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Bin Liu

Publications and source records attributed to Bin Liu.

At least 91 records · Page 5Linked to original sources
arXiv2026

Global well-posedness and scattering for defocusing energy-critical inhomogeneous NLS in dimensions $d\ge 3$

We study the defocusing energy-critical inhomogeneous nonlinear Schrödinger equation \[ i\partial_tu+Δu=|x|^{-b}|u|^{\frac{4-2b}{d-2}}u, \qquad (t,x)\in\R\times\R^d, \] with initial data $u_0\in\dot H_x^1(\R^d)$, where $d\ge 3$ and $0<b<\min\{2,\frac d2\}$. We prove global well-posedness and scattering for arbitrary non-radial data. The main difficulties are that, when $d\ge 6$, the derivative of the critical nonlinearity is only Hölder continuous, so the short-time perturbation argument cannot be closed in $\dot S^1$, and that the singular coefficient $|x|^{-b}$ breaks translation symmetry. To handle these issues, we exploit the weak-space structure $|x|^{-b}\in L^{\frac{d}{b},\infty}(\R^d)$, introduce exotic Strichartz norms, and prove a long-time stability theorem for the general energy-critical inhomogeneous nonlinear Schrödinger equation. We also show that profiles escaping to spatial infinity are asymptotically linear because of the decay of $|x|^{-b}$. Consequently, almost periodic solutions are compact modulo scaling only, with neither spatial nor frequency center parameters. Combined with the concentration--compactness argument of Kenig--Merle [\emph{Invent. Math.} \textbf{166} (2006), 645--675], this yields the main theorem.

math.AP↗
arXiv2026

Racing to Release: Priority, Congestion, and Community Recognition in Open-Source LLM Ecosystems

Open-source large language models have made platforms such as Hugging Face central hubs for decentralized AI innovation. Yet these ecosystems are shaped not only by collaboration, but also by competition for priority and community attention. Drawing on Hill and Stein's Race-to-the-Bottom framework, this study extends the logic of project potential, maturation, competition, and quality from scientific production to open-source LLM ecosystems, where prominent base models attract concentrated derivative entry under rapid and highly visible platform feedback. Using a large-scale sample of derivative models on Hugging Face, we find that later releases and more crowded competitive environments are both associated with weaker community recognition, even after accounting for differences in model and ecosystem prominence. These findings suggest that competition for priority remains an important organizing force in open-source LLM ecosystems, shaping which derivative innovations receive community recognition.

cs.SI↗
arXiv2026

Hidden Elo: Private Matchmaking through Encrypted Rating Systems

Matchmaking has become a prevalent part in contemporary applications, being used in dating apps, social media, online games, contact tracing and in various other use-cases. However, most implementations of matchmaking require the collection of sensitive/personal data for proper functionality. As such, with this work we aim to reduce the privacy leakage inherent in matchmaking applications. We propose H-Elo, a Fully Homomorphic Encryption (FHE)-based, private rating system, which allows for secure matchmaking through the use of traditional rating systems. In this work, we provide the construction of H-Elo, analyse the security of it against a capable adversary as well as benchmark our construction in a chess-based rating update scenario. Through our experiments we show that H-Elo can achieve similar accuracy to a plaintext implementation, while keeping rating values private and secure. Additionally, we compare our work to other private matchmaking solutions as well as cover some future directions in the field of private matchmaking. To the best of our knowledge we provide one of the first private and secure rating system-based matchmaking protocols.

cs.CR↗
arXiv2026

To understand the radiative processes of pulsars and fast radio bursts with the FAST

The radiative mechanism of coherent radio emission has remained an enigma since the discovery of pulsars, even the emergence of fast radio bursts (FRBs), which exhibit similarities to the single-pulse behavior of pulsars and have opened a new view for deciphering the long-standing mystery. Besides tremendous efforts in modelling, advanced facilities matter for solving the problem. The authors review the observational breakthroughs from the Five-hundred-meter Aperture Spherical radio Telescope (FAST), which are providing pivotal insights to unravel the underlying physics of pulsars and FRBs. This study offers a novel perspective in the era when pulsars meet FRBs, and further investigations are encouraged to utilize the highly sensitive telescope, the FAST.

astro-ph.HE↗
arXiv2026

On the origin of superlattice stacking faults nucleation via climb of Frank partial in CoNi-based superalloys

