arXiv subjects

Chong Wang

Publications and source records attributed to Chong Wang.

At least 19 recordsLinked to original sources

arXiv2026

Taming Noise Initialization for Zero-shot Video Restoration with Image Diffusion Priors

Recent advances in diffusion models have achieved remarkable success in restoring high-quality images in a zero-shot manner. Unfortunately, extending this success to video restoration is challenging due to the substantial computational resources required to pre-train video diffusion models. A rising trend seeks to address video restoration by utilizing a pre-trained image diffusion model, initiating restoration from either frame-wise independent or identical Gaussian noise. Yet, these approaches often fall short, as such simple noise initiations fail to capture and utilize the inherent temporal correlations within degraded video frames. In this work, we propose taming noise initialization for zero-shot video restoration, which preserves the intrinsic temporal correlations by generating initial noises directly using DDIM inversion on the corrupted video frames themselves. Building on this, we introduce an adaptive noise blending strategy that suppresses degradation patterns encoded during DDIM inversion while gradually steering the noise toward a Gaussian distribution. To further emphasize temporal consistency, we propose measurement-guided trajectory attention to better align features across frames. Extensive experiments on multiple video restoration tasks demonstrate that our method outperforms existing zero-shot approaches, consistently achieving superior results on the DAVIS, REDS4, and Set8 datasets.

cs.CV↗

arXiv2026

Improving Flexible Image Tokenizers for Autoregressive Image Generation

Flexible image tokenizers aim to represent an image using an ordered 1D variable-length token sequence. This flexible tokenization is typically achieved through nested dropout, where a portion of trailing tokens is randomly truncated during training, and the image is reconstructed using the remaining preceding sequence. However, this tail-truncation strategy inherently concentrates the image information in the early tokens, limiting the effectiveness of downstream AutoRegressive (AR) image generation as the token length increases. To overcome these limitations, we propose \textbf{ReToK}, a flexible tokenizer with \underline{Re}dundant \underline{Tok}en Padding and Hierarchical Semantic Regularization, designed to fully exploit all tokens for enhanced latent modeling. Specifically, we introduce \textbf{Redundant Token Padding} to activate tail tokens more frequently, thereby alleviating information over-concentration in the early tokens. In addition, we apply \textbf{Hierarchical Semantic Regularization} to align the decoding features of earlier tokens with those from a pre-trained vision foundation model, while progressively reducing the regularization strength toward the tail to allow finer low-level detail reconstruction. Extensive experiments demonstrate the effectiveness of ReTok: on ImageNet 256$\times$256, our method achieves superior generation performance compared with both flexible and fixed-length tokenizers. Code will be available at: \href{https://github.com/zfu006/ReTok}{https://github.com/zfu006/ReTok}

cs.CV↗

arXiv2026

Axion Quasibound States in the Continuum and Their Enhanced Axion Nonlinearity

Dynamical axion quasiparticles (DAQs) in condensed-matter systmes have emerged as a fascinating platform for exploring axion physics and nonlinear optical phenomena. However, the amplitude of the coherent oscillating $θ$ field is even smaller than that of the static counterpart, making the experimental observation of DAQs particularly challenging. Here, we introduce symmetry-sensitive bound states in the continuum (BICs) physics to enable the all-optical detection of DAQs and enhance DAQ-mediated second-harmonic generation (SHG). Specifically, we uncover that DAQs induce optical shear that breaks the $C_{2z}$ symmetry of metagratings without geometric engineering, thereby converting symmetry-protected BICs into axion quasi-BICs (qBICs). In this context, the evolution of the quality factor and topological charge in momentum space, together with the appearance of optical resonances in far field associated with axion qBICs, offers achiral optical signatures for DAQ detection beyond the widely used Kerr and Faraday effects. Furthermore, by exploiting axion qBICs in the critical coupling regime to enhance fundamental optical field, we show that the SHG efficiency can be enhanced more than $10^5$-fold compared to bulk axion materials, achieving a conversion efficiency exceeding $10^{-6}~W^{-1}$. The SHG is governed by the magnetoelectric coupling, which differs from that in conventional SHG materials. Our findings establish a new approach for probing DAQs via achiral optical effects, while providing a versatile route for reconfigurable photonics and terahertz nonlinear optics.

