arXiv subjects

Wenbin He

Publications and source records attributed to Wenbin He.

At least 19 recordsLinked to original sources

arXiv2026

Refine Connections, Close the Gap: A Reliable Enhancement Framework for Driving Scene Topology

In autonomous driving, understanding scene topology - the connectivity between lanes and traffic elements - is critical for safe path planning and motion control. While current methods excel at detecting individual map elements, their connectivity reasoning often falls short of its theoretical potential, leaving a significant performance gap relative to the theoretical upper-bound achievable given the underlying detections. Furthermore, the decision-ready topology graphs passed to downstream tasks often remain unreliable. Current approaches typically derive connectivity by thresholding continuous topology scores; however, these scores often fail to reflect the true logical likelihood of connectivity, resulting in false positives or missing connections. Existing benchmarks further overlook this issue by primarily evaluating continuous metrics, rather than assessing the discrete connectivity required for decision-making. To bridge these gaps, we propose TopoEnhance, a novel topology enhancement framework designed to unlock the latent potential of existing methods and improve the reliability of decision-ready topology. We formulate topology enhancement as a denoising-based reconstruction process, where the model learns to recover structural consistency from stochastically corrupted ground-truth graphs. This formulation enables the model to resolve logical inconsistencies and rectify unreliable connections, producing robust discrete topology graphs that closely approach theoretical maximum performance. Extensive experiments across different baselines show that TopoEnhance consistently improves both continuous topology metrics (TOP score), and discrete connectivity measured by our adapted Topology Jaccard Similarity (TJS) metric. As a flexible, source-agnostic framework, TopoEnhance delivers substantial gains across diverse state-of-the-art baselines without requiring retraining.

cs.CV↗

arXiv2026

From Sight to Foresight: Predictive Spatial Reasoning in Vision-Language Models

Predicting future spatial states supports collision avoidance and timely decision-making in dynamic environments. However, existing vision-language models (VLMs) and benchmarks for spatial reasoning primarily focus on observed scenes, leaving predictive spatial reasoning beyond the observed interval underexplored. To this end, we introduce SpatialMind, a metric-scale VLM for spatial reasoning and future prediction. Its metric depth adapter anchors spatial reasoning to real-world scale, while its progressive state chain establishes current spatial states and observed dynamics as the foundation for future prediction. Given a video prefix, SpatialMind predicts distances, motion directions, and spatial relations in both observed and unseen future frames. For training and evaluation, we build a scalable data engine that grounds entity descriptions in metric geometry to generate question-answer pairs and state supervision. Using this engine, we construct the SpatialMind-30K dataset and the SpatialMind-2K benchmark, both covering driving and everyday egocentric scenes. The benchmark spans eight tasks across three levels: current-state understanding, observed-dynamics understanding, and future prediction. Experiments show that SpatialMind substantially outperforms both general and spatially specialized models on our benchmark while achieving competitive zero-shot performance on VSI-Bench, OSI-Bench, and VLM4D.

cs.CV↗

arXiv2026

From Fragments to Global Maps: Learning Vectorized Map Aggregation with Large Language Models

Large-scale vectorized HD maps provide structured road information that is essential for perception, localization, and planning in autonomous driving. Constructing such maps requires aggregating noisy, fragmented, and overlapping local predictions collected along a vehicle trajectory into a coherent global map. Existing aggregation methods typically rely on hand-crafted rules for fragment association and refinement. However, a fixed set of thresholds cannot effectively handle variations in road structures and prediction errors, often requiring detector-specific tuning or manual adjustment. To address this limitation, we propose MapMergeLLM, a data-driven framework that formulates vectorized map aggregation as conditional sequence generation with a large language model. Given serialized local vectorized maps, our model directly predicts the aggregated global map polylines. To reduce dependence on any particular upstream detector, we train the model on synthetic local maps generated from clean vector maps using corruptions that simulate representative prediction errors. We further introduce a coordinate tokenizer with geometry-aware pretraining to precisely represent map coordinates. In addition, we propose a line-level association loss that explicitly supervises correspondences between local observations of the same map element. Experiments on Argoverse2 and nuScenes using multiple recent upstream detectors demonstrate that MapMergeLLM substantially outperforms heuristic and optimization-based aggregation baselines without detector-specific retraining.

cs.CV↗

arXiv2026

Understanding Software Defect Prediction: A Large-scale Empirical Study Across Uncertainty Quantification and Performance Evaluation

