arXiv subjects

Xiaofeng Wang

Publications and source records attributed to Xiaofeng Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

SN 2025fhm: A central-engine powered Ic-BL supernova associated with X-ray transient EP250304a

We present X-ray, optical, and radio follow-up observations of EP250304a, an extragalactic fast X-ray transient (EFXT) discovered by the Einstein Probe. Its X-ray light curve exhibits two broad pulses with comparable peak fluxes within the first $\sim$1~ks, a feature rarely seen among low-luminosity gamma-ray bursts or EFXTs. Optical follow-up observations were carried out using the Korea Microlensing Telescope Network, the Thai Robotic Telescope, the Las Cumbres Observatory 1~m global network, the Gemini Multi-Object Spectrograph on Gemini south telescope, and the Global Supernova Network. The fast-cooling phase (within 3 days) of optical data can be well fitted by a shocked cocoon model. However, during the supernova phase (SN 2025fhm, from 3 to 88 days), the late-time light curve cannot be explained solely by radioactive $^{56}$Ni decay, as demonstrated by a grid of simulations using the one-dimensional Lagrangian radiation hydrodynamics code SNEC, which reveals a significant energy excess at late epochs. To account for this excess, a central engine like a rapidly spinning, highly magnetized neutron star is needed to provide additional energy injection. This model yields a best-fit spin period of $\sim$12.60~ms and magnetic field strength of $\sim 3.52\times10^{15} \rm G$, and it successfully explains both the late-time bolometric light curve and the early X-ray pulse structures. Our results indicate that EP250304a/SN 2025fhm is likely powered by a central magnetar rather than by radioactive decay alone, offering new insights into the energy budget and physical origin of EFXTs and their associated supernovae.

astro-ph.HE↗

arXiv2026

Drive-HWM: Hierarchical World Models for Dynamic-Latent Guided Autonomous Driving

World models offer a promising paradigm for autonomous driving by predicting how traffic scenes may evolve and using such predictions to support action generation. However, existing approaches either separate future prediction from action generation or jointly predict them at the same temporal scale, making it difficult to simultaneously achieve long-horizon anticipation and responsive, observation-grounded decision making. We present Drive-HWM, a hierarchical slow--fast world modeling framework that organizes future representation prediction and action generation at complementary temporal scales. The slow world model predicts multi-step future representations to capture extended scene evolution. To explicitly model the abundant motion dynamics in driving environments, we introduce Dynamic-Aware Latents learned through optical-flow prediction. Guided by these future representations, the fast model uses a lightweight multimodal backbone and an autoregressive expert to jointly predict the next frame and the immediate action from the latest observation. Next-frame prediction encourages the fast model to capture imminent scene evolution, while one-step action generation allows decisions to be continuously updated as new observations arrive. Extensive experiments on NAVSIM v1 and v2 demonstrate the strong driving performance of Drive-HWM. Comprehensive ablation studies further validate the effectiveness of the hierarchical slow--fast design, dynamics-aware future representations, and joint next-frame and action prediction.

cs.CV↗

arXiv2026

SN 2021pfs: A Type Ia Supernova Likely Affected by Progenitor Metallicity, as Revealed by Comparison with Its Twin Counterpart

We present extensive photometric and spectroscopic observations of the normal type Ia supernovae (SNe Ia) 2021pfs, which occurred in the Seyfert 2 galaxy NGC 5427 at a redshift 0.009. SN 2021pfs reached an absolute \textit{B}-band peak magnitude of $M_{\rm max}(B)=$-19.28 $\pm$ 0.40 mag. The mag and a post-peak decline rate of $Δm_{15}(B)=$1.13 $\pm$ 0.06 mag. The observed properties of this nearby SN Ia closely resemble those of SN 2011fe, including the main optical spectroscopic features and photometric evolution. Despite their similar decline rates, SN 2021pfs rose more rapidly in the $U$ band but more slowly in the $r$ and $i$ bands compared to SN 2011fe in very early phases. This photometric difference, particularly at short wavelengths, can introduce a systematic uncertainty of up to $\sim$12% in distance estimates. Analysis of the host galaxy's local and global environment shows an environment consistent with producing a higher-metallicity progenitor for SN 2021pfs than that of SN 2011fe.This higher progenitor metallicity may explain the observed photometric discrepancy and the resulting distance between SN 2021pfs and SN 2011fe, though a larger sample of such "twin" SNe Ia is needed to confirm this trend and assess its impact on cosmological measurements.

astro-ph.SR↗

arXiv2026

Absolute summability from diagonal operators to Carleson embeddings and Hankel operators