High-temperature deformation in superalloys is governed by the cooperative glide-climb motion of dislocations. Superlattice stacking faults (SFs) in the gamma prime phase are predominantly interpreted as nucleating via conservative Shockley partial glide. Here, we demonstrate that non-conservative climb of a/3<111> Frank partials constitutes a general and kinetically viable pathway for both superlattice intrinsic (SISFs) and extrinsic stacking faults (SESFs) formation in the L12 structure of CoNi-based superalloys during compression at 850 Celsius. High-resolution transmission electron microscopy reveals that Frank partials form at gamma/gamma prime interface can climb into the gamma prime phase, generating SISFs via positive climb and SESFs via negative climb. Importantly, the negative climb-assisted nucleation of SESFs is experimentally confirmed for the first time, and the observed positive climb-assisted SISF configuration differs fundamentally from previously reported mechanisms. We show that these Frank partials originate from the reaction between a leading 30 degree Shockley partial and a 60 degree mixed dislocation on conjugate {111} planes, producing energetically stable configurations that promote subsequent climb. Energetic and kinetic analyses demonstrate that solute segregation induced reduction of SF energy provides a dominant contribution to Frank partial climb, enabling sustained climb and consequent SF expansion. Quantitative comparisons further indicate that, at elevated temperatures, solute drag-controlled Shockley glide can achieve mobilities comparable to vacancy diffusion-controlled Frank climb. These findings establish climb-assisted SF formation as a unified deformation mechanism in gamma prime phase, and that both SISF and SESF expansion can proceed through Frank partial climb.

cond-mat.mtrl-sci↗
arXiv2026

Dual-level Modality Debiasing Learning for Unsupervised Visible-Infrared Person Re-Identification

Two-stage learning pipeline has achieved promising results in unsupervised visible-infrared person re-identification (USL-VI-ReID). It first performs single-modality learning and then operates cross-modality learning to tackle the modality discrepancy. Although promising, this pipeline inevitably introduces modality bias: modality-specific cues learned in the single-modality training naturally propagate into the following cross-modality learning, impairing identity discrimination and generalization. To address this issue, we propose a Dual-level Modality Debiasing Learning (DMDL) framework that implements debiasing at both the model and optimization levels. At the model level, we propose a Causality-inspired Adjustment Intervention (CAI) module that replaces likelihood-based modeling with causal modeling, preventing modality-induced spurious patterns from being introduced, leading to a low-biased model. At the optimization level, a Collaborative Bias-free Training (CBT) strategy is introduced to interrupt the propagation of modality bias across data, labels, and features by integrating modality-specific augmentation, label refinement, and feature alignment. Extensive experiments on benchmark datasets demonstrate that DMDL could enable modality-invariant feature learning and a more generalized model. The code is available at https://github.com/priester3/DMDL.

cs.CV↗
arXiv2026

Intercity mobility reveals the hyperbolic geometry of city systems

The hierarchy and proximity are key dimensions of urban relational processes, but their interplay in shaping intercity interactions and the underlying structures of city systems remain unclear. We develop a novel geometric model of city systems embedding intercity mobility into a latent hyperbolic geometry, which unravels the measures of hierarchy and proximity accounting for their interplay. It is successfully validated against 12 different nationwide intercity mobility datasets. We find a bottom-up emergence of city hierarchies, along which the variations of city-hinterland relations are non-stationary in terms of their nesting and range properties. Such non-stationarity originates from trade-offs between city hierarchy and hinterland range in determining the formation of city-hinterland structures. Hierarchy- and proximity-dominated urban processes can be elucidated from examining dynamics of the trade-offs. The revealed urban relational processes of city systems are at the core of the emerging science of cities and crucial for spatial planning and regional policymaking.

