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Jun Yang

Publications and source records attributed to Jun Yang.

At least 19 recordsLinked to original sources
arXiv2026

Harmonic brightness oscillations point to magnetar giant flares in rare short gamma-ray bursts

Kilohertz quasi-periodic oscillations (QPOs) have been observed in a few rare gamma-ray bursts (GRBs), but their physical origin remains uncertain. We reanalyze the two short GRBs with reported kilohertz QPOs, GRB 910711 and GRB 200415A, and find evidence for possible harmonic-like organization around a characteristic frequency scale near 360 hertz. The similar harmonic-like pattern in GRB 910711 and in the confirmed magnetar giant flare (MGF) event GRB 200415A, including features extending above 4 kilohertz, points to timing phenomenology on frequency scales relevant to magnetar oscillation modes. The temporal and spectral similarities between the two events and the difficulty of explaining these features in merger-driven scenarios favor an MGF-like interpretation of GRB 910711. This timing phenomenology may therefore provide an additional observational handle, complementary to spectral, temporal, and localization information, for distinguishing rare MGF-like transients from merger-driven GRBs.

astro-ph.HE↗
arXiv2026

UniSGR-V2: Stepwise Target-Side Feature Anticipation and Crossing for Generative Recommendation

Semantic-ID-based generative recommendation unifies retrieval and ranking through autoregressive decoding but differs structurally from discriminative models, which can directly access rich target-item side features. The attributes are compressed into a finite codebook during quantization, while the target item remains undetermined during decoding and its features cannot be looked up. This target-side feature vacuum weakens generation at each semantic-ID level through high conditional entropy and cascading errors, and final ranking through missing target-side feature crossing. Existing methods interact with candidate representations or generate discrete attribute tokens, but none reconstructs a continuous target-side representation inside decoding to support both stepwise generation and ranking. We propose TacGR (Stepwise Target-Side Feature Anticipation and Crossing for Generative Recommendation). Before decoding each codeword, TacGR anticipates a continuous target-side feature vector and crosses it with the semantic prefix and user behavior sequence inside the decoder. The crossed representation conditions current-level generation and is directly used for ranking. Continuous-vector regression risks a learnable target collapsing with its prediction and prediction noise propagating into generation through attention. TacGR addresses these challenges with a semantic-anchoring auxiliary tower and a unidirectional visibility mask, respectively. For extremely long industrial behavior histories, it introduces gated convolutional denoising to suppress noise and adapts the pyramid memory funnel to reduce inference cost. Offline experiments on a large-scale Lazada dataset show significant gains over OneRec, UniRec, and UniSGR. Online A/B testing improves IPV, Order, and GMV by 0.79%, 5.55%, and 3.09%, respectively.

cs.IR↗
arXiv2026

Microscopic constitutive freedom in metamaterials

Metamaterials expand the range of accessible macroscopic responses through architectural design. Here, a cross-scale design strategy is introduced that combines microscopic control of static constitutive properties with spectral engineering through macroscopic architecture. Beyond shaping frequency-dependent responses, this strategy modifies the corresponding broadband performance bounds in passive systems by engineering the static parameters entering causal constraints. The concept is realized in granular activated carbon, where reversible sorption enhances equilibrium compressibility. Extrapolation of a multiscale model supported by low-frequency measurements to the zero-frequency limit yields an effective static bulk modulus approximately half the conventional nonsorbing isothermal reference value. This reduction substantially lowers the corresponding causal minimum-thickness bound for rigidly backed absorbers. A cascaded hierarchical composite architecture further redistributes absorption toward the target frequency band, achieving near-perfect absorption with a normal-incidence absorption coefficient above 0.9 across most of the 40--6400 Hz range. Together, static constitutive control and macroscopic spectral design enable ultrabroadband sound absorption beyond the conventional isothermal thickness benchmark without violating causality. This strategy offers a new approach to extending the attainable performance of metamaterials.