Software defect prediction (SDP) classifiers produce probabilities used for inspection prioritization, threshold tuning, and risk communication. Probability-based uncertainty quantification (UQ) characterizes prediction confidence, but whether common UQ metrics reliably indicate performance and calibration remains unclear. We conducted a large-scale empirical study of probability-based UQ for SDP. We evaluated five UQ metrics, six performance metrics, and three calibration metrics for 16 representative classifiers. We analyzed these relationships under two prediction settings: within-project defect prediction (WPDP), using 36 benchmark datasets, and cross-project defect prediction (CPDP), using 32 feature-compatible datasets. Results showed that UQ was highly context-dependent. Under WPDP, UQ correlated more consistently with false positive rate and AUC than with MCC, F1 score, and other metrics; these correlations also varied across classifier categories and dataset collections. Performance and calibration were related but not interchangeable; classifiers with strong discrimination could still exhibit large calibration error. Under CPDP, several UQ-performance and UQ-calibration correlations weakened or reversed, indicating that uncertainty signals do not reliably transfer across projects. Thus, UQ should be evaluated against specific performance objectives. Calibration should be assessed independently using multiple metrics. Transferred probabilities should be revalidated before guiding quality-assurance decisions.

cs.SE↗

arXiv2026

Low-threshold efficient N${_2^+}$ lasing driven by sub-cycle soliton dynamics in a hollow waveguide

The phenomenon of N${_2^+}$ lasing, observed in femtosecond-laser filamentation, attract considerable interests in recent several years, with great application potentials in fields of remote sensing and ultrafast spectroscopy. Efficient N${_2^+}$ lasing at relatively-low pump energies and with high beam quality, while being highly-demanded for applications, remains, however, quite challenging in practical experiments. Here, we demonstrate a new route of generating low-threshold N${_2^+}$ lasing with unprecedently-high efficiency, which is enabled by soliton dynamics in a gas-filled hollow-tapered-capillary system. High-order-soliton compression of a 12-fs, 10-$μ$J-level pump pulse forms a sub-cycle asymmetric transient that tunnel-ionizes N${_2}$ to N${_2^+}$ and, through direct, single-photon resonant excitation, creates population inversion between the ground state ${X^2Σ_g^+}$ and the excited state ${B^2Σ_u^+}$${-}$a dynamic process distinct from the widely adopted three-state coupling picture${-}$and remarkably at unexpectedly low pump energy. In the experiments, we obtained 100-nJ-level N${_2^+}$ lasing pulses at 391 nm with conversion efficiencies up to 3.3$\times$10$^{-3}$, at pump energies of less than 50 $μ$J. These results represent improvement of more than one orders of magnitude in both generation efficiency and lasing threshold, compared with prevailing filamentation-based schemes. Our study bridges two generally-disparate fields (sub-cycle soliton dynamics and N${_2^+}$ lasing), and paves the way for narrow-band, high-beam-quality lasing pulses that may find wide applications in advanced spectroscopy and nonlinear pump-probe experiments.

physics.optics↗

arXiv2026

Scaling Expert Feedback with Reflective Edit Propagation in Compositional Knowledge Bases

Domain-specific knowledge bases (KBs) encode vertical expertise and proprietary information that organizations depend on, but curating them at scale is a persistent challenge. Although Large Language Models (LLMs) can draft initial entries efficiently, technical accuracy still requires human expert validation, and reviewing entries one by one at scale is impractical. We present Reflective Agent for Identifier Dictionary (RAID), a novel system that transforms individual expert edits into systematic knowledge updates. Unlike traditional "correct-and-save" paradigms, RAID utilizes a reflective agent to infer the underlying semantic intent behind a single expert edit and propagates that correction across the entire KB through a three-step architecture: Intent Inference, Reflection-based Planning, and User Controlled Execution. We evaluated the reflection and propagation performance on a public dataset and conducted a user study with subject matter experts with proprietary data. The evaluation shows RAID's technical feasibility in capturing expert intent and its potential to scale specialized expertise across industrial knowledge bases.

cs.HC↗

arXiv2026

Enhancement of vacuum-ultraviolet dispersive-wave emission using gas-filled tapered hollow-core fibers

