arXiv subjects

Yong Zhang

Publications and source records attributed to Yong Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2026

Anomalous enhancement of thermal radiation transport by quasidisorder

The transition from order to disorder is conventionally regarded as detrimental to solid-state heat transfer in classical wave and quasiparticle systems. In striking contrast, we show that in near-field thermal radiation, breaking long-range order-shifting from periodic to quasiperiodic configurations-induces a counterintuitive enhancement of energy transport. This effect arises from delocalized interactions within quasiperiodic elements, where this quasiperiodicity relays and amplifies thermal electromagnetic energy transfer across large spatial separations, surpassing even corresponding near-field scenarios. The extraordinary transport properties induced by quasidisorder effect of near-field thermal radiation could unlock exciting opportunities for heat flow manipulation, offering transformative implications for thermal science and advancing the fundamental understanding of collective excitations in non-ordered systems.

physics.app-ph↗

arXiv2026

MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering

Existing granular-ball generation methods are still mainly driven by handcrafted quality measures and heuristic splitting or stopping criteria, which may weaken the transparency of local generation decisions in clustering. To address this issue, this paper proposes Minimum Description Length based Granular-Ball Generation (MDL-GBG), a non-parametric and interpretable granular-ball generation method for clustering. MDL-GBG reformulates granular-ball generation as a local model selection problem under the Minimum Description Length principle. For each granular ball, three candidate explanations are compared, namely a single-ball model, a two-ball model, and a core-ball-residual model, and the model with the shortest description length is selected. In this way, ball retention, splitting, and residual peeling are unified within a common coding-theoretic framework. A residual reassignment mechanism is further introduced to re-evaluate peeled-off boundary samples after stable granular balls are formed. Experiments on 20 UCI datasets show that the stable granular balls generated by MDL-GBG provide an effective upstream representation for clustering. In particular, MDL-GBG+AC achieves the highest average ARI, ACC, and NMI values among the compared methods, while the Friedman-Nemenyi analysis further supports its favorable average ranking. These results indicate that MDL-GBG offers a principled and interpretable alternative to heuristic granular-ball generation strategies.

cs.LG↗

arXiv2026

Interferometric molecular line observations toward the 21 μm protoplanetary nebula IRAS 06530-0213

The identification of the $21\,μ\mathrm{m}$ feature in some protoplanetary nebulae remains a longstanding puzzle, whose interpretation requires characterization of the molecular gas environments of associated sources. Here, we present high-resolution interferometric observations from the Northern Extended Millimeter Array toward the $21\,μ\mathrm{m}$ protoplanetary nebula IRAS 06530-0213. Multiple molecular transitions of $\mathrm{HC_3N}$, $\mathrm{C_4H}$, and $\mathrm{SiC_2}$ are detected and spatially resolved. We analyze these spectral lines alongside previously reported CO and $^{13}\mathrm{CO}$ emission toward this source. Differing from the extended barrel-like molecular structure traced by CO and $^{13}\mathrm{CO}$, $\mathrm{HC_3N}$ and $\mathrm{C_4H}$ are predominantly concentrated in the inner low-latitude equatorial zones. Reproducing the observed morphology requires $\mathrm{HC_3N}$ to possess a more inwardly concentrated abundance distribution compared with CO and $^{13}\mathrm{CO}$, implying that the inner equatorial regions provide favorable conditions for carbon-chain chemistry.

astro-ph.GA↗

arXiv2026

Non-volatile integrated photonics on lithium tantalate-on-insulator

Scalable reconfigurable photonic integrated circuits require low-loss, high-speed optical control without continuous holding power. Yet widely used thermo-optic tuning and continuously biased electro-optic tuning consume static power and introduce thermal crosstalk or bias drift. Here we demonstrate a monolithic non-volatile photonics platform on lithium tantalate-on-insulator (LTOI). In congruent x-cut lithium tantalate, the switched ferroelectric-domain configuration is retained after the write field is removed. On the same LTOI platform, we demonstrate a waveguide propagation loss of approximately 0.05-0.06 dB/cm and multilevel non-volatile phase tuning in separate devices. The programmed states remain distinguishable through $10^{6}$ write cycles. Weighted segmented electrodes resolve 137 phase positions across a $π$ range, corresponding to an analogue phase-setting resolution of approximately $0.007π$. We further combine non-volatile phase control with high-speed electro-optic modulation to achieve zero-static-power bias control of a >110 GHz modulator and a 59.3 dB extinction ratio after non-volatile trimming. At the system level, an image-edge-detection chip achieves a measured energy efficiency of 3.48 TOPS/W. These results establish LTOI as an integrated photonics platform that combines persistent optical reconfigurability with low-loss routing and high-speed electro-optic modulation.