physics.soc-ph↗
arXiv2026

Variational Bayesian Personalized Ranking

Pairwise learning underpins implicit collaborative filtering, yet its effectiveness is often hindered by sparse supervision, noisy interactions, and popularity-driven exposure bias. In this paper, we propose Variational Bayesian Personalized Ranking (VarBPR), a tractable variational framework for implicit-feedback pairwise learning that offers principled exposure controllability and theoretical interpretability. VarBPR reformulates pairwise learning as variational inference over discrete latent indexing variables, explicitly modeling noise and indexing uncertainty, and divides training into two stages: variational inference and variational learning. In the variational inference stage, we develop a variational formulation that integrates preference alignment, denoising, and popularity debiasing under a unified ELBO/regularization objective, deriving closed-form posteriors with clear control semantics: the prior encodes a target exposure pattern, while temperature/regularization strength controls posterior-prior adherence. As a result, exposure controllability becomes an endogenous and interpretable outcome of variational inference. In the variational learning stage, we propose a posterior-compression objective that reduces the ideal ELBO's computational complexity from polynomial to linear, with the approximation justified by an explicit Jensen-gap upper bound. Theoretically, we provide interpretable generalization guarantees by identifying a structural error component and revealing the opportunity cost of prioritizing certain exposure patterns (e.g., long-tail), offering a concrete analytical lens for designing controllable recommender systems. Empirically, We validate VarBPR across popular backbones; it demonstrates consistent gains in ranking accuracy, enables controlled long-tail exposure, and preserves the linear-time complexity of BPR.

cs.IR↗
arXiv2026

EchoGen: Cycle-Consistent Learning for Unified Layout-Image Generation and Understanding

In this work, we present EchoGen, a unified framework for layout-to-image generation and image grounding, capable of generating images with accurate layouts and high fidelity to text descriptions (e.g., spatial relationships), while grounding the image robustly at the same time. We believe that image grounding possesses strong text and layout understanding abilities, which can compensate for the corresponding limitations in layout-to-image generation. At the same time, images generated from layouts exhibit high diversity in content, thereby enhancing the robustness of image grounding. Jointly training both tasks within a unified model can promote performance improvements for each. However, we identify that this joint training paradigm encounters several optimization challenges and results in restricted performance. To address these issues, we propose progressive training strategies. First, the Parallel Multi-Task Pre-training (PMTP) stage equips the model with basic abilities for both tasks, leveraging shared tokens to accelerate training. Next, the Dual Joint Optimization (DJO) stage exploits task duality to sequentially integrate the two tasks, enabling unified optimization. Finally, the Cycle RL stage eliminates reliance on visual supervision by using consistency constraints as rewards, significantly enhancing the model's unified capabilities via the GRPO strategy. Extensive experiments demonstrate state-of-the-art results on both layout-to-image generation and image grounding benchmarks, and reveal clear synergistic gains from optimizing the two tasks together.

cs.CV↗
arXiv2026

Multimodal Emotion Regression with Multi-Objective Optimization and VAD-Aware Audio Modeling for the 10th ABAW EMI Track

We participated in the 10th ABAW Challenge, focusing on the Emotional Mimicry Intensity (EMI) Estimation track on the Hume-Vidmimic2 dataset. This task aims to predict six continuous emotion dimensions: Admiration, Amusement, Determination, Empathic Pain, Excitement, and Joy. Through systematic multimodal exploration of pretrained high-level features, we found that, under our pretrained feature setting, direct feature concatenation outperformed the more complex fusion strategies we tested. This empirical finding motivated us to design a systematic approach built upon three core principles: (i) preserving modality-specific attributes through feature-level concatenation; (ii) improving training stability and metric alignment via multi-objective optimization; and (iii) enriching acoustic representations with a VAD-inspired latent prior. Our final framework integrates concatenation-based multimodal fusion, a shared six-dimensional regression head, multi-objective optimization with MSE, Pearson-correlation, and auxiliary branch supervision, EMA for parameter stabilization, and a VAD-inspired latent prior for the acoustic branch. On the official validation set, the proposed scheme achieved our best mean Pearson Correlation Coefficient of 0.478567.

