arXiv subjects

Han Gao

Publications and source records attributed to Han Gao.

At least 37 records · Page 2Linked to original sources

arXiv2026

Spin splitting, Kondo correlation and singlet-doublet quantum phase transition in a superconductor-coupled InSb nanosheet quantum dot

We realize a superconductor-coupled quantum dot (QD) in an InSb nanosheet, a 2D platform promising for studies of topological superconductivity. The device consists of a superconductor-QD-superconductor junction, where a bottom bilayer gate defines the QD and allows tuning of its coupling to the superconducting leads. The QD exhibits large $g$-factors and strong spin-orbit coupling. Transport measurements reveal Coulomb diamond-shaped differential conductance features with even-odd alternating sizes and pronounced conductance lines associated with the superconducting gap, confirming a few-electron, superconductor-coupled regime. At an odd electron occupation, Kondo signatures emerge, including a zero-bias peak that splits with magnetic field and is logarithmically suppressed at elevated temperatures. We further observe a doublet-singlet quantum phase transition, manifested by a clear change of Andreev bound states from crossing to anticrossing as the coupling strength increases. These results underscore the rich physics of InSb nanosheet QDs and their promise for topological quantum devices.

cond-mat.supr-con↗

arXiv2026

vLLM-Omni: Fully Disaggregated Serving for Any-to-Any Multimodal Models

Any-to-any multimodal models that jointly handle text, images, video, and audio represent a significant advance in multimodal AI. However, their complex architectures (typically combining multiple autoregressive LLMs, diffusion transformers, and other specialized components) pose substantial challenges for efficient model serving. Existing serving systems are mainly tailored to a single paradigm, such as autoregressive LLMs for text generation or diffusion transformers for visual generation. They lack support for any-to-any pipelines that involve multiple interconnected model components. As a result, developers must manually handle cross-stage interactions, leading to huge performance degradation. We present vLLM-Omni, a fully disaggregated serving system for any-to-any models. vLLM-Omni features a novel stage abstraction that enables users to decompose complex any-to-any architectures into interconnected stages represented as a graph, and a disaggregated stage execution backend that optimizes resource utilization and throughput across stages. Each stage is independently served by an LLM or diffusion engine with per-stage request batching, flexible GPU allocation, and unified inter-stage connectors for data routing. Experimental results demonstrate that vLLM-Omni reduces job completion time (JCT) by up to 91.4% compared to baseline methods. The code is public available at https://github.com/vllm-project/vllm-omni.

cs.DC↗

arXiv2026

Supercurrent and multiple Andreev reflections in Ge hut nanowire Josephson Junctions

We report an experimental study of induced superconductivity in Ge hut nanowire Josephson junctions. The Ge hut nanowires are grown on prepatterned SiGe ridges via molecular beam epitaxy (MBE) and Josephson junction devices are fabricated by contacting the nanowires with Al electrodes. Low-temperature current-bias transport measurements of the Josephson junctions are performed and the measurements show that the devices exhibit gate-tunable supercurrent and excess current. The analysis of excess current indicates that the transparency of the Ge hut nanowire Josephson junctions is as high as 85%. Voltage-bias spectroscopy measurements of the devices show multiple Andreev reflections up to the fourth order. With magnetic field and temperature-dependent measurements of the multiple Andreev reflections, the critical field and the critical temperature of the induced superconductivity in the Josephson junctions are extracted to be ~0.12 T and ~1.4 K. The success in introducing superconductivity into Ge hut nanowires will stimulate their applications in building advanced quantum processors.