physics.optics↗

arXiv2026

On two-dimensional steady compactly supported Euler flows with constant vorticity

In this paper, we study the two-dimensional steady compactly supported incompressible Euler equations with free boundaries. We consider flows with constant vorticity that are perturbations of annular equilibria, in contrast to the laminar flows that predominate in the existing literature on steady water waves. More precisely, we analyze three distinct classes of steady Euler flows with compact support, which correspond, respectively, to partially overdetermined, two-phase overdetermined, and (fully) overdetermined elliptic free-boundary problems. Our main contributions are threefold. For each class, we first prove a flexibility result-the existence of nontrivial admissible domains-by combining shape derivatives with local bifurcation theory. Second, we establish the corresponding rigidity results. Third, we apply the implicit function theorem to show that the standard annular flows are stable under small perturbations of the Neumann boundary condition. These results provide new perspectives on the theory of overdetermined elliptic problems.

math.AP↗

arXiv2026

Leveraging Raman response in X-cut thin-film lithium tantalate for ultrabroadband combs and polychromatic visible light

X-cut thin-film lithium tantalate (TFLT) offers a unique combination of third nonlinearity, electro-optic effects, and a high optical damage threshold. However, its strong Raman response has historically hindered broadband Kerr comb generation. Here, we leverage this inherent Raman response by engineering coupling-defined dissipation. This allows us to reconfigure the relative thresholds of Raman and Kerr processes without modifying the intrinsic microresonator dispersion. Through this coupling-engineered threshold control, we can deliberately access distinct comb states, ranging from pure Kerr combs to Raman-Kerr synergistic broadband combs. We demonstrate a Kerr comb spanning 450 nm and a Raman-Kerr comb spanning 650 nm, representing the broadest combs reported to date on X-cut TFLT platforms. Moreover, in strongly coupled devices, we show that a single near-infrared pump can generate visible emission across multiple bands (from violet to red) via cascaded second sum-frequency processes. Our work demonstrates that a strong Raman response can be transformed from a parasitic competitor into an enabling mechanism for achieving broader comb spectra and generating polychromatic visible light. This work establishes X-cut TFLT as a powerful monolithic platform for nonlinear light sources, electro-optic functions, and complex photonic systems.

physics.optics↗

arXiv2026

DiffLens: A Visualization System to Explore Local Differences in Graph Sampling

Graph sampling techniques have been widely used to simplify network computation and visualization, which also results in inevitable differences between the sampled networks and the original networks in terms of nodes, edges and structures. Investigating such differences can inform graph sampling technique users of the pros and cons of different techniques and select the appropriate one, and can also help graph sampling developers evaluate their own technique. However, there are still no systematic ways to achieve such a goal. This paper fills this research gap by first proposing systematic and generic quantitative measures to quantify three categories of graph differences (i.e., neighbor-based, path-based, and structure-based). Built upon this, we further propose DiffLens, a novel visualization system to help graph sampling developers and users intuitively explore local differences at different regions of their interest within a sampled graph, where three new lens-based visual designs are presented to display the neighbor-based, path-based, and structure-based differences respectively. We conducted two case studies and a user study using real-world network datasets to evaluate DiffLens. The results confirmed its effectiveness and usability in helping users explore local differences and compare different graph sampling strategies.

cs.HC↗

arXiv2026

Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry

Large language models (LLMs) are widely used as reference-free evaluators via prompting, but this "LLM-as-a-Judge" paradigm is costly, opaque, and sensitive to prompt design. In this work, we investigate whether smaller models can serve as efficient evaluators by leveraging internal representations instead of surface generation. We uncover a consistent empirical pattern: small LMs, despite with weak generative ability, encode rich evaluative signals in their hidden states. This motivates us to propose the Semantic Capacity Asymmetry Hypothesis: evaluation requires significantly less semantic capacity than generation and can be grounded in intermediate representations, suggesting that evaluation does not necessarily need to rely on large-scale generative models but can instead leverage latent features from smaller ones. Our findings motivate a paradigm shift from LLM-as-a-Judge to Representation-as-a-Judge, a decoding-free evaluation strategy that probes internal model structure rather than relying on prompted output. We instantiate this paradigm through INSPECTOR, a probing-based framework that predicts aspect-level evaluation scores from small model representations. Experiments on reasoning benchmarks (GSM8K, MATH, GPQA) show that INSPECTOR substantially outperforms prompting-based small LMs and closely approximates full LLM judges, while offering a more efficient, reliable, and interpretable alternative for scalable evaluation. The code and data are available at: https://github.com/zhuochunli/Representation-as-a-judge