cs.AI↗
arXiv2026

HiAR: Efficient Autoregressive Long Video Generation via Hierarchical Denoising

Autoregressive (AR) diffusion offers a promising framework for generating videos of theoretically infinite length. However, a major challenge is maintaining temporal continuity while preventing the progressive quality degradation caused by error accumulation. To ensure continuity, existing methods typically condition on highly denoised contexts; yet, this practice propagates prediction errors with high certainty, thereby exacerbating degradation. In this paper, we argue that a highly clean context is unnecessary. Drawing inspiration from bidirectional diffusion models, which denoise frames at a shared noise level while maintaining coherence, we propose that conditioning on context at the same noise level as the current block provides sufficient signal for temporal consistency while effectively mitigating error propagation. Building on this insight, we propose HiAR, a hierarchical denoising framework that reverses the conventional generation order: instead of completing each block sequentially, it performs causal generation across all blocks at every denoising step, so that each block is always conditioned on context at the same noise level. This hierarchy naturally admits pipelined parallel inference, yielding a 1.8 wall-clock speedup in our 4-step setting. We further observe that self-rollout distillation under this paradigm amplifies a low-motion shortcut inherent to the mode-seeking reverse-KL objective. To counteract this, we introduce a forward-KL regulariser in bidirectional-attention mode, which preserves motion diversity for causal inference without interfering with the distillation loss. On VBench (20s generation), HiAR achieves the best overall score and the lowest temporal drift among all compared methods.

cs.CV↗
arXiv2026

Incomplete Multi-Label Image Recognition by Co-learning Semantic-Aware Features and Label Recovery

Multi-label image recognition with incomplete labels is a challenging yet vital task in computer vision, which faces two fundamental challenges: learning semantic-aware features and recovering missing labels. In this paper, we propose a Co-learning framework for Semantic-aware features and Label recovery (CSL), designed to address both challenges in a unified learning paradigm. Specifically, we develop a semantic-related feature learning module that captures robust semantic-related representations by discovering semantic information and label correlations. Furthermore, a semantic-guided feature enhancement module is introduced to generate highly discriminative semantic-aware features by effectively aligning visual and semantic spaces. Finally, we present a collaborative learning framework that integrates semantic-aware feature learning with label recovery. This framework not only dynamically enhances the discriminability of semantic-aware features but also adaptively infers and recovers missing labels, thereby forming a mutually reinforcing mechanism between the two processes. Extensive experiments on three widely used public datasets (MS-COCO, VOC2007, and NUS-WIDE) demonstrate that CSL outperforms state-of-the-art methods for incomplete multi-label image recognition.

cs.CV↗
arXiv2026

Intrinsic Spin Filter Effect in a $d$-wave altermagnet KV$_2$Se$_2$O with Open Fermi Surface

Altermagnets offer a unique pathway to functional spintronics by combining vanishing magnetization with large spin splitting. Here, we demonstrate that the canonical d-wave altermagnet KV2Se2O can deliver giant tunneling magnetoresistance through orientation-dependent spin filtering. By analyzing the crystallographic spin segregation, we show that transport along specific crystallographic axes is nearly fully spin-polarized within the symmetry-protected ballistic channels. We implement this mechanism in a lattice-matched KV2Se2O/Bi2O2Se/KV2Se2O magnetic tunnel junction, which achieves a robust half-metallic transport regime. The symmetry-protected spectral gap in the parallel/anti-parallel configuration ensures a high tunneling magnetoresistance ratio, resulting in substantial tunneling magnetoresistance, robust thermally driven spin filtering, and spin Seebeck effect at room temperature. These findings provide a path of altermagnetic heterostructures as a high-performance platform for scalable, field-free, and thermally stable spin logic.

cond-mat.mtrl-sci↗
arXiv2026

Pixel2Phys: Distilling Governing Laws from Visual Dynamics

Discovering physical laws directly from high-dimensional visual data is a long-standing human pursuit but remains a formidable challenge for machines, representing a fundamental goal of scientific intelligence. This task is inherently difficult because physical knowledge is low-dimensional and structured, whereas raw video observations are high-dimensional and redundant, with most pixels carrying little or no physical meaning. Extracting concise, physically relevant variables from such noisy data remains a key obstacle. To address this, we propose Pixel2Phys, a collaborative multi-agent framework adaptable to any Multimodal Large Language Model (MLLM). It emulates human scientific reasoning by employing a structured workflow to extract formalized physical knowledge through iterative hypothesis generation, validation, and refinement. By repeatedly formulating, and refining candidate equations on high-dimensional data, it identifies the most concise representations that best capture the underlying physical evolution. This automated exploration mimics the iterative workflow of human scientists, enabling AI to reveal interpretable governing equations directly from raw observations. Across diverse simulated and real-world physics videos, Pixel2Phys discovers accurate, interpretable governing equations and maintaining stable long-term extrapolation where baselines rapidly diverge.

cs.CE↗
arXiv2026

SiGRRW: A Single-Watermark Robust Reversible Watermarking Framework with Guiding Strategy

