arXiv subjects

Bin Wu

Publications and source records attributed to Bin Wu.

At least 73 records · Page 4Linked to original sources

arXiv2026

CoCoDiff: Correspondence-Consistent Diffusion Model for Fine-grained Style Transfer

Transferring visual style between images while preserving semantic correspondence between similar objects remains a central challenge in computer vision. While existing methods have made great strides, most of them operate at global level but overlook region-wise and even pixel-wise semantic correspondence. To address this, we propose CoCoDiff, a novel training-free and low-cost style transfer framework that leverages pretrained latent diffusion models to achieve fine-grained, semantically consistent stylization. We identify that correspondence cues within generative diffusion models are under-explored and that content consistency across semantically matched regions is often neglected. CoCoDiff introduces a pixel-wise semantic correspondence module that mines intermediate diffusion features to construct a dense alignment map between content and style images. Furthermore, a cycle-consistency module then enforces structural and perceptual alignment across iterations, yielding object and region level stylization that preserves geometry and detail. Despite requiring no additional training or supervision, CoCoDiff delivers state-of-the-art visual quality and strong quantitative results, outperforming methods that rely on extra training or annotations.

cs.CV↗

arXiv2026

Systematic analysis of the form factors of $B_{c}$ to $P$-wave charmonia and corresponding weak decays

In this article, the vector, axial vector and tensor form factors of $B_{c}\to χ_{cJ}$ ($J=0,1,2$) and $B_{c}\to h_{c}$ are analyzed within the framework of three-point QCD sum rules. With the calculated vector and axial vector form factors, we directly study the decay widths and branching ratios of semileptonic decays $B_{c}^{-}\to χ_{cJ}l \barν_l, h_{c}l \barν_l$ $(l=e, μ$ and $τ)$ and analyze the nonleptonic decays $B_{c}^{-}\to χ_{cJ}π^{-}, χ_{cJ}K^{-}, χ_{cJ}ρ^{-}, χ_{cJ}K^{*-}$, $B_{c}^{-}\to h_{c}π^{-}, h_{c}K^{-}, h_{c}ρ^{-}, h_{c}K^{*-}$ by using the naive factorization approach (NFA). These results can provide more information to understand the properties of $B_{c}$ meson and $P$-wave charmonia and to study the heavy quark dynamics.

hep-ph↗

arXiv2026

Jet quenching and its substructure dependence due to color decoherence

Motivated by color coherence and decoherence effects in the QCD medium, we propose a theoretical framework that combines vacuum-like emissions and medium-induced radiation to study jet quenching and its dependence on jet cone sizes and substructure. In our approach, a jet produced at a hard scale $Q$ first undergoes vacuum-like evolution, as described by the well-established generating-function method in the double logarithmic approximation. These vacuum-like emissions generate subjets at an infrared momentum scale $Q_0$. Each subjet then experiences medium-induced energy loss as described by the BDMPS-Z formalism. By modeling the QCD bulk medium using OSU (2+1)-dimensional viscous hydrodynamics and treating $Q_0$ together with the jet-quenching parameters at the initial proper time of the hydrodynamic evolution as free parameters, our approach provides a very good description of the inclusive jet modification factor $R_{AA}$ for large-radius jets and its dependence on jet substructure in 0-10% PbPb collisions at $\sqrt{s_{NN}} = 5.02~\rm{TeV}$, as measured by the ATLAS experiment.

hep-ph↗

arXiv2026

Collaborative User Prompt for Personalized Generative Recommendation

Large Language Models (LLMs) have become powerful foundations for generative recommender systems, framing recommendation tasks as text generation tasks. However, existing generative recommendation methods often rely on discrete ID-based prompts or task-specific soft prompts, which overlook the valuable collaborative signals shared among users with similar interests. To address this limitation, this paper presents a compositional framework that integrates a user's individual preferences with collective preferences from similar users to build personalized soft prompts. Specifically, an attention-based mechanism fuses embeddings from users with similar interests, creating a richer representation that captures multiple facets of user preferences. This design dynamically emphasizes shared interests while preserving individual user preferences. Experiments on three real-world datasets demonstrate the effectiveness of the proposed approach across sequential recommendation, top-n recommendation, and explanation generation tasks, underscoring the advantages of incorporating collaborative signals through an attention-based compositional strategy.