physics.optics↗

arXiv2026

CAMGen: A Core Change-Aware Multi-View Framework for Commit Message Generation

Commit messages help developers understand code changes and support software maintenance. Recent approaches use repository context to improve commit message generation (CMG) with large language models (LLMs). However, existing CMG methods provide limited support for understanding how local edits affect functionality jointly implemented by multiple code entities. This functional understanding is essential for generating commit messages that explain the main change and its impact. To address this problem, we propose CAMGen, a core change-aware multi-view commit message generation framework that incorporates functional context into generation. CAMGen identifies core entities that represent the primary change intent. It then forms candidate functional units through repository-wide semantic clustering and uses those containing the core entities to construct a Functional Semantic View. This view supplies semantic context for interpreting the affected functionality. CAMGen also uses context provided by the Structural Dependency View and the Historical Exemplar View. The context from all three views is then provided to the LLM together with the code diff to guide CMG. The experimental results show that CAMGen outperforms representative CMG baselines across three programming languages. Human evaluation further shows that CAMGen generates more complete commit messages with clearer explanations of change rationale and impact. These results demonstrate the value of functional context and complementary repository information for CMG.

cs.SE↗

arXiv2026

Field Theory of the Gutzwiller Projection

The Gutzwiller projection produces a well-known ansatz for spin-liquid and quantum Hall ground-state wavefunctions that is amenable to numerical simulation, with the Laughlin wavefunction being the most celebrated example. A long-standing issue is the precise relation between the Gutzwiller projection and the closely related parton gauge theory. Here, we clarify this issue by providing a field-theoretic description of the Gutzwiller projection. In a path integral description, the universal physics of the projected state is described in terms of a gauge field supported on a temporal slab of vanishing width $ε\rightarrow 0$ coupled to the bulk matter fields. We analytically derive the universal physics of various Gutzwiller projected states. Our construction is also applicable to discrete symmetries, and we use it to analyze various projected SPT states. Among our results, we show that the Gutzwiller projection is not a good ansatz for the long-distance properties of $U(1)$ Dirac spin liquids, spinon Fermi surface spin liquids or composite Fermi liquids, and that the Gutzwiller projection of a quantum spin Hall state has only quasi-long range order. In contrast, in examples where the gauge field is gapped, including Laughlin states, $\mathbb{Z}_2$ spin liquids and Luttinger liquids, we show that the slab gauge theory from Gutzwiller projection reproduces the correct universal physics of the full parton gauge theory. We also present a generalization of our field theory that describes $G$-intertwining defects, with application to self-duality defects of recent theoretical interest.

cond-mat.str-el↗

arXiv2026

Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code

Large language models (LLMs) frequently generate defective code, ranging from logical bugs to security vulnerabilities. Training-data quality is one contributor to these outcomes alongside model architecture, optimization and alignment, prompting and inference context, and decoding. However, the literature connecting specific training data quality issues to observable generated code quality issues has not been synthesized systematically. We review 114 included papers and establish taxonomies of eight generated-code quality dimensions and two broad groups of training-data issues. We then synthesize 12 recurring relationship families between training-data issues and generated-code outcomes, tracing each to its supporting papers, evidence basis, and limitations. We also synthesize detection and mitigation techniques across the data, model, and generation lifecycle. The findings position data curation as one layer of code-generation quality assurance coordinated with model- and inference-level controls, and identify provenance and controlled attribution as open research needs. Our repository is available at https://github.com/SYSUSELab/From-Data-to-Code.