cs.CL↗

arXiv2026

OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators

We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR video generators can produce long videos with low latency, but still suffer from error accumulation and weakened motion dynamics during long autoregressive rollout. OPSD-V reduces long-horizon degradation while preserving the original few-step inference path. The key idea is to introduce real long-video data as temporal context during training and use it to provide dense trajectory-level supervision. Specifically, the student follows the exact inference-time rollout, generating each chunk conditioned on its own previously generated KV cache. In parallel, the teacher is evaluated at the same student-visited denoising states, but uses a cleaner AR-consistent temporal cache in which older history can be replaced by real-video context. This provides dense denoising-level corrective targets under on-policy AR cache dynamics, without changing the sampler, number of denoising steps, or inference-time cache mechanism. We apply OPSD-V to representative few-step AR video models, including Self-Forcing and LongLive. Experiments show consistent improvements in visual quality, motion dynamics, and VBenchLong scores. A user study with 10 participants comparing 20 video pairs shows that OPSD-V is preferred over the base models in 66.0% of overall-preference judgments (82.5% excluding ties).

cs.CV↗

arXiv2026

Network Dynamics-Based Framework for Understanding Deep Neural Networks

Advancements in artificial intelligence call for a deeper understanding of the fundamental mechanisms underlying deep learning. In this work, we propose a theoretical framework to analyze learning dynamics through the lens of dynamical systems theory. We redefine the notions of linearity and nonlinearity in neural networks by introducing two fundamental transformation units at the neuron level: order-preserving transformations and non-order-preserving transformations. Different transformation modes lead to distinct collective behaviors in weight vector organization, different modes of information extraction, and the emergence of qualitatively different learning phases. Transitions between these phases may occur during training, accounting for key phenomena such as grokking. To further characterize generalization and structural stability, we introduce the concept of attraction basins in both sample and weight spaces. The distribution of neurons with different transformation modes across layers, along with the structural characteristics of the two types of attraction basins, forms a set of core metrics for analyzing the performance of learning models. Hyperparameters such as depth, width, learning rate, and batch size act as control variables for fine-tuning these metrics. Our framework not only sheds light on the intrinsic advantages of deep learning, but also provides a novel perspective for optimizing network architectures and training strategies.

cs.LG↗

arXiv2026

Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression

Retrieval-augmented generation (RAG) often suffers from long and noisy retrieved contexts. Existing context compression methods typically rely on heuristic relevance estimation or supervised compression models rather than on how LLMs utilize retrieved context during inference. We propose Sentinel, a lightweight sentence-level compression framework that decodes inference-time contextual utilization behaviors from head-wise attention patterns of frozen LLMs. To ground supervision in retrieval-dependent answering behavior, Sentinel trains a lightweight probe using QA examples where the model succeeds only when retrieved context is available. Sentinel performs compression using only a single non-autoregressive forward pass without dedicated compression training or autoregressive scoring. Empirically, we find that effective contextual utilization signals remain accessible even in compact proxy models. On LongBench, Sentinel with a 0.5B proxy model achieves up to 5$\times$ compression while attaining question-answering performance competitive with compression methods built on 7B-scale models. Despite being trained only on English QA data, Sentinel also generalizes effectively to Chinese and out-of-domain settings.