The present paper consists of three parts, each building on the preceding one. In Part I, we characterize the scalar sequences $\mathbf b=\{b_k\}$ for which the diagonal operator $\mathscr{M}_{\mathbf b}\bigl(\{a_k\}\bigr):=\{b_ka_k\}$ is $r$-summing from $\ell^p$ to $\ell^q$ for $1\le p,q\le \infty$ and $1\le r<\infty$. This resolves a problem left open in Garling's 1974 classification of $r$-summing diagonal operators. In particular, in the previously unresolved exceptional range $\{(p,q,r):1 \max\{p',q\}\},$ we identify a new intermediate exponent $κ\in(\max\{p',q\},r)$ and prove that $\mathscr M_{\mathbf b}$ is $r$-summing if and only if $\mathbf b\in\ell^κ$. In Part II, by developing two general transference principles for $r$-summability and building on the characterization from Part I, we characterize the positive Borel measures $μ$ for which the Carleson embedding operator $J_μ$ is $r$-summing from the weighted Bergman space $A_α^p(\mathbb B_n)$ to $L^q(\mathbb B_n,dμ)$ for $ 1\leq p,q<\infty$ , $1\leq r<\infty$, and $α>-1$, thereby extending the existing theory to the full off-diagonal range. In Part III, building on the characterization from Part II, we characterize the symbols $f$ and $g$ for which the big and little Hankel operators $H_f^β$ and $h_{\bar g}^β$, respectively, are $r$-summing from $A_α^p(\mathbb B_n)$ to $L^q(\mathbb B_n,dv_β)$, where $1\leq p<\infty$, $1 -1$, and $dv_β(z):=c_β(1-|z|^2)^β\,dv(z)$. These characterizations appear to be new even in the diagonal case $p=q$ and $α=β$.

math.FA↗

arXiv2026

When Safety Routing Breaks: Understanding Alignment Fragility under Benign Fine-Tuning

Benign fine-tuning severely weakens the safety alignment of large language models (LLMs), so we study why refusal behavior is so fragile. While prior work often attributes this failure to gradient conflict, we propose a fundamentally different Fisher-geometric explanation: safety Fisher is low-rank, and alignment makes the safety geometry flatter while preserving an output-routing pathway. After 100 benign fine-tuning examples, this pathway is selectively re-sharpened in output-side MLP modules, explaining the asymmetric fragility: safety can collapse to high attack success rates, while general utility degrades mildly. The routing view also explains why few safety examples can restore refusal behavior, indicating that internal safety-relevant representations are preserved. Finally, we show that LoRA and ASAM mitigate early collapse by suppressing output-side sharpness, but their protection weakens at larger fine-tuning scales. Overall, safety failure is best understood as a disruption of a low-rank output-routing mechanism

cs.CR↗

arXiv2026

KnockGS:interaction-Grounded Calibrationof Physical Gaussian Representations

Physics-integrated 3D Gaussian representations now allow reconstructed deformable objects to be simulated and rendered under explicit material models. Existing pipelines, however, assume that material parameters are known or manually specified, limiting their applicability when these parameters must be inferred from observed object dynamics. We propose KnockGS, an interaction-response PhysicalGS framework that estimates the elasticity and density scales of a 3D Gaussian object from its dynamics under a known applied force. Rather than treating physical simulation only as a forward process, we turn the force-induced response into a calibration signal: temporal response features are xtracted from the observed dynamics, the two material scales are estimated from those features, and the estimate is then frozen and written back into the same simulator so that it can be tested on an interaction it was never fitted to.We evaluate the framework on both parameter recovery and response-level fidelity. The estimated scales are compared against hidden ground truth, and the re-simulated object is measured against the target using 3D particle trajectories, response-curve statistics, and rendered-frame quality. Across five held-out material targets, our method recovers the scales substantially more accurately than response retrieval, global regression, or a fixed default material, and the frozen estimate remains predictive under interactions that differ in direction and in magnitude. Interaction response therefore carries enough information to calibrate material scales in physically grounded 3D Gaussian representations.Our study is a first step toward interactive PhysicalGS systems that calibrate a Gaussian asset whose rendered appearance and simulated response are consistent.