cs.SE↗

arXiv2026

Benchmarking and Enhancing Design-Aware Code Review in the Linux Kernel

Code review often requires reasoning beyond local correctness. In mature software systems, reviewers must recognize design trade-offs introduced by a proposed change, compare alternative solutions, and reason about their compatibility with project-specific constraints. We refer to this problem as design-aware code review. However, existing automated review approaches primarily focus on defects and correctness, while the design knowledge accumulated in historical review discussions remains difficult to reuse. To address these challenges, we present DesignLore, a knowledge-augmented approach that extracts design decisions from historical review discussions, organizes them into an adaptive design taxonomy, and reuses relevant historical knowledge to guide code review. We further construct DesignReviewBench, a benchmark derived from real patch-review histories for systematically evaluating design-aware code review. Our evaluation shows that design-aware review remains challenging for current coding agents, with the strongest baseline achieving an F1 of only 0.213 on design trade-off detection. In comparison, DesignLore achieves an F1 of 0.259, a 21.6% relative improvement, and consistently delivers gains across different agent systems and backbone. These results highlight the importance of design-aware review and demonstrate the potential of recovering and reusing historical design knowledge to support it.

cs.SE↗

arXiv2026

Where Does the Performance Go? Understanding and Recovering Performance Regressions in C-to-Rust Translation

Rust is increasingly adopted as a memory-safe successor to C, and transpilers such as \crust{} can migrate entire C projects while preserving their behavior. Preserving behavior, however, does not guarantee preserving performance. We study this gap on 20 real-world C projects and their \crust{} translations. On the eight projects used for analysis, the translated Rust is more than 3\% slower than the original C on 12 of 19 operations, by up to 62.2\%. By comparing execution-cost profiles, optimized LLVM IR, and compiler optimization remarks across each C--Rust pair, we trace these slowdowns to nine recurring performance issues, spanning residual runtime work, missed compiler optimizations, and C-style source patterns, and derive an optimization direction for each. We then present \ourtool{}, an LLM-based approach that encodes these issues as reusable optimization rules to recover the performance of translated Rust. \ourtool{} profiles each project to locate hot functions and matches them against rule-specific patterns. The LLM then checks each rule's rewrite preconditions against the project context before rewriting, and a rewrite is kept only if it passes both functional and performance validation. On 41 operations from twelve held-out projects, \ourtool{} preserves C outputs on all tested workloads and speeds up 40 operations over the \crust{} translations, 18 of which end up faster than the original C. It outperforms the general-purpose coding agent \codex{} on 31 of the 37 operations where both preserve C outputs, and outperforms its own variant without rule guidance on 34 of the 41 operations.

cs.AI↗

arXiv2026

Finite-event modulation transfer through forward-scattering media

How spatial structure is transferred through a forward-scattering medium depends jointly on pattern scale and propagation distance. Starting from photon exit displacements, we derive modulation transfer while retaining the number, depths, and projected angles of individual deflections, with event occurrences described by a homogeneous Poisson process along the propagation path. The second angular moment yields a leading low-frequency loss proportional to $k^2z^3$, where $k$ is angular spatial frequency and $z$ is thickness. At finite frequency, the model predicts two coupled departures: loss bends below its low-frequency quadratic extrapolation at fixed thickness, and transfer curves separate by thickness at fixed $k^2z^3$. The leading corrections to both effects arise from the same fourth angular moment. For symmetric, nondegenerate projected-angle distributions, the full response further establishes increasing loss with thickness in the same medium at fixed joint variable. Binary-pattern measurements through TiO$_2$ suspensions span five nominal loadings, seven thicknesses, and 20 spatial frequencies, supporting the approximate low-frequency scaling and showing both departures. A Gaussian effective-angle response calibrated only on lower-frequency data predicts the held-out higher-frequency band without refitting, with a logarithmic root-mean-square error of 0.0181 compared with 0.1021 for quadratic extrapolation. The results show that finite-event statistics provide a common physical origin for spectral bending and thickness-dependent departure from low-frequency scaling, linking coarse-pattern transport to finer spatial structure.