cs.IR↗

arXiv2026

SFCoT: Safer Chain-of-Thought via Active Safety Evaluation and Calibration

Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks. However, they remain highly susceptible to jailbreak attacks that undermine their safety alignment. Existing defense mechanisms typically rely on post hoc filtering applied only to the final output, leaving intermediate reasoning steps unmonitored and vulnerable to adversarial manipulation. To address this gap, this paper proposes a SaFer Chain-of-Thought (SFCoT) framework, which proactively evaluates and calibrates potentially unsafe reasoning steps in real time. SFCoT incorporates a three-tier safety scoring system alongside a multi-perspective consistency verification mechanism, designed to detect potential risks throughout the reasoning process. A dynamic intervention module subsequently performs targeted calibration to redirect reasoning trajectories toward safe outcomes. Experimental results demonstrate that SFCoT reduces the attack success rate from $58.97\%$ to $12.31\%$, demonstrating it as an effective and efficient LLM safety enhancement method without a significant decline in general performance.

cs.CR↗

arXiv2026

Multiplicity distributions in QCD jets and jet topics

We evaluate the Koba-Nielsen-Olesen (KNO) scaling functions for quark- and gluon-initiated jets by incorporating energy conservation into the Double Logarithmic Approximation (DLA). The resulting modified DLA (MDLA) expressions differ substantially from the DLA predictions and qualitatively align with the recently proposed QCD-inspired expressions, albeit with some quantitative differences. By fixing the two parameters in the MDLA expressions, we show that the inclusive charged-particle multiplicity distributions of the two leading jets in $pp$ collisions at $\sqrt{s} = 13$ TeV, measured by ATLAS over a wide jet $p_T$ range of $0.1$-$2.5$ TeV, are well described within experimental uncertainties and consistent with PYTHIA simulations. This conclusion is further supported by direct comparisons with quark- and gluon-initiated jet distributions extracted via jet topics, though the propagated uncertainties from experimental data remain sizable.

hep-ph↗

arXiv2026

Functional Analysis and Parallel Domain Decomposition for the TV-Stokes Model

The TV-Stokes model is a two-step variational method for image denoising that combines the estimation of a divergence-free tangent field with total variation regularization in the first step and then uses that to reconstruct the image in the second step. Although effective in practice, its mathematical structure and potential for parallelization have remained unexplored. In this work, we establish a rigorous functional-analytic foundation for the TV-Stokes model. We formulate both steps in appropriate infinite-dimensional function spaces, derive their dual formulations, and analyze the compatibility and mathematical consistency of the coupled system. In particular, we identify analytical inconsistencies in the original formulation and demonstrate how an alternative model resolves them. We also examine the orthogonal projection onto the divergence-free subspace, proving its existence in a continuous setting and establishing consistency with its discrete counterpart. Building on this theoretical framework, we develop the first domain decomposition method for TV-Stokes by applying overlapping Schwarz-type iterations to the duals of both steps. Although the divergence-free constraint gives rise to a global projection operator in the continuous model, we show that it becomes locally computable in the discrete setting. This insight enables a fully parallelizable algorithm suitable for large-scale image processing in memory-constrained environments. Numerical experiments demonstrate the correctness of the domain decomposition approach and its usability in parallel image reconstruction.

math.NA↗

arXiv2026

NativeTok: Native Visual Tokenization for Improved Image Generation

VQ-based image generation typically follows a two-stage pipeline: a tokenizer encodes images into discrete tokens, and a generative model learns their dependencies for reconstruction. However, improved tokenization in the first stage does not necessarily enhance the second-stage generation, as existing methods fail to constrain token dependencies. This mismatch forces the generative model to learn from unordered distributions, leading to bias and weak coherence. To address this, we propose native visual tokenization, which enforces causal dependencies during tokenization. Building on this idea, we introduce NativeTok, a framework that achieves efficient reconstruction while embedding relational constraints within token sequences. NativeTok consists of: (1) a Meta Image Transformer (MIT) for latent image modeling, and (2) a Mixture of Causal Expert Transformer (MoCET), where each lightweight expert block generates a single token conditioned on prior tokens and latent features. We further design a Hierarchical Native Training strategy that updates only new expert blocks, ensuring training efficiency. Extensive experiments demonstrate the effectiveness of NativeTok.

cs.CV↗

arXiv2026

Analysis of the semileptonic decays of $Ξ_{cc}$ and $Ω_{cc}$ baryons in QCD sum rules