cond-mat.mes-hall↗

arXiv2026

CoINS: Counterfactual Interactive Navigation via Skill-Aware VLM

Recent Vision-Language Models (VLMs) have demonstrated significant potential in robotic planning. However, they typically function as semantic reasoners, lacking an intrinsic understanding of the specific robot's physical capabilities. This limitation is particularly critical in interactive navigation, where robots must actively modify cluttered environments to create traversable paths. Existing VLM-based navigators are predominantly confined to passive obstacle avoidance, failing to reason about when and how to interact with objects to clear blocked paths. To bridge this gap, we propose Counterfactual Interactive Navigation via Skill-aware VLM (CoINS), a hierarchical framework that integrates skill-aware reasoning and robust low-level execution. Specifically, we fine-tune a VLM, named InterNav-VLM, which incorporates skill affordance and concrete constraint parameters into the input context and grounds them into a metric-scale environmental representation. By internalizing the logic of counterfactual reasoning through fine-tuning on the proposed InterNav dataset, the model learns to implicitly evaluate the causal effects of object removal on navigation connectivity, thereby determining interaction necessity and target selection. To execute the generated high-level plans, we develop a comprehensive skill library through reinforcement learning, specifically introducing traversability-oriented strategies to manipulate diverse objects for path clearance. A systematic benchmark in Isaac Sim is proposed to evaluate both the reasoning and execution aspects of interactive navigation. Extensive simulations and real-world experiments demonstrate that CoINS significantly outperforms representative baselines, achieving a 17\% higher overall success rate and over 80\% improvement in complex long-horizon scenarios compared to the best-performing baseline

cs.RO↗

arXiv2025

VLA-RAIL: A Real-Time Asynchronous Inference Linker for VLA Models and Robots

Vision-Language-Action (VLA) models have achieved remarkable breakthroughs in robotics, with the action chunk playing a dominant role in these advances. Given the real-time and continuous nature of robotic motion control, the strategies for fusing a queue of successive action chunks have a profound impact on the overall performance of VLA models. Existing methods suffer from jitter, stalling, or even pauses in robotic action execution, which not only limits the achievable execution speed but also reduces the overall success rate of task completion. This paper introduces VLA-RAIL (A Real-Time Asynchronous Inference Linker), a novel framework designed to address these issues by conducting model inference and robot motion control asynchronously and guaranteeing smooth, continuous, and high-speed action execution. The core contributions of the paper are two fold: a Trajectory Smoother that effectively filters out the noise and jitter in the trajectory of one action chunk using polynomial fitting and a Chunk Fuser that seamlessly align the current executing trajectory and the newly arrived chunk, ensuring position, velocity, and acceleration continuity between two successive action chunks. We validate the effectiveness of VLA-RAIL on a benchmark of dynamic simulation tasks and several real-world manipulation tasks. Experimental results demonstrate that VLA-RAIL significantly reduces motion jitter, enhances execution speed, and improves task success rates, which will become a key infrastructure for the large-scale deployment of VLA models.

cs.RO↗

arXiv2025

AINav: Large Language Model-Based Adaptive Interactive Navigation

Robotic navigation in complex environments remains a critical research challenge. Traditional navigation methods focus on optimal trajectory generation within fixed free workspace, therefore struggling in environments lacking viable paths to the goal, such as disaster zones or cluttered warehouses. To address this problem, we propose AINav, an adaptive interactive navigation approach that proactively interacts with environments to create feasible paths to achieve originally unreachable goals. Specifically, we present a primitive skill tree for task planning with large language models (LLMs), facilitating effective reasoning to determine interaction objects and sequences. To ensure robust subtask execution, we adopt reinforcement learning to pre-train a comprehensive skill library containing versatile locomotion and interaction behaviors for motion planning. Furthermore, we introduce an adaptive replanning approach featuring two LLM-based modules: an advisor serving as a flexible replanning trigger and an arborist for autonomous plan adjustment. Integrated with the tree structure, the replanning mechanism allows for convenient node addition and pruning, enabling rapid plan adaptation in a priori unknown environments. Comprehensive simulations and experiments have demonstrated AINav's effectiveness and adaptivity in diverse scenarios. The supplementary video is available at: https://youtu.be/CjXm5KFx9AI.

cs.RO↗

arXiv2025

ReSPIRe: Informative and Reusable Belief Tree Search for Robot Probabilistic Search and Tracking in Unknown Environments

Target search and tracking (SAT) is a fundamental problem for various robotic applications such as search and rescue and environmental exploration. This paper proposes an informative trajectory planning approach, namely ReSPIRe, for SAT in unknown cluttered environments under considerably inaccurate prior target information and limited sensing field of view. We first develop a novel sigma point-based approximation approach to fast and accurately estimate mutual information reward under non-Gaussian belief distributions, utilizing informative sampling in state and observation spaces to mitigate the computational intractability of integral calculation. To tackle significant uncertainty associated with inadequate prior target information, we propose the hierarchical particle structure in ReSPIRe, which not only extracts critical particles for global route guidance, but also adjusts the particle number adaptively for planning efficiency. Building upon the hierarchical structure, we develop the reusable belief tree search approach to build a policy tree for online trajectory planning under uncertainty, which reuses rollout evaluation to improve planning efficiency. Extensive simulations and real-world experiments demonstrate that ReSPIRe outperforms representative benchmark methods with smaller MI approximation error, higher search efficiency, and more stable tracking performance, while maintaining outstanding computational efficiency.