physics.optics↗

arXiv2026

Maximal Axion Optical Chirality Enabled by Degenerate Quasibound States in the Continuum

Axion quasiparticles introduce magnetoelectric coupling in topological materials, yet their optical signatures are intrinsically weak, severely limiting experimental detection and practical exploitation. Here, we demonstrate maximal optical chirality of axion quasiparticles by coupling them with degenerate quasibound states in the continuum (qBICs). By integrating three-dimensional topological insulators (3DTIs) with achiral photonic crystal slabs (PCSs), we reveal that axion quasiparticles induce an effective Zeeman effect that lifts the degeneracy of achiral qBICs in PCSs, splitting them into two nondegenerate high-Q chiral qBICs with maximally opposite chirality. These result in a more than 10^3- and 200-fold increase in the polarization conversion efficiency and Faraday rotation angle, respectively, compared to the intrinsic chiroptical response of bare 3DTI films. Moreover, circular dichroism and nonreciprocal optical responses are revealed as new key signatures for probing axion quasiparticles in 3DTI-PCS coupled systems. Our results provide a definitive route for observing axion quasiparticles and topological physics, while establishing a promising platform for controlling optical polarization, spin, phase, and nonreciprocal responses.

physics.optics↗

arXiv2026

Image Diffusion Models Are Good 3D Human Motion Generators

Text-to-motion generation typically relies on dataset-specific skeletal representations, with models trained separately for benchmarks with different joint layouts. We investigate whether image diffusion architectures can model 3D human motion through a common image-compatible representation that accommodates heterogeneous skeletal layouts while preserving motion-specific kinematic structure. To this end, we organize root-centered joint trajectories into topology-informed joint--time images and model global root dynamics through a complementary latent stream. A motion autoencoder conditions local-motion decoding on the root latent, while a text-conditioned Scalable Interpolant Transformer (SiT) jointly generates both latent streams, with supervision on decoded 3D joint positions further constraining generation in motion space. In addition to dataset-specific models, we train a single motion autoencoder on HumanML3D and KIT-ML, followed by a single text-conditioned SiT shared across both datasets, preserving their respective skeletal definitions without skeleton retargeting to a common joint layout. Experiments on HumanML3D and KIT-ML show that our dataset-specific models achieve state-of-the-art performance, while shared training yields further gains on both benchmarks, demonstrating the benefits of shared modeling across heterogeneous skeletal layouts.

cs.CV↗

arXiv2026

From Suppression to Repair: Mitigating Object Hallucination in Large Vision-Language Models via Localized Distribution Alignment

Object hallucination remains a major obstacle for large vision-language models (LVLMs) to generate reliable content. An intuitive mitigation strategy is to suppress hallucination-related components in hidden representations. However, these components may also contain useful information, and suppressing them can weaken the model's multimodal capabilities. In this paper, we propose ResOT, a training-free method that repairs representations at inference time through localized distribution alignment. Specifically, ResOT projects dominant hallucinated directions away from the faithful subspace, forming a low-dimensional residual subspace for intervention. Within this subspace, ResOT uses Gaussian optimal transport (OT) to align the hallucinated distribution with the faithful one. The resulting map defines repair targets with minimal changes to the original representations. At inference, ResOT adaptively controls how far each token state moves toward its OT target. Experiments on three representative LVLMs show that ResOT substantially reduces object hallucination while improving image caption quality and multimodal performance across multiple benchmarks. Code will be released.

cs.CV↗

arXiv2026

A density-based topology optimization framework for steady Navier-Stokes flow with design-dependent wall actuation