cs.CL↗

arXiv2026

From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models

Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances. Reliable temporal understanding is essential for such agents to anticipate events, attribute causes, and act safely in dynamic environments, yet this remains a significant challenge even for state-of-the-art (SoTA) VLMs. Prior video benchmarks have emphasized other content (sports, cooking, etc.), yet no existing benchmark focuses exclusively on temporal understanding for both short- and long-form AD footage. To fill this gap, we present the Temporal Understanding in Autonomous Driving (TAD) benchmark, comprising nearly 6000 question-answer (QA) pairs across 7 tasks, and evaluate 9 closed- and open-source generalist as well as AD-specialist models. Current SoTA models perform substantially below human accuracy on TAD. To improve the temporal reasoning of VLM-based driving agents, we propose two novel training-free solutions: Scene-CoT, which uses Chain-of-Thought (CoT) reasoning, and TCogMap, which incorporates an ego-centric temporal cognitive map produced by a trajectory-analysis module that operates as an agentic tool around the VLM. Integrated with existing VLMs, our methods improve average accuracy on TAD by up to $17.72\%$ and by up to $10.35\%$ on STSBench. By introducing TAD, benchmarking SoTA models, and proposing effective enhancements, this work aims to catalyze further progress on temporal understanding for agentic AD systems operating in the wild. The benchmark and evaluation code are available at ${\href{https://huggingface.co/datasets/vbdai/TAD}{\text{Hugging Face}}}$ and ${\href{https://github.com/vbdi/tad_bench}{\text{GitHub}}}$, respectively.

cs.CV↗

arXiv2026

Hybrid Energy-Aware Reward Shaping: A Unified Lightweight Physics-Guided Methodology for Policy Optimization

Deep reinforcement learning for continuous control often suffers from high variance, low energy efficiency, and poor generalization under distribution shift, as purely data-driven exploration ignores available physical structure. This paper proposes Hybrid Energy-Aware Reward Shaping (H-EARS), which encodes dominant energy terms -- assumed known a priori -- directly as reward potentials at O(n) per-step computation. H-EARS decomposes the shaping potential into task-oriented and energy-based components, supplemented by an action regularization term that deliberately modifies the optimization objective to enforce energy-efficient control. A complete theoretical foundation is established: functional independence of shaping and regularization, energy-based gradient enrichment under positive-definite Hessian conditions, convergence guarantees under function approximation, and approximate potential error bounds. Across four continuous control benchmarks and four baseline algorithms, H-EARS achieves consistent gains in convergence speed, policy stability, and final performance. High-fidelity vehicle simulations validate applicability in safety-critical settings under extreme road conditions.

cs.LG↗

arXiv2026

CPPO: Contrastive Perception Policy Optimization for VLM Agents

We introduce CPPO, a Contrastive Perception Policy Optimization method for finetuning vision--language models (VLMs). Reliable perception is a core requirement for VLM-based agents that must reason and act in open-ended environments: faulty visual grounding cascades directly into faulty actions, hallucinated tool calls, and unsafe decisions. While reinforcement learning (RL) has significantly improved reasoning in language models, extending these advances to multimodal agents requires improving both perception and reasoning. Prior works address this challenge mainly through explicit perception rewards, which often require extra LLM judges, ground-truth annotations, or forced separation of perception from reasoning. CPPO addresses this limitation in a self-supervised manner by extending the RL objective with a Contrastive Perception Loss (CPL) that provides a direct learning signal for visual grounding. The contrastive objective encourages the model to become more sensitive to input visual information. To apply this signal effectively, CPPO identifies perception tokens using an entropy-shift mechanism in the model's output distributions under perturbed images and applies the contrastive loss selectively to those tokens during training. Experiments show that CPPO surpasses prior methods while avoiding extra models, making training more efficient and scalable, and yielding policies that are better suited to perception-critical agentic tasks.

cs.CV↗

arXiv2026

LongCat-Video-Avatar 1.5 Technical Report

Despite advances in audio-driven video generation, achieving commercial-grade stability remains challenging. We present LongCat-Video-Avatar 1.5, an upgraded open-source framework prioritizing systematic engineering and production-readiness over architectural novelty. By upgrading the audio encoder to Whisper Large and meticulously scaling our training recipes, v1.5 achieves accurate lip-synchronization, full-body temporal stability, and robust long-video generation with strict identity consistency. Through rigorous data curation and RLHF Training, the model readily generalizes to stylized domains such as anime and animals, and natively handles complex real-world conditions, such as multi-person interactions and object handling. Furthermore, addressing the practical demands of industrial deployment, we employ advanced step distillation to accelerate inference to an optimal 8 NFE, achieving a favorable trade-off between serving efficiency and visual fidelity. The superiority of our approach is validated through extensive quantitative metrics and a rigorous human evaluation conducted on a comprehensive benchmark of over 500 diverse test cases. Results show that v1.5 achieves competitive or superior performance compared to leading closed-source systems (e.g., HeyGen, OmniHuman 1.5, Kling Avatar 2.0) across human-likeness ratings and expert-level quality assessments on our benchmark. With its open-source release, LongCat-Video-Avatar 1.5 narrows the gap between academic research prototypes and commercial-grade deployment.