physics.app-ph↗
arXiv2026

Object- and Task-Aware Active Perception for Safe Navigation of Mobile Manipulators in Semi-Static Environments

Mobile manipulators must navigate safely in cluttered, partially unknown environments across logistics, manufacturing, and service applications. Semi-static changes between robot visits can lead to outdated scene knowledge and increased collision risk. We propose an active perception framework that enables a high degree-of-freedom (DoF) mobile manipulator to inspect uncertain or safety-critical regions with its hand camera during navigation. A probabilistic, object- and task-aware next-best-view (NBV) formulation prioritizes observations near the desired path and around uncertain or potentially changing objects. The perception objective is integrated into hierarchical-task model predictive control (HTMPC), exploiting kinematic redundancy for viewpoint adjustment while preserving the higher priority of base-trajectory tracking. The system unifies navigation, map maintenance, and active perception in a closed loop. Real-world tests show improved map consistency, reduced task-dependent map entropy, and lower navigation risk than passive or non-task-aware baselines. Additional simulation and real-world sequences demonstrate responses to controlled semi-static scene changes.

cs.RO↗
arXiv2026

SWatcher+: A Holistic Approach to Deploying Learned Cardinality Estimators in a Dynamic Setting

Cardinality estimation (CE) is crucial in database query optimization. Although recent advances have improved accuracy, deploying machine-learned CE models in dynamic databases remains challenging due to data updates and workload shifts. Retraining can be costly, but existing practices offer little guidance on when and how to update models, resulting in simple periodic retaining strategies that are suboptimal. We propose SWatcher+, a framework that adapts learned CE to dynamic workloads by monitoring data updates and query feedback. SWatcher+ selectively monitors high-impact subqueries for which wrong estimates lead to poor plan performance, and it proactively applies corrections to estimates during query optimization. This strategy enables immediate adaption to data and workload shifts without waiting for full model retraining, and carefully balances the cost and benefit of monitoring and adaption versus retraining. Our experiments show that SWatcher+ offers a better trade-off between query performance and overhead compared with existing approaches, making it a more practical solution for deploying learned CE in dynamic settings.

cs.DB↗
arXiv2026

Averaged proximal reflected gradient method for monotone variational inequalities

Projected reflected gradient (PRG) method proposed by Malitsky is efficient for solving monotone variational inequality (MVI), while the existing upper bound of step size is not tight due to the inequality scaling in the theoretical analysis. In this paper, we construct an averaged variant of PRG method for more general MVI and present a novel Lyapunov function to establish convergent theory. This averaged PRG method provides an improvement of the golden ratio algorithm [Y, Malitsky, Math. Program., 184, 383-410, 2020], and the involved step size is compatible with that for the classical methods, such as Popov's extragradient and forward-reflected-backward methods. Moreover, a fully adaptive strategy without linesearch is presented to adjust step sizes, which generates closed-form and potentially much larger step sizes. Numerical experiments on the Nash-Cournot equilibrium, HpHard, and image reconstruction problems demonstrate that the proposed algorithm significantly outperforms existing state-of-the-art methods.

math.OC↗
arXiv2026

Stereographic Multiple-Try Metropolis

Multiple-proposal MCMC algorithms have recently gained attention for their potential to improve performance, especially through parallel implementation on modern hardware. We introduce Stereographic Multiple-Try Metropolis (SMTM), a novel family of gradient-free algorithms designed for sampling high-dimensional distributions. By integrating multiple-try Metropolis (MTM) with the stereographic MCMC framework, SMTM overcomes the traditional limitations of MTM, particularly its pathological convergence behavior often observed in high dimensions. For both light-tailed and heavy-tailed targets, SMTM not only outperforms classical MTM and the existing stereographic random-walk Metropolis but also demonstrates strong robustness to tuning. These advantages are supported by high-dimensional scaling analysis and validated through extensive simulation studies.

stat.CO↗
arXiv2026

A possible evolutionary connection between high-mass X-ray binary pulsars and isolated rotation-powered pulsars