cs.CV↗

arXiv2026

XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment

Robot policy evaluation and deployment remain fragmented by model-specific software dependencies, data representations, and runtime interfaces, so that connecting N policies to M evaluation environments requires O(NM) separate integrations. We present XPolicyLab, a unified standard and open ecosystem that reduces this cost to O(N+M). XPolicyLab specifies common observation, action, and trajectory schemas together with a minimal adapter interface for observation updates, action prediction, batched execution, and episode reset, while a dependency-isolated client/server architecture separates policy inference from environment execution, so that each side retains its native software stack and may run locally or remotely. The ecosystem integrates 42 robot policies and standardizes their installation, debugging, serving, and evaluation workflows. Across these adapters, model-specific code varies by an order of magnitude while the environment-facing loop stays within a few lines of a fixed reference, confirming that the contract confines heterogeneity to the policy side. In a controlled study, conforming to the standard reduces the integration effort of a representative policy from over five hours to two hours, and packaged agent skills reduce it further to thirty minutes. The same adapters serve RoboTwin, RoboDojo simulation, and standardized real-robot evaluation through one interface. XPolicyLab is released as shared infrastructure for reproducible policy comparison and standardized deployment across simulation and physical platforms. Project website: https://xpolicylab.github.io/.

cs.RO↗

arXiv2026

SMTrap: Cost-Effective DoS Attacks Against Large Reasoning Models via SMT Conflict Guidance

Existing LRM-DoS methods rely heavily on model feedback to synthesize attack queries, requiring either repeated queries to the target model or training a dedicated attack model. These expensive operations severely weaken attack leverage. In this paper, we propose \emph{search amplification}, a novel, model-feedback-free LRM-DoS paradigm. It employs the conflict count derived from an Satisfiability Modulo Theories (SMT) solver as a low-cost external signal to guide the synthesis of inference-heavy Constraint Satisfaction Problem (CSP) instances. Our key observation is that LRMs depend on trial-and-backtracking search when solving CSPs, where higher SMT conflict counts on a given CSP instance positively correlate with more extensive LRM backtracking search and substantially longer output trajectories. Building on this finding, we propose \textsc{SMTrap}, a lightweight, CPU-only framework. Guided by SMT conflict counts, \textsc{SMTrap} generates inference-heavy CSP queries without model queries, attack-model training, or GPU computation. Evaluations across seven frontier models demonstrate the state-of-the-art LRM-DoS capability of \textsc{SMTrap}, producing DoS effects multiple times stronger than existing baselines. To mitigate the threat of \textsc{SMTrap}, we demonstrate a tool-based mitigation that significantly cuts token usage.

cs.CL↗

arXiv2026

GigaBrain-0.7: Scaling Embodied Foundation Models to Emergent Capabilities with a Three-System Architecture

Vision-language-action (VLA) models have become a dominant paradigm for generalist embodied agents, demonstrating strong complex and long-horizon task completion in structured settings. Yet it remains an open question whether current VLA systems can benefit from more effective architectural design, scale to substantially larger and more heterogeneous data regimes, and achieve broader generalization across tasks and embodiments. To this end, we present GigaBrain-0.7, an embodied foundation model with substantially improved generalization across diverse robot embodiments. Specifically, GigaBrain-0.7 unifies understanding, prediction, and action through a three-system architecture, scales pretraining to over 37,000 hours of heterogeneous embodied data, and introduces one-stage alignment training that jointly optimizes vision-language understanding and multi-embodiment action generation. Compared with the preceding GigaBrain-0 series and prior state-of-the-art models including $π_{0.5}$, GigaBrain-0.7 achieves substantial improvements in foundation zero-shot capabilities, language-conditioned instruction following, and post-training task success rates. In particular, on our in-house Maker H01 platform and mainstream robot embodiments, GigaBrain-0.7 demonstrates strong task adaptability and completion ability across both home and industrial scenarios. All training code and pretrained model weights will be released.

cs.RO↗

arXiv2026

Self-induced crystalline fluctuation spin-glass state in Mn7C3 binary compounds

Crystalline spin glasses are attractive compounds owing to their unique nature and applications. Here, we synthesised a bulk Pnma-type Mn7C3 spin glass by a high-temperature, high-pressure method. Experimental characterisation including X-ray diffraction and magnetic susceptibility measurements demonstrated that the compound has a triangular Ising-model-based structure, high freezing temperature of 37.4 K, and novel competition mechanism. Theoretical calculations and simulations revealed that the triangular Mn units are spontaneously frustrated and bridge neighbouring Mn units via polarised C atoms and messenger Mn atoms. Triangular C units each share one electron within a three-pronged electron cloud. This electron is the direct cause of frustration and competition in Mn7C3. The competition within the triangular Mn units suggests that the possible magnetic configurations are highly degenerate and that the Mn7C3 spin glass has high robustness. This work introduces a new family of spin glasses with ordered microgeometries that drive electronic structure disorder, and an application-friendly spin-glass material for use in fields like high-efficiency hardware and algorithm design in artificial intelligence.

cond-mat.mtrl-sci↗

arXiv2026

Enhancing Social Intelligence in LLMs with Hierarchical Reasoning and Utterance-Level Goal Rewarding