We firstly carry out a systematic analysis on the spin $\frac{1}{2}^{+}\rightarrow\frac{3}{2}^{+}$ weak transition process in the framework of three-point QCD sum rules, where the initial and final states are doubly and singly charmed baryons. In the phenomenological side, all possible couplings of interpolating current to hadronic states are considered. In doing operator production expansion at QCD side, the contributions of the perturbative part, vacuum condensate terms of $\langle{\bar qq}\rangle$, $\langle g_{s}^{2}GG\rangle$, $\langle \bar q g_{s}σGq\rangle$ and $g_{s}^{2}\langle{\bar qq}\rangle^{2}$ are all considered. After the form factors in space-like region ($Q^2>0$) are obtained, the numerical results are extrapolated into time-like region ($Q^2<0$) by a fitting function. Using the predicted form factors, we finally analyze the semileptonic decays of $Ξ_{cc}^{++}\rightarrow Σ_{c}^{*+}l^{+}ν_{l}$, $Ξ_{cc}^{++}\rightarrow Ξ_{c}^{\prime*+}l^{+}ν_{l}$, $Ω_{cc}^{+}\rightarrowΞ_{c}^{\prime*0}l^{+}ν_{l}$ and $Ω_{cc}^{+}\rightarrow Ω_{c}^{*0}l^{+}ν_{l}$ with $l=e,μ$. The predictions in this work can deepen our understanding of the dynamics in the decay processes of doubly heavy baryons and provide useful information to explore the possibility of new physics in heavy baryonic decay channels.

hep-ph↗

arXiv2025

Reactive near-field subwavelength microwave imaging with a non-invasive Rydberg probe

Non-invasive microwave field imaging--accurately mapping field distributions without perturbing them--is essential in areas such as aerospace engineering, biomedical imaging and integrated-circuit diagnostics. Conventional metal probes, however, inevitably perturb reactive near fields: they act as strong scatterers that drive induced currents and secondary radiation, remap evanescent components and thereby degrade both accuracy and spatial resolution, particularly in the reactive near-field regime that is most relevant to these applications. Here we demonstrate, to our knowledge for the first time, reactive near-field subwavelength imaging of microwave fields using the quantum non-demolition properties of Rydberg atoms, realized with a compact, non-invasive single-ended fibre-integrated Rydberg probe engineered to minimize field disturbance. The probe achieves an imaging resolution of {\unboldmath$λ/56$}, and the measured field distributions agree with full-wave simulations with structural similarity approaching unity, confirming both its subwavelength spatial resolution and its genuinely non-invasive character compared with conventional metal-based probes. Because the atomic sensor is intrinsically isotropic, the same device can faithfully image multi-dimensional field structures without orientation-dependent calibration. Our results therefore establish a general, non-invasive route to high-accuracy, subwavelength reactive near-field microwave imaging, with particular promise for applications such as chip-defect detection and integrated-circuit diagnostics, where even small perturbations by the probe can mask the underlying physics of interest.

quant-ph↗

arXiv2025

Enhancing Blind Face Restoration through Online Reinforcement Learning

Blind Face Restoration (BFR) encounters inherent challenges in exploring its large solution space, leading to common artifacts like missing details and identity ambiguity in the restored images. To tackle these challenges, we propose a Likelihood-Regularized Policy Optimization (LRPO) framework, the first to apply online reinforcement learning (RL) to the BFR task. LRPO leverages rewards from sampled candidates to refine the policy network, increasing the likelihood of high-quality outputs while improving restoration performance on low-quality inputs. However, directly applying RL to BFR creates incompatibility issues, producing restoration results that deviate significantly from the ground truth. To balance perceptual quality and fidelity, we propose three key strategies: 1) a composite reward function tailored for face restoration assessment, 2) ground-truth guided likelihood regularization, and 3) noise-level advantage assignment. Extensive experiments demonstrate that our proposed LRPO significantly improves the face restoration quality over baseline methods and achieves state-of-the-art performance.

