arXiv subjects

Bin Yan

Publications and source records attributed to Bin Yan.

At least 19 recordsLinked to original sources

arXiv2026

Classical Verification of Quantum Advantage via Clifford Obfuscation

Demonstrating quantum advantage in a manner that can be independently verified by classical means remains one of the most pressing open problems in quantum computing. Here we propose a new heuristic approach for constructing quantum circuits that are hard to classically simulate yet whose output distributions can be efficiently verified classically. Our approach is based on Clifford circuit obfuscation. This obfuscation scheme hides the Clifford structure and injects a controlled and rapidly growing non-stabilizer resources that resist known classical attack strategies including reverse engineering and direct simulation. Our protocol is heuristic but is supported by both numerical and theoretical evidence. This work provides a new avenue toward classically verifiable quantum advantage that avoids the stringent implementation requirements of known approaches.

quant-ph↗

arXiv2026

Probing Gluon Linear Polarization with Dihadron Fragmentation in $\chi_b$ Decays

The dihadron fragmentation function (DiFF) of a linearly polarized gluon has not yet been accessed experimentally, leaving an important aspect of spin-dependent gluon hadronization unexplored. We show that, at leading order, the color-singlet decay channel of the $P$-wave bottomonium state $\chi_{b0}$ produces two energetic gluons with correlated linear polarizations. Within collinear factorization, their fragmentation into separate dihadron pairs generates an Artru--Collins-type angular correlation that provides the first direct probe of the linearly polarized gluon DiFF, while the corresponding semi-inclusive decay rate constrains the unpolarized gluon DiFF. A spectator-model benchmark indicates percent-level asymmetries, potentially within reach of existing Belle data. A dedicated Belle~II data set would substantially improve the statistical precision, enabling more stringent constraints on the kinematic dependence of the linearly polarized gluon DiFF.

hep-ph↗

arXiv2026

Probing the CP violation effects via the angular coefficients in Drell-Yan production

The naive-$T$-odd angular coefficients $A_5$--$A_7$ in Drell--Yan production are highly suppressed in the Standard Model (SM), making them sensitive probes of CP-violating interactions beyond the SM. We study these observables within the Standard Model Effective Field Theory (SMEFT), focusing on dimension-six electroweak and chromomagnetic quark dipole operators. Due to their chirality-flipping structure, the leading contributions from CP-violating dipole interactions arise at $\mathcal{O}(1/\Lambda^4)$ through the interference between electroweak and chromomagnetic dipole amplitudes. We find that the sensitivity is primarily driven by $A_6$ and $A_7$ in the high dilepton transverse momentum region, where the dipole contributions are enhanced, while $A_5$ remains largely insensitive. Combining the ATLAS and CMS measurements, we constrain the CP-violating dipole combinations of Wilson coefficients at the $\mathcal{O}(0.1)$ level for $\Lambda=1~{\rm TeV}$. Assuming statistically dominated uncertainties at the HL-LHC, the sensitivity can be improved to the $\mathcal{O}(10^{-3})$ level. Our results highlight $A_6$ and $A_7$ as complementary probes of CP-violating dipole interactions at hadron colliders.

hep-ph↗

arXiv2026

Attention-Only White-Box Transformer via LeJEPA-Based Self-Supervised Pretraining

Existing studies on self-supervised learning for white-box networks typically decouple the derivation of white-box networks via optimization algorithms from self-supervised learning paradigms. In this work, we instead revisit the two components from a joint perspective. The LeJEPA-based self-supervised framework assumes an isotropic Gaussian distribution as the optimal embedding distribution for downstream tasks, which is conceptually equivalent to the expansion term $R(Z)$ in the sparse rate reduction objective guiding white-box Transformer optimization. Building on this observation, we use the LeJEPA self-supervised paradigm to optimize $R(Z)$, and derive the remaining terms $R^{c}(Z\mid U_{[K]})+\lambda\lVert Z\rVert_{0}$ via the alternating direction method of multipliers (ADMM) into an attention-only Transformer that dispenses with the ISTA structure or MLP layers of the original design. Experimental results demonstrate that our attention-only white-box Transformer achieves classification accuracies of $88.88\%$ on CIFAR-10 and $63.54\%$ on CIFAR-100 at the Base scale under the LeJEPA self-supervised paradigm, while the original white-box Transformer CRATE achieves classification accuracies of $89.18\%$ on CIFAR-10 and $63.56\%$ on CIFAR-100. Our model achieves competitive performance while reducing the parameter count by roughly $31\%$. Beyond the white-box setting, we further investigate standard ViTs and find that replacing all MLP blocks with ReLU activations under knowledge distillation removes approximately 66\% of the parameters while preserving competitive accuracy, motivating further investigation into the potential redundancy of MLP modules in standard ViT architectures.