Large language models (LLMs) excel in structured tasks but struggle with dynamic social interactions, where success requires long-term goal coordination and rapid adaptation. Current methods often apply uniform goal-based rewards to every utterance, overlooking the specificity of objectives at each dialogue turn and failing to account for the rationale of potential strategies. Inspired by the Theory of Planned Behavior, we propose the Think-Strategy-Response (TSR) framework, which decomposes social dialogue into two hierarchical stages: high-level strategic planning and low-level linguistic execution. To optimize TSR, we introduce Linearized Hierarchical Reinforcement Learning with Variance-Gated Rewards (LHRL-VGR), a novel algorithm that dynamically routes rewards - balancing goal completion and strategy adherence - based on the variance of goal achievement scores. Experiments on the SOTOPIA benchmark show that our approach fine-tunes a Qwen2.5-7B agent to surpass the GPT-4o baseline by 7.32% in goal completion success, demonstrating state-of-the-art performance in multi-agent social negotiation tasks.

cs.CL↗

arXiv2026

SN 2022erq: A Superluminous Thermonuclear Supernova with Escalating Preexplosion Mass Loss

We present a photometric and spectroscopic study of the superluminous Type Ia supernova SN 2022erq. Its early spectra, dominated by iron-group elements with weak intermediate-mass features, might indicate highly efficient nuclear burning, broadly similar to that inferred for some overluminous SNe Ia. The rapid emergence and persistence of narrow Balmer emission lines superposed on this iron-rich spectrum provide clear evidence of long-lived interaction with a hydrogen-rich circumstellar medium (CSM), establishing SN 2022erq as a member of the rare Ia-CSM class. SN 2022erq reached a peak bolometric luminosity of about 8 x 10^43 erg/s and exhibited an exceptionally slow post-peak decline, indicating that its light curve is dominated by long-duration ejecta-CSM interaction. By combining H-alpha diagnostics with bolometric light-curve modeling, we reconstruct the pre-explosion mass-loss history of the progenitor. The mass-loss rate escalated by one order of magnitude over the final decades, rising from about 0.04 to about 0.6 solar masses per year. This surge produced a massive, extended CSM shell of about 3 solar masses out to about 3.5 x 10^16 cm. The young stellar environment (about 100 Myr) together with this substantial, extensive CSM points to a progenitor system consisting of a white dwarf and an intermediate-mass companion that underwent increasing mass loss prior to explosion.

astro-ph.HE↗

arXiv2026

The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results

This paper presents a review of the second LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aims to advance unified image restoration under diverse real-world degradation conditions, including blur, low-light, haze, rain, and snow. It provides a common benchmark for evaluating the restoration accuracy, robustness, and generalization capability of models across multiple degradation categories within a unified framework. The competition attracted 158 registered participants, and 20 teams were included in the final ranking after their submitted results were successfully reproduced and verified. This report provides a comprehensive analysis of the submitted solutions and corresponding results, highlighting recent advances in real-world all-in-one image restoration. The summarized methods and empirical findings reveal effective design strategies and establish an updated benchmark for future research in real-world low-level vision.

cs.CV↗

arXiv2026

Zero2Skill: Bootstrapping Robot Skills through Autonomous Data Collection, Training, and Deployment

Autonomous data collection governs the volume and quality of real-world trajectories for manipulation policy learning. Existing pipelines reduce human effort via self-resetting, VLM verification, or language-guided correction, yet episode-scoped fixes must be reissued whenever the same failure recurs, so oversight cost grows with session length rather than with the number of distinct problems. We present Zero2Skill, a human-robot symbiotic agentic system in which corrections are retained and reused across rounds. The collection loop collects, verifies, and resets autonomously, pausing for a remote operator only when a phase exhausts an explicit retry budget. An LLM parser maps each natural-language utterance to a structured adjustment stored in Corrective Memory, so addressed failure modes typically need not be corrected again under the same conditions. On a real-robot desktop-clearing testbed, Zero2Skill matches teleoperation episode success while reducing human working time to 16%. Language corrections improve verifier-human agreement in all four evaluated settings and raise average single-attempt success from 12.5% to 47.5% (arm-selection: 20.0% to 50.0%). Policies fine-tuned on Zero2Skill data match teleoperation-trained policy success at a fraction of collection human cost.

cs.RO↗

arXiv2026

Optical observations on the young Type Ia SN 2021fxy with detached high velocity features