cs.CV↗

arXiv2025

MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling

Lifelong user interest modeling is crucial for industrial recommender systems, yet existing approaches rely predominantly on ID-based features, suffering from poor generalization on long-tail items and limited semantic expressiveness. While recent work explores multimodal representations for behavior retrieval in the General Search Unit (GSU), they often neglect multimodal integration in the fine-grained modeling stage -- the Exact Search Unit (ESU). In this work, we present a systematic analysis of how to effectively leverage multimodal signals across both stages of the two-stage lifelong modeling framework. Our key insight is that simplicity suffices in the GSU: lightweight cosine similarity with high-quality multimodal embeddings outperforms complex retrieval mechanisms. In contrast, the ESU demands richer multimodal sequence modeling and effective ID-multimodal fusion to unlock its full potential. Guided by these principles, we propose MUSE, a simple yet effective multimodal search-based framework. MUSE has been deployed in Taobao display advertising system, enabling 100K-length user behavior sequence modeling and delivering significant gains in top-line metrics with negligible online latency overhead. To foster community research, we share industrial deployment practices and open-source the first large-scale dataset featuring ultra-long behavior sequences paired with high-quality multimodal embeddings. Our code and data is available at https://taobao-mm.github.io.

cs.IR↗

arXiv2025

Analysis of the semileptonic decays $Λ_b\toΛ_cl\barν_l$ and $Ξ_b\toΞ_cl\barν_l$ in QCD sum rules

In this article, the electroweak transition form factors of $Λ_b\toΛ_c$ and $Ξ_b\toΞ_c$ are analyzed within the framework of three-point QCD sum rules. In phenomenological side, all possible couplings of interpolating current to hadronic states are considered. In QCD side, the perturbative part and the contributions of vacuum condensates up to dimension 8 are also included. With the estimated form factors, we study the decay widths and branching ratios of semileptonic decays $Λ_b\toΛ_cl\barν_l$ and $Ξ_b\toΞ_cl\barν_l$ ($l=e,μ$ and $τ$). Our results for the branching ratios of $Λ_b\toΛ_cl\barν_l$ are comparable with experimental data and the results from other collaborations. In addition, our prediction for the branching ratio of $Ξ_b\toΞ_cl\barν_l$ can provide a valuable reference for future experimental measurements.

hep-ph↗

arXiv2025

PolarStore: High-Performance Data Compression for Large-Scale Cloud-Native Databases

In recent years, resource elasticity and cost optimization have become essential for RDBMSs. While cloud-native RDBMSs provide elastic computing resources via disaggregated computing and storage, storage costs remain a critical user concern. Consequently, data compression emerges as an effective strategy to reduce storage costs. However, existing compression approaches in RDBMSs present a stark trade-off: software-based approaches incur significant performance overheads, while hardware-based alternatives lack the flexibility required for diverse database workloads. In this paper, we present PolarStore, a compressed shared storage system for cloud-native RDBMSs. PolarStore employs a dual-layer compression mechanism that combines in-storage compression in PolarCSD hardware with lightweight compression in software. This design leverages the strengths of both approaches. PolarStore also incorporates database-oriented optimizations to maintain high performance on critical I/O paths. Drawing from large-scale deployment experiences, we also introduce hardware improvements for PolarCSD to ensure host-level stability and propose a compression-aware scheduling scheme to improve cluster-level space efficiency. PolarStore is currently deployed on thousands of storage servers within PolarDB, managing over 100 PB of data. It achieves a compression ratio of 3.55 and reduces storage costs by approximately 60%. Remarkably, these savings are achieved while maintaining performance comparable to uncompressed clusters.

cs.DC↗

arXiv2025

LLM-CoT Enhanced Graph Neural Recommendation with Harmonized Group Policy Optimization

Graph neural networks (GNNs) have advanced recommender systems by modeling interaction relationships. However, existing graph-based recommenders rely on sparse ID features and do not fully exploit textual information, resulting in low information density within representations. Furthermore, graph contrastive learning faces challenges. Random negative sampling can introduce false negative samples, while fixed temperature coefficients cannot adapt to the heterogeneity of different nodes. In addition, current efforts to enhance recommendations with large language models (LLMs) have not fully utilized their Chain-of-Thought (CoT) reasoning capabilities to guide representation learning. To address these limitations, we introduces LGHRec (LLM-CoT Enhanced Graph Neural Recommendation with Harmonized Group Policy Optimization). This framework leverages the CoT reasoning ability of LLMs to generate semantic IDs, enriching reasoning processes and improving information density and semantic quality of representations. Moreover, we design a reinforcement learning algorithm, Harmonized Group Policy Optimization (HGPO), to optimize negative sampling strategies and temperature coefficients in contrastive learning. This approach enhances long-tail recommendation performance and ensures optimization consistency across different groups. Experimental results on three datasets demonstrate that LGHRec improves representation quality through semantic IDs generated by LLM's CoT reasoning and effectively boosts contrastive learning with HGPO. Our method outperforms several baseline models. The code is available at: https://anonymous.4open.science/r/LLM-Rec.