We investigate the long-term spin evolution of high-mass X-ray binary (HMXB) pulsars in the Small Magellanic Cloud (SMC) using archival measurements of spin periods and period derivatives from XMM-Newton, Chandra, and RXTE. In the P-|dP/dt| plane, the HMXB pulsars follow an approximately linear relation in log-log space with a slope of 1.02 +/- 0.11, similar to the trend shown by the youngest isolated rotation-powered pulsars (RPPs; e.g., Manchester et al., 2005; Lorimer and Kramer, 2005). For comparison, young RPPs from the ATNF Pulsar Catalogue give slopes that depend strongly on the characteristic-age cut: 1.10 +/- 0.16 for characteristic ages below 10,000 years and 0.72 +/- 0.08 for characteristic ages below 100,000 years. The full non-recycled population shows no significant correlation. The HMXB pulsars lie in the same characteristic-age range (approximately 1,000-10,000 years) as the youngest RPPs, but at spin periods two to three orders of magnitude longer. Reference characteristic-age contours in the HMXB P-|dP/dt| plane are roughly parallel to this relation, consistent with a continuous evolutionary sequence.

astro-ph.HE↗
arXiv2026

ESL-former: Event Sparse Linear Transformer for FPGA-Accelerated Malware Detection

Transformer based intrusion detection has shown strong potential for identifying unknown malware variants, but its deployment on resource constrained edge devices remains difficult due to the high computational and memory cost of self attention. This paper presents an event sparse Linear Transformer framework for malware detection on FPGA edge platforms. Instead of relying on an explicit attention matrix, the proposed method derives token and channel importance directly from the accumulated key value state of Linear Transformer. A contribution aware scoring function is introduced to measure the perturbation of each token or kernel channel to the linear attention state, allowing redundant tokens and low contribution channels to be removed without constructing quadratic attention maps. To translate sparsity into practical hardware acceleration, the proposed design further replaces conventional binary mask based execution with a Top K active token buffer and a hardware compaction unit. The selected active tokens and channels are reorganized into dense fixed length data streams, enabling downstream processing elements to execute regular multiply accumulate operations with reduced redundant memory access and control overhead. This design is suitable for low power FPGA based malware detection, where both latency and on chip resource usage are critical. Experimental evaluation demonstrates that the proposed sparse Linear Transformer can reduce hardware cost while preserving malware classification accuracy.

cs.AR↗
arXiv2026

MCAIR: Monitoring and Correcting Agent Unfaithfulness via Internal Representations

Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in completing complex tasks through multi-step reasoning and tool interactions. However, recent studies have revealed that LLM-based agents can exhibit unfaithful behaviors, where their final reports fail to faithfully reflect their actual execution trajectories. Specifically, when agents fail to accomplish tasks through the intended sources or procedures, they may resort to alternative sources or strategies while failing to disclose such deviations, leading users to incorrectly believe that the tasks have been completed according to the original requirements. We refer to this specific form of agent unfaithfulness as Agent Completion Unfaithfulness. Detecting and mitigating agent completion unfaithfulness is critical yet remains largely unexplored. In this work, we propose MCAIR, a runtime monitoring and correction framework that leverages internal representations of LLM-based agents. MCAIR uses multi-step internal representations as inputs to train a risk-aware probe, enabling online detection of potential completion unfaithfulness during execution. Furthermore, MCAIR introduces a risk-guided correction loop that prompts agents to review their execution history and mitigate potential inconsistencies between their execution trajectories and final reports. Additionally, we construct ACUD, a dataset for Agent Completion Unfaithfulness detection that provides step-level execution traces, step-level internal representations, and consistency annotations for training and evaluating runtime detection probes. Experiments demonstrate that MCAIR outperforms LLM-as-a-judge baselines, generalizes across prompts and tasks, and effectively reduces agent completion unfaithfulness.

cs.AI↗
arXiv2026

Low-cost non-iterative Sylvester solvers for dynamical low-rank approximation of reaction-diffusion equations