Design-dependent wall velocities are difficult to treat in density-based fluid topology optimization. This difficulty arises because the fluid-solid interface is represented implicitly and evolves with the design. This paper proposes a diffuse-interface formulation for topology optimization of steady incompressible Navier-Stokes flows with design-dependent wall actuation. A continuous topological description field parameterized by compactly supported radial basis functions (CS-RBFs) provides the local interface orientation, and its regularized projection defines the pseudo-density for Brinkman flow analysis. The wall velocity is imposed on a fixed mesh through an equivalent volumetric momentum source, which is localized by a diffuse-interface measure constructed from the pseudo-density and the gradient of the description field. Assuming a locally planar interface and uniformly distributed Wendland radial basis functions, the source coefficient is determined analytically by balancing the equivalent momentum input with the Brinkman resistance at the interface center. These assumptions also yield a characteristic transition-width estimate in terms of the projection steepness and support radius. Continuous adjoint analysis is performed to derive the design sensitivities. Two- and three-dimensional examples formulated using the analogy equation demonstrate the effectiveness of the proposed topology optimization method for flow-rate maximization. Body-fitted re-simulations verify the flow fields and transport performance of the optimized designs. The formulation is further extended to electroosmotic transport under the thin electric double layer (EDL) assumption using the Helmholtz-Smoluchowski relation.

physics.comp-ph↗

arXiv2026

TAME:Topology-Aware Text-Driven Motion Editing across Heterogeneous Humanoid Skeletons

Text-driven motion editing modifies an existing motion sequence according to a text instruction while preserving the content of the source motion. Existing methods are typically built for a single, fixed skeletal topology, which limits their use in animation pipelines where characters differ in joint count and skeletal hierarchy. We present Topology-Aware Motion Editor (TAME), a flow-matching transformer that edits motions on humanoid skeletons of varying topology. TAME represents motion as per-joint, per-frame tokens and models interactions among joints, across frames, and with the text instruction through skeletal, temporal, and text cross-attention layers. To make the skeletal attention follow each character's hierarchy, TAME replaces full joint attention with Topology-Constrained Skeletal Propagation (TCSP), which restricts attention to one-hop kinematic neighbors in the skeleton's adjacency matrix. We further introduce Edit-Focused Representation Alignment (EFRA), a self-distilled representation alignment strategy that aligns student features with cleaner EMA-teacher features exclusively on edit-relevant joint-time tokens, making edits faithful to the instruction. To make this setting trainable and comparable, we construct TopoMotionFix, a multi-topology extension of MotionFix with seen- and unseen-topology evaluation protocols. TAME outperforms previous methods in edit alignment and source preservation on MotionFix and reliably edits motions on unseen skeletons in TopoMotionFix.

cs.CV↗

arXiv2026

GoCo: structure-guided conformal calibration for error-controlled Gene Ontology annotation release

Computational gene-function prediction generates annotations at a scale that experimental characterization cannot match, yet substantial errors remain even among leading methods. Explicit error-controlled calibration for Gene Ontology (GO) prediction remains largely undeveloped. We introduce GoCo, a structure-guided framework that calibrates how scores from an existing predictor are converted into released annotations. GoCo certifies a global threshold for a GO-hierarchy-aware gene-level false discovery proportion (FDP), then constructs a nested release path under the same asymptotic risk-control guarantee. At the primary operating point (alpha=0.10, delta=0.50), GoCo-M, developed on Wainberg co-essentiality modules and applied without retuning to three independent studies with the same shared-evidence structure, kept mean held-out gene-level FDP at or below target while releasing 6.3-17.6% more reference-supported GO terms than Multilabel. For gene-specific neighbourhoods, GoCo-N orders individual calls. Developed on FunMap and validated without retuning on the STRING v12.0 experiments-only network, GoCo-N released 33.8% and 36.4% more reference-supported GO terms than Multilabel on the same refined score grid in FunMap and STRING, respectively, while keeping mean held-out FDP below target. The same pattern held under the stricter delta=0.10 setting. Thus, predictor-native biological structure can be reused at calibration, not only prediction, to release more supported annotations at a chosen gene-level error target without retraining the predictor.