The recent breakthroughs in laser-driving 229Th nuclear transition have created an urgent demand for coherent vacuum-ultraviolet (VUV) sources delivering high spectral brightness at the critical 148.38 nm isomer energy. However, generating sufficient photon flux to overcome the low nuclear excitation probability remains a challenge for compact setups. While resonant dispersive wave emission in gas-filled hollow-core fibers offers a promising route, standard capillaries face a fundamental trade-off: maximizing input coupling requires large core diameters, whereas efficient nonlinear VUV conversion demands the high intensities using small cores. Here, we resolve this conflict using a gas-filled tapered capillary fiber. This architecture utilizes a longitudinally decreasing core diameter to combine a large input aperture with adiabatic field concentration, thereby continuously enhancing the nonlinear interaction. Experimentally, we demonstrate a widely tunable source (135-240 nm) that achieves a twofold efficiency enhancement specifically at the 148.38 nm wavelength compared to uniform geometries. By providing a scalable route to high-flux VUV generation, this work establishes a critical tabletop tool for advancing solid-state nuclear clocks and time-resolved spectroscopy.

physics.optics↗

arXiv2026

Deep-ultraviolet Cherenkov radiation in all-normal-dispersion waveguide enabled by spatial-temporal dynamics

Nonlinear propagation of ultrashort pulses in multi-mode waveguides, featuring complex spatial-temporal dynamics, provides new degrees of freedom in the fields of nonlinear optics and ultrafast lasers. Here, we demonstrate a new scheme of ultraviolet Cherenkov (dispersive-wave) radiation in a gas-filled capillary with unprecedently-high pulse energy, enabled by spatial-temporal dynamics. We found that mJ-level, 40-fs pulses, launched into a large-core capillary filled with high-pressure noble gas, would experience self-phase-modulation and self-steepening effects in this normal-dispersion waveguide, leading to high-intensity shock wave generation and asymmetric spectral broadening. Spatial-temporal dynamics, stemming from strong nonlinear inter-mode coupling, causes spatial shrink and temporal deceleration of the pulse which dramatically alter the capillary dispersion landscape. As a result, a phase-matching point can be created in the ultraviolet, giving rise to the radiation of multi-mode dispersive waves with 100-μJ-level pulse energies and few-fs pulse widths. Our findings inspire new insights into multi-mode nonlinear optics, and the demonstrated high-energy ultraviolet light source with broadband tunability and compact set-up configuration, may find a few applications in time-resolved spectroscopy, ultrafast electronics and femtosecond chemistry.

physics.optics↗

arXiv2026

Hierarchical self-organization of highly-ordered granular ensemble of optical solitons through collective motions

Self-organizations of ordered patterns in far-from-equilibrium many-body systems host fundamental importance in many disciplines. Meanwhile, complex systems often feature hierarchical structures with distinct scales for different layers, enabling high-level effective dynamics without exhaustive tracking of all possible degrees of freedoms. In this work, we report a study of the self-organization dynamics of highly-ordered soliton ensembles in a high-harmonic mode-locked fiber lasers through collective motions driven by nonlocal optomechanical interactions and local collisions, which exhibit a series of universal characteristics reminiscent of phase transitions. Moreover, the multi-soliton laser-field can be coarsely grained as a granular ensemble of limit-cycle oscillators with simple interaction rules derived from fine-scale physics. The self-organization of the multitude of solitons in the mode-locked laser cavity can then be mapped into a low-dimensional dynamic model that essentially reproduced the emergent process. Our work affords a conceptual framework for understanding the complex structure formation in nonlinear laser systems, and may help to design ultrafast lasers by exploiting universal principles of collective motions.

physics.optics↗

arXiv2025

Rethinking Agentic Workflows: Evaluating Inference-Based Test-Time Scaling Strategies in Text2SQL Tasks

Large language models (LLMs) are increasingly powering Text-to-SQL (Text2SQL) systems, enabling non-expert users to query industrial databases using natural language. While test-time scaling strategies have shown promise in LLM-based solutions, their effectiveness in real-world applications, especially with the latest reasoning models, remains uncertain. In this work, we benchmark six lightweight, industry-oriented test-time scaling strategies and four LLMs, including two reasoning models, evaluating their performance on the BIRD Mini-Dev benchmark. Beyond standard accuracy metrics, we also report inference latency and token consumption, providing insights relevant for practical system deployment. Our findings reveal that Divide-and-Conquer prompting and few-shot demonstrations consistently enhance performance for both general-purpose and reasoning-focused LLMs. However, introducing additional workflow steps yields mixed results, and base model selection plays a critical role. This work sheds light on the practical trade-offs between accuracy, efficiency, and complexity when deploying Text2SQL systems.

cs.CL↗

arXiv2025

Reducing Averaging Time in Dual-comb Spectroscopy via Phase-Patterned Higher-Repetition-Rate Pulses