In this work, we develop low-cost non-iterative Sylvester solvers for dynamical low-rank approximation of reaction-diffusion equations and incorporate them into a basis-update \& Galerkin mass-lumped finite element method (\mbox{BUG-MLFEM}). By incorporating mass lumping via Gauss-Lobatto quadrature, the spatial discretization yields a matrix differential equation equipped with diagonal positive-definite mass matrices. We establish the weighted dynamical low-rank approximation (WDLRA) within the Galerkin framework of the MLFEM system, proving the theoretical equivalence between the BUG integrator and MLFEM on low-rank subspaces, which guarantees the discrete well-posedness of the implicit BUG steps. To overcome the severe computational bottleneck of standard $O(m^3+n^3)$ Sylvester solvers, we develop tailored, low-cost non-iterative solvers that exploit the inherent sparse-by-small algebraic structure. Under Dirichlet and Neumann boundary conditions (BCs), the large coefficient matrices are banded, enabling efficient diagonalization of the small $r \times r$ factor matrix and reducing the per-step computational complexity to $O((m+n)r^2)$ and storage to $O((m+n)r)$. For periodic BCs, we combine this diagonalization strategy with a rank-2 Sherman-Morrison-Woodbury update to handle wrap-around corner entries at negligible additional cost. Numerical experiments demonstrate first-order temporal and $(k+1)$th-order spatial convergence of the proposed BUG-MLFEM in the discrete $L^2$ norm, where $k$ denotes the polynomial degree of the finite element space, together with significant computational speedups over existing low-rank integrators.

math.NA↗
arXiv2026

Rapid Mixing of Stereographic MCMC for Heavy-Tailed Sampling

Sampling from high-dimensional, heavy-tailed distributions is a fundamental challenge in computational statistics, as many standard Markov chain Monte Carlo (MCMC) methods mix poorly in such settings. Recently, Stereographic MCMC [Yang et al., 2024] and the Sub-Cauchy Projection Sampler [Grazzi et al., 2026] have been shown to perform successfully on such tasks. However, establishing their non-asymptotic convergence properties remains an important open problem. In this work, we fill this gap by establishing non-asymptotic upper bounds on the mixing time of the stereographic projection and sub-Cauchy projection samplers. Our results demonstrate that, under certain conditions on the target and initial distributions, the mixing time is polynomial in dimension for a broad class of distributions, including light- and heavy-tailed cases. Motivated by the theoretical analysis, we further establish a new weighted isoperimetric inequality that extends the classical version for log-concave distributions to the heavy-tailed setting with optimal dimension dependence. The proof techniques provide new insights into the geometric properties of heavy-tailed distributions that govern rapid mixing in high dimensions.

stat.CO↗
arXiv2026

Extreme superluminal expansion traces an ultra-narrow GRB jet across relativistic regimes

The long-term evolution of gamma-ray burst (GRB) jets from ultra-relativistic launch to a slowly expanding radio source remains a fundamental problem in high-energy astrophysics, rarely resolved directly by imaging. We use very long baseline interferometry (VLBI) to trace the radio angular scale of GRB 221009A, the brightest GRB ever detected, from 5 to 299 days after the burst, and constrain the jet's geometry and dynamics. The Very Long Baseline Array data at 8.4 and 15.2 GHz are calibrated and imaged with AIPS and Difmap, and the source size is characterized by a circular-Gaussian model fitted to the complex visibilities. The jet dynamics, multi-wavelength emission, and source size are modeled within a two-shell collision scenario. A hydrodynamic simulation confirms that such an ultra-narrow jet undergoes lateral spreading. The observations reveal apparent superluminal expansion, with the expansion speed declining from about 90c at 5 days to about 6c at 300 days, tracing the jet's deceleration from the ultra-relativistic to the mildly relativistic regime and placing GRB 221009A far beyond the luminosity--velocity envelope of active galactic nuclei and microquasar jets. Combined with the multi-wavelength afterglow, they establish an ultra-narrow relativistic jet with a half-opening angle of about 0.01--0.03 radians, propagating through a wind-like medium at small radii before entering an interstellar-medium-like profile at larger radii. Joint modeling favors a two-phase size evolution with tentative evidence for lateral spreading. These results provide a direct geometric view of a GRB outflow and establish GRB 221009A as a benchmark for relativistic jet physics.