q-bio.QM↗

arXiv2026

Persistence Forcing: Exploiting Feature Specialization in Pixel-Space Diffusion

Pixel-space diffusion Transformers (DiTs) directly operate on high-dimensional visual data, yet their hidden representations typically undergo uniform refinement across depth. Natural images, however, are inherently organized at different levels of granularity. Global structure can often be represented compactly, whereas local textures and fine details require richer representations. Motivated by this, we introduce heterogeneous refinement in pixel-space DiTs, assigning different feature groups distinct refinement budgets across depth. Consequently, an ordered feature specialization emerges: sparsely refined features predominantly encode global visual structure, whereas more frequently refined features increasingly specialize toward localized, high-frequency details. We refer to these two groups as persistent and active features, respectively. Building on this emergent specialization, we introduce Persistence Forcing (PerF), which explicitly exploits this persistent--active feature organization for pixel-space image generation. This enables persistent features to continuously condition actively refined features, allowing stable global information to guide the ongoing refinement of finer visual details. During generative sampling, this interaction further induces a meaningful guidance direction that promotes coherent global structure and naturally complements classifier-free guidance. On ImageNet $256\times256$, PerF-L achieves FID of $1.91$, approaching $1.86$ of JiT-H with only half the parameters, while PerF-H further achieves FID of $1.63$ and $1.76$ on ImageNet $256\times256$ and $512\times512$, respectively.

cs.CV↗

arXiv2026

Bridging Generative and Discriminative Noisy-Label Learning via Direction-Agnostic EM Formulation

Although noisy-label learning is often approached with discriminative methods for simplicity and speed, generative modeling offers a principled alternative by capturing the joint mechanism that produces features, clean labels, and corrupted observations. However, prior work typically (i) introduces extra latent variables and heavy image generators that bias training toward reconstruction, (ii) fixes a single data-generating direction ($Y\rightarrow\!X$ or $X\rightarrow\!Y$), limiting adaptability, and (iii) assumes a uniform prior over clean labels, ignoring instance-level uncertainty. We propose a single-stage, EM-style framework for generative noisy-label learning that is \emph{direction-agnostic} and avoids explicit image synthesis. First, we derive a single Expectation-Maximization (EM) objective whose E-step specializes to either causal orientation without changing the overall optimization. Second, we replace the intractable $p(X\mid Y)$ with a dataset-normalized discriminative proxy computed using a discriminative classifier on the finite training set, retaining the structural benefits of generative modeling at much lower cost. Third, we introduce \emph{Partial-Label Supervision} (PLS), an instance-specific prior over clean labels that balances coverage and uncertainty, improving data-dependent regularization. Across standard vision and natural language processing (NLP) noisy-label benchmarks, our method achieves state-of-the-art accuracy, lower transition-matrix estimation error, and substantially less training compute than current generative and discriminative baselines. Code: https://github.com/lfb-1/EMNL

cs.CV↗

arXiv2026

Fluctuation-Driven Nonlinear Amplification of Quantum Statistics

Photon statistics have moved to the forefront of modern optics, as intensity fluctuations and correlations shape multiphoton interactions and reveal information beyond mean-intensity measurements. Developing high-quality photon sources with pronounced correlations is a fundamental necessity in these fields. Here we demonstrate fluctuation-driven nonlinear statistical amplification of quantum light in spontaneous four-wave mixing using filtered amplified spontaneous emission (ASE). Extending the coherent-pump framework to fluctuating fields, we show how nonlinear weighting of pump intensity combines with bosonic bunching to amplify quantum statistics and reshape temporal correlations. In a SiN microring, ASE pumping increases the zero-delay unconditional second-order correlation from 2.01 to 7.58 and extends the Hanbury Brown--Twiss correlation time by a factor of approximately 2.4. The super-bunched quantum source nevertheless retains heralded single-photon behaviour with $g_H^{(2)}(0)\simeq0.04$, while the same ASE pump supports time--energy entanglement in a silicon waveguide with a raw Franson visibility of 89.84\%. These results establish driving-field statistics as a design dimension for quantum light, broadening the horizons for research into higher-order correlations and nonlinear physics.

quant-ph↗