Dual-comb spectroscopy (DCS) is a powerful Fourier-transform spectroscopic technique that provides high-speed, high-resolution, and broadband measurements without moving parts. However, the high peak power of mode-locked pulses limits the photodetector's dynamic range, resulting in a low signal-to-noise ratio (SNR) per acquisition. While coherent averaging can improve SNR, it sacrifices temporal resolution and demands stringent system stability. Here, we introduce a novel concept to enhance SNR by using phase-patterned higher-repetition-rate combs. We reinterpret the self-imaging process of comb spectrum from a new perspective on mode interference among sub-pulse trains As a proof-of-concept, we densified two 250-MHz frequency combs to 12.5-MHz mode spacings via phase modulation and performed DCS on an $\mathrm{H^{13}C^{14}N}$ gas cell, and compared the results with an emulated conventional 12.5-MHz DCS, demonstrating a 17-fold increase in mode amplitude. This concept is expected to be combined with ultra-high repetition rate combs, such as microcombs, and thereby deployed in practical applications that typically require spectral sampling spacings from hundreds of MHz to GHz range.

physics.optics↗

arXiv2025

ProSAM: Enhancing the Robustness of SAM-based Visual Reference Segmentation with Probabilistic Prompts

The recent advancements in large foundation models have driven the success of open-set image segmentation, a task focused on segmenting objects beyond predefined categories. Among various prompt types (such as points, boxes, texts, and visual references), visual reference segmentation stands out for its unique flexibility and strong zero-shot capabilities. Recently, several SAM-based methods have made notable progress in this task by automatically generating prompts to guide SAM. However, these methods often generate prompts at boundaries of target regions due to suboptimal prompt encoder, which results in instability and reduced robustness. In this work, we introduce ProSAM, a simple but effective method to address the stability challenges we identified in existing SAM-based visual reference segmentation approaches. By learning a variational prompt encoder to predict multivariate prompt distributions, ProSAM avoids generating prompts that lie in unstable regions, overcoming the instability caused by less robust prompts. Our approach consistently surpasses state-of-the-art methods on the Pascal-5$^i$ and COCO-20$^i$ datasets, providing a more robust solution for visual reference segmentation.

cs.CV↗

arXiv2025

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models

The recent explosive interest in the reasoning capabilities of large language models, such as DeepSeek-R1, has demonstrated remarkable success through reinforcement learning-based fine-tuning frameworks, exemplified by methods like Group Relative Policy Optimization (GRPO). However, such reasoning abilities remain underexplored and notably absent in vision foundation models, including representation models like the DINO series. In this work, we propose \textbf{DINO-R1}, the first such attempt to incentivize visual in-context reasoning capabilities of vision foundation models using reinforcement learning. Specifically, DINO-R1 introduces \textbf{Group Relative Query Optimization (GRQO)}, a novel reinforcement-style training strategy explicitly designed for query-based representation models, which computes query-level rewards based on group-normalized alignment quality. We also apply KL-regularization to stabilize the objectness distribution to reduce the training instability. This joint optimization enables dense and expressive supervision across queries while mitigating overfitting and distributional drift. Building upon Grounding-DINO, we train a series of DINO-R1 family models that integrate a visual prompt encoder and a visual-guided query selection mechanism. Extensive experiments on COCO, LVIS, and ODinW demonstrate that DINO-R1 significantly outperforms supervised fine-tuning baselines, achieving strong generalization in both open-vocabulary and closed-set visual prompting scenarios.

cs.CV↗

arXiv2025

ViT-Split: Unleashing the Power of Vision Foundation Models via Efficient Splitting Heads

Vision foundation models (VFMs) have demonstrated remarkable performance across a wide range of downstream tasks. While several VFM adapters have shown promising results by leveraging the prior knowledge of VFMs, we identify two inefficiencies in these approaches. First, the interaction between convolutional neural network (CNN) and VFM backbone triggers early layer gradient backpropagation. Second, existing methods require tuning all components, adding complexity. Besides, these adapters alter VFM features, underutilizing the prior knowledge. To tackle these challenges, we propose a new approach called ViT-Split, based on a key observation: the layers of several VFMs, like DINOv2, can be divided into two distinct components: an extractor for learning low-level features and an adapter for learning task-specific features. Leveraging this insight, we eliminate the CNN branch and introduce two heads, task head and prior head, to the frozen VFM. The task head is designed to learn task-specific features, mitigating the early gradient propagation issue. The prior head is used to leverage the multi-scale prior features from the frozen VFM, reducing tuning parameters and overfitting. Extensive experiments on various tasks (e.g., segmentation, detection, depth estimation, and visual question answering) validate the effectiveness and efficiency of ViT-Split. Specifically, ViT-Split reduces training time up to $4\times$ while achieving comparable or even better results on ADE20K, compared to other VFM adapters.