cs.LG↗

arXiv2026

Quantum Information of Photon Pairs at Lepton Colliders

Photon pairs have provided an ideal laboratory for exploring entanglement and Bell inequality violation in low-energy experiments. Extending such studies to high-energy colliders is of great interest but has yet to be explored. Exploiting the photon conversion process for nearly on-shell photons, we formulate a factorization framework and an effective two-qubit description, which enable access to quantum information encoded in photon pairs. Using the existing Belle data set, we estimate that a $7.4\sigma$ violation of the Bell inequality could be achieved. The same framework can also probe quantum discord and nonstabilizerness, which could be measured with precisions of 5.6\% and 1.6\%, respectively. All the reconstructed results from photon conversion in the two-qubit framework are found to be consistent with the kinematic approach of real photons, and the formalism can apply to other spin-1 systems in an appropriate two-qubit limit.

hep-ph↗

arXiv2026

PhysGuard: Fisher-Guided Gradient Projection for Sim-to-Real Neural PDE Surrogates

Neural operator models trained on simulation data often lose accuracy when applied to experimental measurements due to the sim-to-real gap. Standard fine-tuning with limited real data can reduce this gap, but it may also damage the core physics-relevant representations learned during pretraining. Although knowledge-preserving adaptation has been widely investigated in vision or language tasks, it remains unclear whether these methods are suitable for neural operators whose architectures and protected knowledge are fundamentally different. Neural operators need to preserve core-scale physical structures rather than semantic or visual features. We propose PhysGuard, a physics-preserving framework for accurate sim-to-real adaptation of neural operators. Specifically, PhysGuard uses the empirical Fisher Information Matrix computed on simulation data to identify physics-critical parameter directions, then restricts fine-tuning updates to directions that do not interfere with them. A layer-wise Gram-matrix formulation makes this efficient for models with millions of parameters, while an adaptive threshold automatically determines the protected subspace size. A spectral probe experiment shows that the dominant Fisher directions are strongly associated with low-frequency output structures. Experiments on benchmark across four neural operator architectures and different physical systems show that PhysGuard performs strongly on most evaluation metrics compared to baselines. The benefits are most evident under severe domain shift, where it reduces low-frequency error by up to 32\% compared to standard fine-tuning while maintaining adaptability. Our code is available at https://github.com/ZhouChaunge/PhysGuard.

cs.LG↗

arXiv2026

oxo-call: Documentation-grounded Skill Augmentation for Accurate Bioinformatics Command-line Generation with Large Language Models

Command-line bioinformatics tools remain essential for genomic analysis, yet their diversity in syntax and parameterization presents a persistent barrier to productive research. We present oxo-call, a Rust-based command-line assistant that translates natural-language task descriptions into accurate tool invocations through two complementary strategies: documentation-first grounding, which provides the large language model (LLM) with the complete, version-specific help text of each target tool, and curated skill augmentation, which primes the model with domain-expert concepts, common pitfalls, and worked examples. oxo-call (v0.10) ships >150 built-in skills covering 44 analytical categories, from variant calling and genome assembly to single-cell transcriptomics, compiled into a single, statically linked binary. Every generated command is logged with provenance metadata to support reproducible research. oxo-call also provides a DAG-based workflow engine, extensibility through user-defined and community skills via the Model Context Protocol, and support for local LLM inference to address data-privacy requirements. oxo-call is freely available for academic use at https://traitome.github.io/oxo-call/.

q-bio.GN↗

arXiv2026

POSIM: A Multi-Agent Simulation Framework for Social Media Public Opinion Evolution and Governance