cs.RO↗

arXiv2025

Training Report of TeleChat3-MoE

TeleChat3-MoE is the latest series of TeleChat large language models, featuring a Mixture-of-Experts (MoE) architecture with parameter counts ranging from 105 billion to over one trillion,trained end-to-end on Ascend NPU cluster. This technical report mainly presents the underlying training infrastructure that enables reliable and efficient scaling to frontier model sizes. We detail systematic methodologies for operator-level and end-to-end numerical accuracy verification, ensuring consistency across hardware platforms and distributed parallelism strategies. Furthermore, we introduce a suite of performance optimizations, including interleaved pipeline scheduling, attention-aware data scheduling for long-sequence training,hierarchical and overlapped communication for expert parallelism, and DVM-based operator fusion. A systematic parallelization framework, leveraging analytical estimation and integer linear programming, is also proposed to optimize multi-dimensional parallelism configurations. Additionally, we present methodological approaches to cluster-level optimizations, addressing host- and device-bound bottlenecks during large-scale training tasks. These infrastructure advancements yield significant throughput improvements and near-linear scaling on clusters comprising thousands of devices, providing a robust foundation for large-scale language model development on hardware ecosystems.

cs.CL↗

arXiv2025

Pronounced scale-dependent charge carrier density in graphene quantum Hall devices

The miniaturization of quantum Hall resistance standards (QHRS) using epitaxial graphene on silicon carbide necessitates understanding how device dimensions impact performance. This study reveals a pronounced scale-dependent carrier density in graphene Hall devices: under electron doping, carrier density decreases with increasing channel width (Wd), while the opposite occurs under hole doping. This phenomenon, most significant for Wd less than 400 um, directly influences the onset of magnetic field required for quantization. Fermi velocity measurements and angle-resolved photoemission spectroscopy (ARPES) analysis indicate that band structure modifications and electron-electron interactions underlie this size dependence. Utilizing machine learning with limited data, we optimized the device geometry, identifying a channel width of ~360 um as the optimal balance between resistance uncertainty and on-chip integration density. This work provides key insights for designing high-performance, miniaturized graphene-based QHRS arrays.

cond-mat.mes-hall↗

arXiv2025

CannyEdit: Selective Canny Control and Dual-Prompt Guidance for Training-Free Image Editing

Recent advances in text-to-image (T2I) models have enabled training-free regional image editing by leveraging the generative priors of foundation models. However, existing methods struggle to balance text adherence in edited regions, context fidelity in unedited areas, and seamless integration of edits. We introduce CannyEdit, a novel training-free framework that addresses this trilemma through two key innovations. First, Selective Canny Control applies structural guidance from a Canny ControlNet only to the unedited regions, preserving the original image's details while allowing for precise, text-driven changes in the specified editable area. Second, Dual-Prompt Guidance utilizes both a local prompt for the specific edit and a global prompt for overall scene coherence. Through this synergistic approach, these components enable controllable local editing for object addition, replacement, and removal, achieving a superior trade-off among text adherence, context fidelity, and editing seamlessness compared to current region-based methods. Beyond this, CannyEdit offers exceptional flexibility: it operates effectively with rough masks or even single-point hints in addition tasks. Furthermore, the framework can seamlessly integrate with vision-language models in a training-free manner for complex instruction-based editing that requires planning and reasoning. Our extensive evaluations demonstrate CannyEdit's strong performance against leading instruction-based editors in complex object addition scenarios.