astro-ph.HE↗
arXiv2026

Augmented Roothaan-Hall Hessian Applied to Spin-Restricted Open-Shell Density-Functional Theory

We generalize the augmented Roothaan-Hall (ARH) Hessian formalism to the self-consistent field (SCF) optimization of spin-restricted open-shell (RO) wavefunctions, encompassing high-spin, low-spin, and two-determinant electronic states. A detailed ARH formulation is presented. We demonstrate that ARH is a highly efficient optimization algorithm for rapidly identifying accurate SCF solutions, primarily owing to its systematic construction of an effective Hessian, particularly in the case of Euclidean quadratic energy functions. The ARH is built upon a universal energy formulation, including grid-based integration, for spin-restricted closed-shell, spin-unrestricted and RO density functional theory (DFT), thereby unifying and simplifying their numerical implementation. The performance of the present method is evaluated using two benchmarking studies. First, for a series of iron-sulfur clusters exhibiting different spin states, which represent notoriously challenging SCF problems, the ARH algorithm demonstrates superior convergence efficiency relative to L-BFGS and truncated Newton methods, requiring much fewer RO-SCF iterations to achieve convergence. Second, the ARH approach tends to avoid convergence to higher-energy stationary points in two-determinant RO-SCF calculations for singlet excited states of selected photoactive compounds. Finally, an application of the ARH-based RO-SCF is illustrated by an investigation of the mechanistic origin of the spin-crossover phenomenon in Ni(II)-porphyrin complex utilized as a contrast agent.

physics.chem-ph↗
arXiv2026

FC-SWE: Failure-Conditioned RL for Long-Horizon Software Engineering Agents

Repository-level software engineering (SWE) is a challenging long-horizon setting: agents must reason over extended interactions, use tools, and adapt to stateful environments. Recent work trains SWE agents with reinforcement learning methods such as Group Relative Policy Optimization (GRPO), which independently sample multiple trajectories per issue, test the resulting patches, and compare terminal rewards within a fixed group. However, this training setup does not reuse verifier feedback from failed patches as context for subsequent attempts, even though this feedback contains valuable diagnostic information about what went wrong. Training on recovery trajectories is challenging because the preceding outcome determines whether the next trajectory is generated, while the failed execution determines its conditioning context. We introduce FC-SWE, a failure-conditioned RL framework that incorporates recovery attempts into policy training. After a patch fails verification, FC-SWE restores the repository to its original task state and uses the failed patch and verifier feedback as context for a recovery trajectory. FC-SWE adapts GRPO to these chains of complete, multi-turn tool-use trajectories through two mechanisms. Trajectory-local rewards preserve each attempt's verifier outcome, preventing recovery success from rewarding an earlier failed patch. Active-set advantage estimation forms a comparison group from all initial and recovery trajectories actually executed for the same issue, so failed attempts remain in the group while unexecuted attempts are excluded. On all 500 SWE-bench Verified tasks under a verifier-assisted protocol, FC-SWE with Qwen3.5-4B and SWE-agent achieves 41.7% Resolved@1 and 52.8% Resolved@2, compared with 38.9% and 48.5% for GRPO. Although trained with at most two attempts per chain, FC-SWE reaches 70.7% Resolved@11 under an eleven-attempt test-time budget.

cs.LG↗
arXiv2026

Tackling Sim-to-Real Mismatch Through Sampling-Based Disturbance Observers: From Analytical Models to Learned World Models