Modeling social media public opinion evolution is essential for governance decision-making. Traditional epidemic models and rule-based agent-based models (ABMs) fail to capture the cognitive processes and adaptive behaviors of real users. Recent large language model (LLM)-based social simulations can reproduce group-level phenomena like polarization and conformity, yet remain unable to recreate the irrational interactions and multi-phase dynamics of real public opinion events. We present POSIM (Public Opinion Simulator), a multi-agent simulation framework for social media public opinion evolution and governance. POSIM integrates LLM-driven agents with a Belief--Desire--Intention (BDI) cognitive architecture that accounts for irrational factors, places them in a virtual social media environment with social networks and recommendation mechanisms, and drives temporal dynamics through a Hawkes point process engine that captures the co-evolution of agents and the environment across event phases. To validate the framework, we collect real-world public opinion datasets from the Weibo platform covering the full interaction chain of users. Experiments show that POSIM successfully reproduces key characteristics of public opinion evolution from individual mechanisms to collective phenomena, and its effectiveness is further supported by multiple statistical metrics. Building on POSIM, governance-oriented guidance and intervention experiments uncover a counterintuitive empathy paradox: empathetic guidance deepens negative sentiment instead of easing it under certain conditions, offering new insights for governance strategy design. These results demonstrate that the proposed framework can fully serve as a computational experimentation platform for proactive strategy evaluation and evidence-based governance. All source code is available at https://github.com/DeepCogLab/posim/.

cs.GL↗

arXiv2026

Probing the Color-Octet Mechanism via Dihadron Fragmentation in $\chi_b$ Decays

The color-octet (CO) mechanism is a cornerstone of non-relativistic QCD, yet its long-distance matrix elements remain limited, preventing stringent tests of the theory. We demonstrate that the Artru-Collins asymmetry in hadronic decays of the $P$-wave bottomonium state $\chi_{b2}$ provides a direct probe of CO dynamics. The asymmetry arises exclusively from the CO decay channel, whereas the color-singlet (CS) contribution affects only the unpolarized rate, so that a nonzero signal constitutes unambiguous evidence of the CO mechanism. This observable provides a novel way to extract the ratio $\rho_8$ between CO and CS matrix elements. Focusing on $e^+e^-\to\Upsilon(2S)\to\gamma\,\chi_{b2}$ at Belle, we show that the asymmetric beam configuration preserves the asymmetry in the laboratory frame and avoids the strong suppression present in the center-of-mass frame. With the Belle II dataset, $\rho_8$ could be determined with sufficient precision to address the long-standing discrepancy between the lattice calculations and phenomenological determinations.

hep-ph↗

arXiv2026

Transverse spin effects and light-quark dipole moments at colliders

In this talk, we present novel methods to investigate light-quark dipole interactions at colliders. Our approach includes: (1) measuring azimuthal asymmetries of a collinear dihadron in semi-inclusive deep inelastic lepton scattering off an unpolarized proton target at the Electron-Ion Collider, and (2) utilizing azimuthal asymmetries of dihadron $(h_1 h_2)$ produced in association with an additional hadron $h^\prime$ at lepton colliders. These asymmetries provide a unique means to observe transversely polarized quarks, which arise from quantum interference and are exclusively sensitive to dipole interactions at the leading power of the new physics scale. Consequently, they exhibit a linear dependence on the dipole couplings, free from contamination by other new physics effects. This approach has the potential to significantly strengthen current constraints by one to two orders of magnitude. By combining all possible channels of $h^\prime$, this novel approach enables the disentanglement of the up- and down-quark dipole moments. Additionally, by controlling the electron's longitudinal polarization and the center-of-mass energy, it separates the contributions mediated by photon and weak boson. Furthermore, it allows for a simultaneous determination of both real and imaginary parts of the dipole couplings, offering a new avenue for investigating potential $CP$-violating effects at high energies.

hep-ph↗

arXiv2026

A Quantization-Aware Training Based Lightweight Method for Neural Distinguishers

In 2019, Gohr pioneered the application of deep neural networks to differential cryptanalysis, developing DNN-based neural distinguisher classifiers to analyze the SPECK lightweight block cipher. Unlike traditional differential analysis, which relies on Boolean operations on 0-1 sequences, neural distinguishers extract continuous features, introducing 32-bit multiplications operations that increase complexity and potential redundancy. This study proposes a lightweight neural distinguisher based on quantization-aware training. Leveraging learnable step-size quantization, the model's weights are quantized to 1.58 bits, enabling the replacement of all convolutional multiplication operations with Boolean logic. Additionally, the ReLU activation function is reimplemented as a comparison-based indicator function. This transforms the original 32-bit multiplication-dependent architecture into a lightweight structure composed solely of Boolean operations, additions, and indicator functions. Experimental results confirm significant computational complexity reduction. Owing to a high proportion of zero-valued weights, the total operations amount to just 13.9% of Gohr's model. Critically, the most costly 32-bit multiplications are eliminated, with classification accuracy dropping by only 2.87%. When applied exclusively to the initial convolutional layer, the 128 1-by-1 convolutions are replaced with 4 Boolean operations on 16-bit sequences, incurring a negligible 0.3% accuracy loss.

