processes. It is shown that a number of the anomalous properties in the underdoped cuprate superconductors is directly correlated to the opening of the normal-state pseudogap: (i) the structure of the microscopic octet scattering model generated by the normal-state pseudogap is essentially the same both in the superconducting (SC)-state and pseudogap phase, which naturally leads to that the quasiparticle scattering interference octet phenomenology observed in the SC-state exists in the pseudogap phase; (ii) however, the spectral weight at around the antinodal region in the SC-state is gapped out completely by both the SC gap and normal-state pseudogap, while it in the pseudogap phase is suppressed partially by the normal-state pseudogap, this directly leads to that the non-dispersive checkerboard charge ordering with a finite wave vector ${\bf Q}$ appears in the pseudogap phase only. The theory therefore also shows that the electronic-states affected by the normal-state pseudogap exhibit the LDOS modulation spectrum organization.

cond-mat.supr-con↗

arXiv2025

AirSim360: A Panoramic Simulation Platform within Drone View

The field of 360-degree omnidirectional understanding has been receiving increasing attention for advancing spatial intelligence. However, the lack of large-scale and diverse data remains a major limitation. In this work, we propose AirSim360, a simulation platform for omnidirectional data from aerial viewpoints, enabling wide-ranging scene sampling with drones. Specifically, AirSim360 focuses on three key aspects: a render-aligned data and labeling paradigm for pixel-level geometric, semantic, and entity-level understanding; an interactive pedestrian-aware system for modeling human behavior; and an automated trajectory generation paradigm to support navigation tasks. Furthermore, we collect more than 60K panoramic samples and conduct extensive experiments across various tasks to demonstrate the effectiveness of our simulator. Unlike existing simulators, our work is the first to systematically model the 4D real world under an omnidirectional setting. The entire platform, including the toolkit, plugins, and collected datasets, will be made publicly available at https://insta360-research-team.github.io/AirSim360-website.

cs.CV↗

arXiv2025

A region-specific brain dysfunction underlies cognitive impairment in long COVID brain fog

Long COVID "brain fog" is a common and debilitating subjective syndrome often associated with persistent cognitive impairment after COVID-19 infection. Here we identify a specific regional brain dysfunction that mediates this cognitive impairment and provide evidence that targeted neuromodulation improves this deficit. In 120 patients with long COVID brain fog, we found an aberrant perceptual processing pattern. Patients with more severe brain fog committed significantly more false alarms (impulsive responses to non-signals) despite preserved overall accuracy. Both high-density (128-channel) EEG and structural MRI analyses provided converging evidence of a right inferior insula deficit, characterized by a blunted neural monitoring signal and cortical atrophy. We confirmed this deficit in a separate 796-participant UK Biobank longitudinal COVID re-imaging cohort, where COVID-19 survivors also showed selective impairment on a perceptual processing task and corresponding longitudinal atrophy of the right inferior insula compared with healthy controls. Finally, in a proof-of-principle randomized, sham-controlled trial (n = 40), a non-invasive, excitatory theta-burst ultrasound stimulation protocol targeting the right inferior insula rescued the perceptual deficit by reducing false alarms. These findings provide evidence of a causal role for right inferior insula dysfunction in long COVID-related perceptual impairment and show that modulation of this region can rescue the deficit, establishing it as a novel therapeutic target for long COVID cognitive impairment.

q-bio.NC↗

arXiv2025

Heralded Emission Detection in InAs/ZnSe Quantum Dot Solids Using Time-Correlated Photons

Harnessing quantum correlations between photons is an emerging frontier in optical spectroscopy, yet experimental demonstrations have largely remained limited to molecular systems at room temperature. Here, we investigate heralded emission detection (HED) under continuous-wave entangled-photon excitation of near-infrared (NIR)-emitting colloidal III-V quantum dot (QD) solids at low temperatures. We demonstrate the advantages of superconducting nanowire single-photon detectors (SNSPDs) for high time resolution ($\sim$72 ps) and large-area NIR avalanche photodiodes (APDs) for high emission count rates ($\sim$2000 cps). Second-order photon-correlation analysis reveals exciton lifetimes and fine-structure energy splittings. These results establish NIR colloidal QDs as a bright, tunable model system for quantum-light spectroscopy and highlight their compatibility with optical cavities as a further experimental control parameter.

physics.optics↗