Robotic controllers increasingly rely on analytical models, simulators, cost-query interfaces, and learned world models. However, physical deployment can deviate from nominal assumptions, and additional disturbances may arise even when the model itself is accurate. In control systems, disturbance observers (DOB) are widely used to estimate such unmeasured effects from nominal models and measured feedback. Classical DOB formulations are generally built around explicit plant models. This paper develops the sampling-based disturbance observer (SDOB), extending the DOB principle to a broader range of models, including simulators and learned world models, through state-rollout or cost-query interfaces. SDOB separates two observable channels: state-effect disturbances, for which the feedback state differs from its prediction, and cost disturbances, for which the same query state receives different costs as the perceived environment changes. Diverse simulation and real-robot experiments across traditional and learned models demonstrate the effectiveness of SDOB in compensating for sim-to-real mismatch and improving control performance.

cs.RO↗
arXiv2026

Rapid identification based data-driven topology design independent from high-information entropy initial dataset

Topology optimization (TO) has been widely adopted in engineering design; however, it is prone to being trapped in local optima, particularly in strongly nonlinear problems. Sensitivity-free data-driven topology design (DDTD) offers a promising alternative. Nevertheless, existing DDTD-based methods still depend heavily on prior information or sensitivity-based TO methods for initialization, limiting their generality and independence in engineering applications. In this study, an efficient DDTD-based framework capable of being driven from low information-entropy initial datasets is proposed while improving computational efficiency. To reduce the dependence on high information-entropy initial datasets, a mesh-independent mutation module is introduced as a supplementary source of geometric features, enabling stable exploration under low information-entropy initialization. To alleviate the computational bottleneck in DDTD, where all candidate structures require numerical evaluations, a non-AI-based rapid identification algorithm is developed to efficiently identify potential high-performance structures, thereby significantly reducing the number of expensive high-fidelity simulations. The framework generates material distributions on body-fitted meshes to maintain consistency between numerical simulations and physical manufacturing. A signed distance field-based minimum length constraint is further incorporated to ensure reliable mesh generation. Numerical experiments on strongly nonlinear stress-related problems, together with comparisons with sensitivity-based TO methods, demonstrate the effectiveness of the proposed method. In microfluidic reactor and shell design problems involving non-differentiable constraints, the proposed method successfully addresses scenarios that remain challenging for both sensitivity-based TO and conventional DDTD-based methods.

physics.comp-ph↗
arXiv2026

CoRe-VLA: Preserving Cross-View Coordination in VLAs under Camera Shifts

VLAs combine pretrained vision-language representations with action generation to enable language-guided control across diverse tasks, becoming a mainstream paradigm in embodied intelligence. However, multiple studies have reported VLA's substantial declines in task success under camera shifts, revealing a key vulnerability that limits reliable deployment. To address this vulnerability, existing methods collect paired observations of the same scene from different viewpoints to fine-tune the VLA or train visual adaptation modules. Unfortunately, they require additional data collection and VLA training costs. In this paper, we first identify \emph{cross-view coordination breakdown} under external camera shifts: the robot may rely too heavily on wrist-view cues and consequently execute subtasks in the wrong order when losing global view. Motivated by this, we propose CoRe-VLA, a plug-and-play framework requiring neither additional multi-view data collection nor VLA fine-tuning, which can incorporate with exsiting VLAs. It reconstructs a scene point cloud and renders the observation from the VLA's training viewpoint to restore cross-view coordination. In CoRe-VLA, Render-to-Camera (R2C) Restoration reduces rendering-induced visual degradation, while Execution-Trajectory-Conditioned Alignment (ETCA) reduces robot idle time and mitigates motion conflicts during asynchronous execution. Experiments on 5 real-robot tasks, LIBERO-100 and LIBERO-Plus demonstrate CoRe-VLA substantially improves task success across mainstream VLAs under camera shifts. For example, CoRe-VLA raises PI0.5's success rate from 13.3% to 83.3% at a 1.6m camera shift in real-robot environment.

cs.RO↗