cs.CV↗

arXiv2025

All-Electrical Self-Switching of van der Waals Chiral Antiferromagnet

Antiferromagnets have garnered significant attention due to their negligible stray field and ultrafast magnetic dynamics, which are promising for high-density and ultrafast spintronic applications. Their dual functionality as both spin sources and information carriers could enable all-electrical self-induced switching of antiferromagnetic order, offering great potential for ultra-compact spintronic devices. However, related progress is still elusive. Here, we report the deterministic switching of chiral antiferromagnetic orders induced by charge current at zero external magnetic field in the van der Waals (vdW) magnetically intercalated transition metal dichalcogenide CoTa3S6. This system exhibits strong interactions between cobalt atom magnetic moment lattice and itinerant electrons within the metallic layers, as demonstrated by temperature-dependent angle-resolved photoemission, scanning tunneling spectroscopy, and topological Nernst effect measurements. Notably, the itinerant-localization interactions lead to current-induced chiral spin orbit torques as well as Ruderman-Kittel-Kasuya-Yosida (RKKY) exchange torques that interact with the localized magnetic moments, facilitating all-electrical switching of the chiral magnetic order in the CoTa3S6 flake. Our work opens a promising avenue for manipulating antiferromagnetic orders by delicately engineering the synergistic interactions between magnetic moments and itinerant electrons.

cond-mat.mes-hall↗

arXiv2025

Exotic Surface Stripe Orders in Correlated Kagome Metal CsCr3Sb5

The newly discovered kagome superconductor CsCr3Sb5 exhibits distinct features with flat bands and unique magnetism, providing a compelling platform for exploring novel quantum states of correlated electron systems. Emergent charge order in this material is a key for understanding unconventional superconductivity, but it remains unexplored at the atomic scale and the underlying physics is elusive. Here, we identify and unreported stripe orders on the surface which are distinct from the bulk and investigate the underlying bulk electronic properties using a combination of scanning tunneling microscopy (STM), angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT) calculations. Specifically, a mixture of 2a0 * a0 and 3a0 * a0 stripe order is found on Cs-terminated surface while 4a0 * root3a0 stripe order is found on the Sb-terminated surface. The electronic spectra exhibit strongly correlated features resembling that of high temperature superconductors, with kagome flat bands lying about 330 meV above EF, suggesting that the electron correlations arise from Coulomb interactions and Hund's coupling. Moreover, a distinct electron-boson coupling mode is observed at approximately 100 meV. These findings provide new insights into the interplay between surface and bulk charge orders in this strongly correlated kagome system.

cond-mat.str-el↗

arXiv2025

3D-MoRe: Unified Modal-Contextual Reasoning for Embodied Question Answering

With the growing need for diverse and scalable data in indoor scene tasks, such as question answering and dense captioning, we propose 3D-MoRe, a novel paradigm designed to generate large-scale 3D-language datasets by leveraging the strengths of foundational models. The framework integrates key components, including multi-modal embedding, cross-modal interaction, and a language model decoder, to process natural language instructions and 3D scene data. This approach facilitates enhanced reasoning and response generation in complex 3D environments. Using the ScanNet 3D scene dataset, along with text annotations from ScanQA and ScanRefer, 3D-MoRe generates 62,000 question-answer (QA) pairs and 73,000 object descriptions across 1,513 scenes. We also employ various data augmentation techniques and implement semantic filtering to ensure high-quality data. Experiments on ScanQA demonstrate that 3D-MoRe significantly outperforms state-of-the-art baselines, with the CIDEr score improving by 2.15\%. Similarly, on ScanRefer, our approach achieves a notable increase in CIDEr@0.5 by 1.84\%, highlighting its effectiveness in both tasks. Our code and generated datasets will be publicly released to benefit the community, and both can be accessed on the https://3D-MoRe.github.io.

cs.CV↗

arXiv2025

Paraconsistent Constructive Modal Logic

We present a family of paraconsistent counterparts of the constructive modal logic CK. These logics aim to formalise reasoning about contradictory but non-trivial propositional attitudes like beliefs or obligations. We define their Kripke-style semantics based on intuitionistic frames with two valuations which provide independent support for truth and falsity; they are connected by strong negation as defined in Nelson's logic. A family of systems is obtained depending on whether both modal operators are defined using the same or by different accessibility relations for their positive and negative support. We propose Hilbert-style axiomatisations for all logics determined by this semantic framework. We also propose a~family of modular cut-free sequent calculi that we use to establish decidability.