cs.IR↗

arXiv2025

KNO scaling in quark and gluon jets at the LHC

The Koba-Nielsen-Olesen (KNO) scaling of hadron multiplicity distributions, empirically confirmed to hold approximately in $e^+e^-$ collisions and Deep Inelastic Scattering, has been observed to be violated in hadron-hadron collisions. In this work, we show that the universality of KNO scaling can be extended to hadron-hadron collisions when restricted to QCD jets. We present a comprehensive study of KNO scaling in QCD jets produced in proton-proton collisions at the LHC. Using perturbative QCD calculations in the double logarithmic approximation and PYTHIA simulations, we find that KNO scaling approximately holds for both quark and gluon jets across a broad jet $p_T$ range, from $0.1$ TeV to $2.5$ TeV, at both the parton and hadron levels. Especially, we highlight characteristic differences between the KNO scaling functions of quark and gluon jets, with the quark-jet scaling function lying above that of gluon jets at both low and high multiplicities. This distinction is essential for interpreting inclusive jet data at the LHC. Furthermore, we propose direct experimental tests of KNO scaling in QCD jets at the LHC through quark-gluon discrimination using jet substructure techniques, as demonstrated by applying energy correlation functions to PYTHIA-generated data.

hep-ph↗

arXiv2025

Thermodynamic supercriticality and complex phase diagram for charged Gauss-Bonnet AdS black holes

Lee-Yang zero theory plays a crucial role in phase transition theory and is widely employed in the critical behavior of statistical thermodynamics. The supercritical regime of black hole thermodynamics remains a relatively unexplored area, and recent applications of this theory to charged anti-de Sitter (AdS) black holes have initiated probes into this regime, revealing a simple structure partitioned by a single Widom line. In this paper, we apply Lee-Yang theory to charged Gauss-Bonnet AdS black holes, which feature complex phase diagrams (e.g., triple points), to determine how such structures are reflected in the supercritical regime. Notably, we observe a key dimensional difference in that the five-dimensional (5d) system, which lacks a triple point, confirms the known single Widom line structure, while the six-dimensional (6d) system, which admits a triple point, generates two distinct Widom lines. These lines partition the supercritical domain into three sectors (small-, intermediate-, and large- black hole-like phases), corresponding to the three phases coexisting at the triple point. Our results reveal a direct correspondence between the number of coexisting phases at a triple point and the number of distinct supercritical sectors separated by Widom lines.

hep-th↗

arXiv2025

Unifying and Enhancing Graph Transformers via a Hierarchical Mask Framework

Graph Transformers (GTs) have emerged as a powerful paradigm for graph representation learning due to their ability to model diverse node interactions. However, existing GTs often rely on intricate architectural designs tailored to specific interactions, limiting their flexibility. To address this, we propose a unified hierarchical mask framework that reveals an underlying equivalence between model architecture and attention mask construction. This framework enables a consistent modeling paradigm by capturing diverse interactions through carefully designed attention masks. Theoretical analysis under this framework demonstrates that the probability of correct classification positively correlates with the receptive field size and label consistency, leading to a fundamental design principle: an effective attention mask should ensure both a sufficiently large receptive field and a high level of label consistency. While no single existing mask satisfies this principle across all scenarios, our analysis reveals that hierarchical masks offer complementary strengths, motivating their effective integration. Then, we introduce M3Dphormer, a Mixture-of-Experts-based Graph Transformer with Multi-Level Masking and Dual Attention Computation. M3Dphormer incorporates three theoretically grounded hierarchical masks and employs a bi-level expert routing mechanism to adaptively integrate multi-level interaction information. To ensure scalability, we further introduce a dual attention computation scheme that dynamically switches between dense and sparse modes based on local mask sparsity. Extensive experiments across multiple benchmarks demonstrate that M3Dphormer achieves state-of-the-art performance, validating the effectiveness of our unified framework and model design.

cs.CV↗