cs.CV↗

arXiv2026

GPU-Accelerated OLTP: An In-Depth Analysis of Concurrency Control Schemes

Over the past decade, GPUs have demonstrated significant potential in accelerating Online Analytical Processing (OLAP) operations. However, there remains a substantial gap in their application to Online Transaction Processing (OLTP), as GPUs were traditionally considered unsuitable for such workloads. Despite this perception, the massive parallelism and high memory bandwidth of GPUs offer a unique opportunity to process thousands of transactions concurrently, making them promising candidates for OLTP acceleration. Concurrency control schemes, which play a critical role in determining the performance of OLTP systems, may behave differently on GPUs due to their architectural differences from CPUs. This raises a key question: How well do concurrency control schemes designed for CPUs adapt to GPU environments? To answer this, we present gCCTB, the first testbed designed to evaluate concurrency control schemes on GPUs. We implement and benchmark eight CC schemes, including six classic CPU-oriented schemes and two designed specifically for GPUs, on both the YCSB and TPC-C benchmarks under varied contention levels and GPU configurations. Our findings reveal that GPU-optimized schemes do not consistently outperform CPU-oriented schemes, particularly under specific workloads and contention levels. Moreover, GPU-specific parameters, such as the number of threads per warp and warps per block, significantly impact performance and require careful tuning. Finally, we find that conflict resolution overhead is a crucial factor influencing the performance of CPU-oriented schemes on GPUs, with optimistic concurrency control consistently minimizing this overhead and outperforming other CPU-oriented schemes across all workloads.

cs.DB↗

arXiv2026

Boltzmann Distribution from Invariance of Coarse-Graining-Scale and Energy-Shift

We present a concise derivation of the Boltzmann form for single-particle energy distributions in classical many-body Hamiltonian systems. The derivation relies on two physical facts: coarse-graining-scale invariance of the empirical distribution and invariance under a uniform shift of the energy zero. These conditions uniquely yield the Boltzmann factor, whose parameter is fixed by the mean energy per particle. For separable Hamiltonians, the equilibrium weight factorizes into kinetic and configurational contributions sharing the same parameter, identified from the kinetic part as the inverse kinetic temperature. The principle extends to any physical quantity with a stationary distribution and translational invariance. It is illustrated in a one-dimensional diatomic hard-core gas and a nonlinear lattice chain, where it predicts velocity, energy, spacing, collision-time, and pressure-dependent displacement distributions in agreement with simulations. The lattice model further shows how harmonic elasticity, anharmonic corrections, internal pressure, and thermal expansion emerge from the same exponential equilibrium weights. Finally, the relationships among different ensembles are briefly discussed.

cond-mat.stat-mech↗

arXiv2026

A Gaia-linked High-purity QSO Candidate Catalog in Selected Fields with Extinction-binned Calibration and Spectrum-informed Training

We present an extinction-calibrated, Gaia-source-level QSO candidate catalog for selected fields, designed as a high-purity input catalog for fiber-spectroscopic follow-up rather than as an all-sky QSO census. The deployed selector uses Gaia astrometry and photometry, optical/infrared catalog features, and E(B-V)-binned threshold calibration; spectra are used only during training via a source-grouped spectrum-teacher model. The sample definition is layered: a four-field core domain ladder provides the main validation baseline, four application/stress-test fields probe portability, and COSMOS is treated separately as an Extreme Deep boundary case. At the recommended conservative operating point, calibrated to a validation-set purity of 0.98, the P3 spectrum-informed catalog selector achieves a measured test-set purity of 0.9809 and a spectroscopic-label completeness of 0.8869 within the frozen Gaia-linked benchmark, whereas the Gaia official QSO probability yields a spectroscopic-label completeness of 0.4493 under the same threshold protocol. The evaluation protocol excludes downstream validation/test Gaia source IDs from teacher fitting and checkpoint selection, and uses teacher probabilities only for downstream training rows. Relative to the earlier P2 teacher, P3 yields a modest mean completeness gain across five seeds, with a small decrease in purity and a small increase in false positives; the gain is most evident in higher-extinction and faint-source diagnostics. The released product is a catalog and empirical selection-function data product with source identifiers, field-layer assignments, input-coverage flags, calibrated scores, threshold flags, validation metadata, and provenance/QC fields. In COSMOS, the Gaia-linked parent set is much shallower than COSMOS2020; the robust 39-object subset is interpreted as a purity-oriented priority list rather than a completeness measurement.

astro-ph.IM↗