cs.CR↗

arXiv2026

Interpretable and Sparse Linear Attention with Decoupled Membership-Subspace Modeling via MCR2 Objective

Maximal Coding Rate Reduction (MCR2)-driven white-box transformer, grounded in structured representation learning, unifies interpretability and efficiency, providing a reliable white-box solution for visual modeling. However, in existing designs, tight coupling between "membership matrix" and "subspace matrix U" in MCR2 causes redundant coding under incorrect token projection. To this end, we decouple the functional relationship between the "membership matrix" and "subspaces U" in the MCR2 objective and derive an interpretable sparse linear attention operator from unrolled gradient descent of the optimized objective. Specifically, we propose to directly learn the membership matrix from inputs and subsequently derive sparse subspaces from the fullspace S. Consequently, gradient unrolling of the optimized MCR2 objective yields an interpretable sparse linear attention operator: Decoupled Membership-Subspace Attention (DMSA). Experimental results on visual tasks show that simply replacing the attention module in Token Statistics Transformer (ToST) with DMSA (we refer to as DMST) not only achieves a faster coding reduction rate but also outperforms ToST by 1.08%-1.45% in top-1 accuracy on the ImageNet-1K dataset. Compared with vanilla Transformer architectures, DMST exhibits significantly higher computational efficiency and interpretability.

cs.CV↗

arXiv2026

A one-step generation model with a Single-Layer Transformer: Layer number re-distillation of FreeFlow

Currently, Flow matching methods aim to compress the iterative generation process of diffusion models into a few or even a single step, with MeanFlow and FreeFlow being representative achievements of one-step generation based on Ordinary Differential Equations (ODEs). We observe that the 28-layer Transformer architecture of FreeFlow can be characterized as an Euler discretization scheme for an ODE along the depth axis, where the layer index serves as the discrete time step. Therefore, we distill the number of layers of the FreeFlow model, following the same derivation logic as FreeFlow, and propose SLT (Single-Layer Transformer), which uses a single shared DiT block to approximate the depth-wise feature evolution of the 28-layer teacher. During training, it matches the teacher's intermediate features at several depth patches, fuses those patch-level representations, and simultaneously aligns the teacher's final velocity prediction. Through distillation training, we compress the 28 independent Transformer Blocks of the teacher model DiT-XL/2 into a single Transformer Block, reducing the parameter count from 675M to 4.3M. Furthermore, leveraging its minimal parameters and rapid sampling speed, SLT can screen more candidate points in the noise space within the same timeframe, thereby selecting higher-quality initial points for the teacher model FreeFlow and ultimately enhancing the quality of generated images. Experimental results demonstrate that within a time budget comparable to two random samplings of the teacher model, our method performs over 100 noise screenings and produces a high-quality sample through the teacher model using the selected points. Quality fluctuations caused by low-quality initial noise under a limited number of FreeFlow sampling calls are effectively avoided, substantially improving the stability and average generation quality of one-step generation.

cs.CV↗

arXiv2025

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders

Directly probing light-quark Yukawa couplings and their flavor structure remains a major challenge due to their smallness and overwhelming QCD backgrounds. In this Letter, we propose a theoretical framework to access these couplings at lepton colliders through transverse spin dependent azimuthal modulations in dihadron fragmentation. These modulations arise from the interference between Higgs mediated and standard model amplitudes in $e^-e^+\to q\bar{q}Z$, producing angular structures that are linearly sensitive to the Yukawa couplings $y_q$, in contrast to conventional observables that scale as $y_q^2$. By combining channels with an identified accompanying single hadron, $h^\prime=\pi^\pm,K^\pm$, and $p/\bar{p}$, this approach cleanly disentangles the up- and down-quark Yukawa contributions, yielding typical limits at the $\mathcal{O}(10^{-4}\sim 10^{-3})$ level and establishing fragmentation dynamics as a novel and complementary probe of the Higgs flavor structure.