cs.CV↗

arXiv2025

VISTA: A Visual Analytics Framework to Enhance Foundation Model-Generated Data Labels

The advances in multi-modal foundation models (FMs) (e.g., CLIP and LLaVA) have facilitated the auto-labeling of large-scale datasets, enhancing model performance in challenging downstream tasks such as open-vocabulary object detection and segmentation. However, the quality of FM-generated labels is less studied as existing approaches focus more on data quantity over quality. This is because validating large volumes of data without ground truth presents a considerable challenge in practice. Existing methods typically rely on limited metrics to identify problematic data, lacking a comprehensive perspective, or apply human validation to only a small data fraction, failing to address the full spectrum of potential issues. To overcome these challenges, we introduce VISTA, a visual analytics framework that improves data quality to enhance the performance of multi-modal models. Targeting the complex and demanding domain of open-vocabulary image segmentation, VISTA integrates multi-phased data validation strategies with human expertise, enabling humans to identify, understand, and correct hidden issues within FM-generated labels. Through detailed use cases on two benchmark datasets and expert reviews, we demonstrate VISTA's effectiveness from both quantitative and qualitative perspectives.

cs.CV↗

arXiv2025

InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions

The rise of Large Language Models (LLMs) and generative visual analytics systems has transformed data-driven insights, yet significant challenges persist in accurately interpreting users' analytical and interaction intents. While language inputs offer flexibility, they often lack precision, making the expression of complex intents inefficient, error-prone, and time-intensive. To address these limitations, we investigate the design space of multimodal interactions for generative visual analytics through a literature review and pilot brainstorming sessions. Building on these insights, we introduce a highly extensible workflow that integrates multiple LLM agents for intent inference and visualization generation. We develop InterChat, a generative visual analytics system that combines direct manipulation of visual elements with natural language inputs. This integration enables precise intent communication and supports progressive, visually driven exploratory data analyses. By employing effective prompt engineering, and contextual interaction linking, alongside intuitive visualization and interaction designs, InterChat bridges the gap between user interactions and LLM-driven visualizations, enhancing both interpretability and usability. Extensive evaluations, including two usage scenarios, a user study, and expert feedback, demonstrate the effectiveness of InterChat. Results show significant improvements in the accuracy and efficiency of handling complex visual analytics tasks, highlighting the potential of multimodal interactions to redefine user engagement and analytical depth in generative visual analytics.

cs.HC↗

arXiv2025

Flexible delivery of high-power picosecond laser in purely-single optical mode of anti-resonant hollow-core fiber for micromachining

We present the flexible delivery of picosecond laser pulses with up to 20 W average power over a 3-m-long sample of anti-resonant hollow-core fiber (AR-HCF) for laser micromachining applications. Our experiments highlight the importance of optical mode purity of the AR-HCF for the manufacturing precision. We demonstrate that compared with an AR-HCF sample with a capillary to core (d/D) ratio of ~0.5, the AR-HCF with a d/D ratio of ~0.68 exhibits better capability of high-order-mode suppression, giving rise to improved micromachining quality. Moreover, the AR-HCF delivery system exhibits better pointing stability and set-up flexibility than the free-space beam delivery system. These results pave the way to practical applications of AR-HCF in developing advanced equipment for ultrafast laser micromachining.

physics.optics↗

arXiv2024

Tunable ultraviolet dispersive-wave emission driven directly by 40-fs Ti: sapphire laser pulses in hollow capillary fiber

We demonstrate that by using 1-m-long gas-filled hollow capillary fiber (HCF) with a core diameter of 100 μm, tunable ultraviolet (UV) dispersive-wave (DW) pulses can be generated in a compact, single-stage set-up driven directly by 40-fs Ti: sapphire laser pulses. By adjusting the gas type and pressure inside the HCF, the central wavelength of the UV DW can be continuously tuned from 185 nm to ~450 nm. In the experiment, we found that for longer-wavelength (from ~320 to ~450 nm) DW generation, Raman-active gas filled in the HCF can efficiently suppress the pulse splitting effect of the high-order soliton due to the Raman-induced pulse energy dissipation, leading to the high-quality DW generation at these wavelengths with smooth, single-peak spectra. These results provide some useful insights for designing compact, wavelength-tunable ultrafast UV light sources with microjoule-level pulse energies.

physics.optics↗