cs.LO↗

arXiv2025

LLM-based Human-like Traffic Simulation for Self-driving Tests

Ensuring realistic traffic dynamics is a prerequisite for simulation platforms to evaluate the reliability of self-driving systems before deployment in the real world. Because most road users are human drivers, reproducing their diverse behaviors within simulators is vital. Existing solutions, however, typically rely on either handcrafted heuristics or narrow data-driven models, which capture only fragments of real driving behaviors and offer limited driving style diversity and interpretability. To address this gap, we introduce HDSim, an HD traffic generation framework that combines cognitive theory with large language model (LLM) assistance to produce scalable and realistic traffic scenarios within simulation platforms. The framework advances the state of the art in two ways: (i) it introduces a hierarchical driver model that represents diverse driving style traits, and (ii) it develops a Perception-Mediated Behavior Influence strategy, where LLMs guide perception to indirectly shape driver actions. Experiments reveal that embedding HDSim into simulation improves detection of safety-critical failures in self-driving systems by up to 68% and yields realism-consistent accident interpretability.

cs.RO↗

arXiv2025

Domain-Specific Fine-Tuning and Prompt-Based Learning: A Comparative Study for developing Natural Language-Based BIM Information Retrieval Systems

Building Information Modeling (BIM) is essential for managing building data across the entire lifecycle, supporting tasks from design to maintenance. Natural Language Interface (NLI) systems are increasingly explored as user-friendly tools for information retrieval in Building Information Modeling (BIM) environments. Despite their potential, accurately extracting BIM-related data through natural language queries remains a persistent challenge due to the complexity use queries and specificity of domain knowledge. This study presents a comparative analysis of two prominent approaches for developing NLI-based BIM information retrieval systems: domain-specific fine-tuning and prompt-based learning using large language models (LLMs). A two-stage framework consisting of intent recognition and table-based question answering is implemented to evaluate the effectiveness of both approaches. To support this evaluation, a BIM-specific dataset of 1,740 annotated queries of varying types across 69 models is constructed. Experimental results show that domain-specific fine-tuning delivers superior performance in intent recognition tasks, while prompt-based learning, particularly with GPT-4o, shows strength in table-based question answering. Based on these findings, this study identify a hybrid configuration that combines fine-tuning for intent recognition with prompt-based learning for question answering, achieving more balanced and robust performance across tasks. This integrated approach is further tested through case studies involving BIM models of varying complexity. This study provides a systematic analysis of the strengths and limitations of each approach and discusses the applicability of the NLI to real-world BIM scenarios. The findings offer insights for researchers and practitioners in designing intelligent, language-driven BIM systems.

cs.IR↗

arXiv2025

Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution

Modeling symmetry breaking is essential for understanding the fundamental changes in the behaviors and properties of physical systems, from microscopic particle interactions to macroscopic phenomena like fluid dynamics and cosmic structures. Thus, identifying sources of asymmetry is an important tool for understanding physical systems. In this paper, we focus on learning asymmetries of data using relaxed group convolutions. We provide both theoretical and empirical evidence that this flexible convolution technique allows the model to maintain the highest level of equivariance that is consistent with data and discover the subtle symmetry-breaking factors in various physical systems. We employ various relaxed group convolution architectures to uncover various symmetry-breaking factors that are interpretable and physically meaningful in different physical systems, including the phase transition of crystal structure, the isotropy and homogeneity breaking in turbulent flow, and the time-reversal symmetry breaking in pendulum systems.

cs.LG↗

arXiv2025

Thermal dilepton polarization and dynamics of the QCD plasma in relativistic heavy-ion collisions

We present the first theoretical study of the polarization of lepton pairs produced in $\sqrt{s_\mathrm{NN}} = 5.02$ TeV Pb+Pb collisions at the LHC, using next-to-leading order (NLO) dilepton emission rates. These calculations employ a multi-stage framework to simulate the evolution of relativistic heavy-ion collisions, and to explore the sensitivity of polarization to early times. It is found that the intermediate invariant-mass dileptons are indeed probes of the thermal equilibration process, and go beyond the reach of hadronic observables. We compute the polarization anisotropy coefficient obtained with LO dilepton rates, and show that the LO and NLO results differ radically, both in trend and in magnitude, at low and intermediate lepton pair invariant masses.

nucl-th↗