Robust reversible watermarking (RRW) enables copyright protection for images while overcoming the limitation of distortion introduced by watermark itself. Current RRW schemes typically employ a two-stage framework, which fails to achieve simultaneous robustness and reversibility within a single watermarking, and functional interference between the two watermarks results in performance degradation in multiple terms such as capacity and imperceptibility. We propose SiGRRW, a single-watermark RRW framework, which is applicable to both generative models and natural images. We introduce a novel guiding strategy to generate guiding images, serving as the guidance for embedding and recovery. The watermark is reversibly embedded with the guiding residual, which can be calculated from both cover images and watermark images. The proposed framework can be deployed either as a plug-and-play watermarking layer at the output stage of generative models, or directly applied to natural images. Extensive experiments demonstrate that SiGRRW effectively enhances imperceptibility and robustness compared to existing RRW schemes while maintaining lossless recovery of cover images, with significantly higher capacity than conventional schemes.

cs.CR↗
arXiv2026

LAKAN: Landmark-assisted Adaptive Kolmogorov-Arnold Network for Face Forgery Detection

The rapid development of deepfake generation techniques necessitates robust face forgery detection algorithms. While methods based on Convolutional Neural Networks (CNNs) and Transformers are effective, there is still room for improvement in modeling the highly complex and non-linear nature of forgery artifacts. To address this issue, we propose a novel detection method based on the Kolmogorov-Arnold Network (KAN). By replacing fixed activation functions with learnable splines, our KAN-based approach is better suited to this challenge. Furthermore, to guide the network's focus towards critical facial areas, we introduce a Landmark-assisted Adaptive Kolmogorov-Arnold Network (LAKAN) module. This module uses facial landmarks as a structural prior to dynamically generate the internal parameters of the KAN, creating an instance-specific signal that steers a general-purpose image encoder towards the most informative facial regions with artifacts. This core innovation creates a powerful combination between geometric priors and the network's learning process. Extensive experiments on multiple public datasets show that our proposed method achieves superior performance.

cs.CV↗
arXiv2026

Resonant Excitation of Surface Plasmon for Wakefield Acceleration by Beating GW Lasers on Smooth Cylindrical Surface

We present a theoretical and numerical study of resonant surface-plasmon (SP) excitation driven by the beating of two co-propagating laser pulses on a smooth cylindrical plasma-vacuum interface. Analytical expressions for the SP dispersion relation, field amplitude, geometric coupling factor, and resonance conditions are derived and validated by fully three-dimensional particle-in-cell simulations. We reveal that curvature-induced geometric effects can substantially modify the SP dispersion and enable resonant matching by laser beat waves. This is inaccessible in planar geometries or with a single laser. Under matched resonance conditions, a high-amplitude SP-based wakefield can be generated by a few gigawatt lasers, placing this mechanism within reach of state-of-the-art fibre lasers. It therefore opens a route toward portable laser-driven plasma wakefield accelerators.

physics.plasm-ph↗
arXiv2026

A Dual-Loop Agent Framework for Automated Vulnerability Reproduction

Automated vulnerability reproduction from CVE descriptions requires generating executable Proof-of-Concept (PoC) exploits and validating them in target environments. This process is critical in software security research and practice, yet remains time-consuming and demands specialized expertise when performed manually. While LLM agents show promise for automating this task, existing approaches often conflate exploring attack directions with fixing implementation details, which leads to unproductive debugging loops when reproduction fails. To address this, we propose CVE2PoC, an LLM-based dual-loop agent framework following a plan-execute-evaluate paradigm. The Strategic Planner analyzes vulnerability semantics and target code to produce structured attack plans. The Tactical Executor generates PoC code and validates it through progressive verification. The Adaptive Refiner evaluates execution results and routes failures to different loops: the Tactical Loop for code-level refinement, while the Strategic Loop for attack strategy replanning. This dual-loop design enables the framework to escape ineffective debugging by matching remediation to failure type. Evaluation on two benchmarks covering 617 real-world vulnerabilities demonstrates that CVE2PoC achieves 82.9% and 54.3% reproduction success rates on SecBench.js and PatchEval, respectively, outperforming the best baseline by 11.3% and 20.4%. Human evaluation confirms that generated PoCs achieve comparable code quality to human-written exploits in readability and reusability.

cs.SE↗