hep-ph↗

arXiv2025

Multi-Text Guided Few-Shot Semantic Segmentation

Recent CLIP-based few-shot semantic segmentation methods introduce class-level textual priors to assist segmentation by typically using a single prompt (e.g., a photo of class). However, these approaches often result in incomplete activation of target regions, as a single textual description cannot fully capture the semantic diversity of complex categories. Moreover, they lack explicit cross-modal interaction and are vulnerable to noisy support features, further degrading visual prior quality. To address these issues, we propose the Multi-Text Guided Few-Shot Semantic Segmentation Network (MTGNet), a dual-branch framework that enhances segmentation performance by fusing diverse textual prompts to refine textual priors and guide the cross-modal optimization of visual priors. Specifically, we design a Multi-Textual Prior Refinement (MTPR) module that suppresses interference and aggregates complementary semantic cues to enhance foreground activation and expand semantic coverage for structurally complex objects. We introduce a Text Anchor Feature Fusion (TAFF) module, which leverages multi-text embeddings as semantic anchors to facilitate the transfer of discriminative local prototypes from support images to query images, thereby improving semantic consistency and alleviating intra-class variations. Furthermore, a Foreground Confidence-Weighted Attention (FCWA) module is presented to enhance visual prior robustness by leveraging internal self-similarity within support foreground features. It adaptively down-weights inconsistent regions and effectively suppresses interference in the query segmentation process. Extensive experiments on standard FSS benchmarks validate the effectiveness of MTGNet. In the 1-shot setting, it achieves 76.8% mIoU on PASCAL-5i and 57.4% on COCO-20i, with notable improvements in folds exhibiting high intra-class variations.

cs.CV↗

arXiv2025

MFI-ResNet: Efficient ResNet Architecture Optimization via MeanFlow Compression and Selective Incubation

ResNet has achieved tremendous success in computer vision through its residual connection mechanism. ResNet can be viewed as a discretized form of ordinary differential equations (ODEs). From this perspective, the multiple residual blocks within a single ResNet stage essentially perform multi-step discrete iterations of the feature transformation for that stage. The recently proposed flow matching model, MeanFlow, enables one-step generative modeling by learning the mean velocity field to transform distributions. Inspired by this, we propose MeanFlow-Incubated ResNet (MFI-ResNet), which employs a compression-expansion strategy to jointly improve parameter efficiency and discriminative performance. In the compression phase, we simplify the multi-layer structure within each ResNet stage to one or two MeanFlow modules to construct a lightweight meta model. In the expansion phase, we apply a selective incubation strategy to the first three stages, expanding them to match the residual block configuration of the baseline ResNet model, while keeping the last stage in MeanFlow form, and fine-tune the incubated model. Experimental results show that on CIFAR-10 and CIFAR-100 datasets, MFI-ResNet achieves remarkable parameter efficiency, reducing parameters by 46.28% and 45.59% compared to ResNet-50, while still improving accuracy by 0.23% and 0.17%, respectively. This demonstrates that generative flow-fields can effectively characterize the feature transformation process in ResNet, providing a new perspective for understanding the relationship between generative modeling and discriminative learning.

cs.CV↗

arXiv2025

InfinityStar: Unified Spacetime AutoRegressive Modeling for Visual Generation

We introduce InfinityStar, a unified spacetime autoregressive framework for high-resolution image and dynamic video synthesis. Building on the recent success of autoregressive modeling in both vision and language, our purely discrete approach jointly captures spatial and temporal dependencies within a single architecture. This unified design naturally supports a variety of generation tasks such as text-to-image, text-to-video, image-to-video, and long interactive video synthesis via straightforward temporal autoregression. Extensive experiments demonstrate that InfinityStar scores 83.74 on VBench, outperforming all autoregressive models by large margins, even surpassing some diffusion competitors like HunyuanVideo. Without extra optimizations, our model generates a 5s, 720p video approximately 10x faster than leading diffusion-based methods. To our knowledge, InfinityStar is the first discrete autoregressive video generator capable of producing industrial level 720p videos. We release all code and models to foster further research in efficient, high-quality video generation.

cs.CV↗

arXiv2025

Probing Quark Electromagnetic Properties via Entangled Quark Pairs in Fragmentation Hadrons at Lepton Colliders

Electromagnetic dipole interactions of light quarks induce distinct spin correlations in quark pairs produced at lepton colliders, favoring entangled spin-triplet state aligned along the $\hat{z}$ axis or spin-singlet state. These correlations lead to unique $\cos(\phi_1-\phi_2)$ azimuthal asymmetries in inclusive $\pi^+\pi^-$-dihadron pair production and in back-to-back hadron pairs ($\pi\pi,K\pi,KK$), which are absent in the SM. Using published Belle and BaBar measurements together with projected sensitivities based on ratios of azimuthal asymmetries, we demonstrate that these measurements provide significant constraints on light-quark dipole couplings, with a reduced dependence on poorly known nonperturbative fragmentation functions and free from contamination by other new physics effects. This approach offers a clean and novel probe of light-quark dipole interactions in collider experiments.

hep-ph↗