We present optical observations on the young type Ia supernova (SN Ia) SN 2021fxy obtained within a few days after the explosion, with a focus on its prominent high-velocity features (HVFs). It reached a $B$-band maximum of $M_{\rm max}(B) = -19.36\pm0.31$ mag, corresponding to a bolometric luminosity of $\sim 1.3\times10^{43}~\rm{erg~s^{-1}}$ with a synthesized $^{56}$Ni mass of $0.58\pm0.14$ M$_{\odot}$. The early spectra exhibit strong HVFs of intermediate-mass elements that are significantly detached from the photospheric components. In particular, the velocity of the Si II $\lambda6355$ HVFs follows a power-law evolution ($β\approx 0.1$), shallower than the expected photospheric velocity evolution expected for an assumed $n=10$ density profile ($β\approx 0.22$) under homologous expansion. This behavior is consistent with the HVFs forming in intrinsic ejecta structures at least partially decoupled from the bulk outer ejecta, providing a possible constraint on the explosion physics of SNe Ia.

astro-ph.HE↗

arXiv2026

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch

World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future visual observations, using future scene evolution as dense supervision for physically grounded action generation. However, a common design in existing WAMs is to explicitly generate future videos at inference time, incurring substantial computational overhead and hindering real-time closed-loop deployment. GigaWorld-Policy addresses this issue with an action-centered formulation, where future visual dynamics are used during training while action-only decoding is used at inference time. Building upon this framework, we present GigaWorld-Policy-0.5, an enhanced action-centered WAM designed for more efficient robot control. During pretraining, GigaWorld-Policy-0.5 adopts a mixed Action-Conditioned World Modeling (AC-WM) and WAM training strategy. This strengthens the coupling between visual dynamics and robot actions and improves the transferability of action representations for downstream policy learning. For efficient inference, GigaWorld-Policy-0.5 introduces a Mixture-of-Transformers architecture that separates visual dynamics modeling and action generation into specialized experts, reducing active computation during action-only inference and achieving 85 ms inference latency on a local RTX 4090 setup. In addition, we employ an agent-based AutoResearch pipeline to systematically search training configurations, enabling more efficient identification of optimal experimental setups while reducing the time and manual intervention required for hyperparameter tuning. Experiments and ablations show that GigaWorld-Policy-0.5 preserves the training benefits of future visual dynamics while improving inference efficiency for robot control.

cs.RO↗

arXiv2026

X-rays breaking out of pre-explosion ejecta mark a supernova's first light

Massive stars die as core-collapse supernovae, whose optical light emerges days after the implosion. Theory predicts that the initial collapse-driven shock, upon breaking through the star and dense circumstellar medium, emits a brief thermal flash of soft X-rays and ultraviolet. Yet these elusive first signals have remained largely undetected, owing to limited wide-field soft X-ray monitoring. Here we report the discovery of a soft X-ray flash, EP260321a, followed days later by a broad-lined supernova from an envelope-stripped progenitor. Its X-ray spectrum, best modeled with blackbody, establishes it as the long-sought archetypal shock breakout. The burst's duration and energetics place the breakout at a radius of 300 solar radii, tracing a dense surrounding shell and revealing abrupt mass ejection within the final month before collapse.

astro-ph.HE↗

arXiv2026

ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI

Embodied AI is moving from isolated perception or action modules toward physical agents that understand, plan under goals, act through robot bodies, monitor progress, and improve from experience. Existing systems address this loop only in parts: end-to-end policies generate actions but often lack spatial reasoning, planning, and execution assessment, while robot-agent systems orchestrate tools or specialists but do not learn a shared representation. This fragmentation limits general Physical Agentic AI. We present ACE-Brain-0.5, a unified embodied foundation model that organizes robot intelligence into five coupled functions: spatial perception, decision making, embodied interaction, self-monitoring, and self-improvement. Built on ACE-Brain-0, which established spatial intelligence as a shared scaffold across robot platforms, ACE-Brain-0.5 extends an understanding-centric model into a closed-loop foundation model. A single 8B backbone instantiates the first four functions: grounding objects and affordances, reasoning over 3D and egocentric spatial relations, decomposing instructions into subgoals, generating navigation and manipulation actions, and estimating progress for verification and recovery. To unify these capabilities without cross-task interference, we introduce SSR+, which extends Scaffold-Specialize-Reconcile with a Reactivate stage after task-vector merging. The fifth function, self-improvement, is realized by a companion framework that updates external execution state, including task schemas, spatial memory, and failure-recovery cases, from rollouts. Across fifteen benchmarks, ACE-Brain-0.5 improves over ACE-Brain-0 on 14 of 18 spatial perception and grounding benchmarks, achieves competitive navigation and manipulation performance, and provides strong progress estimation in ID and OOD settings. Together, these results mark an early step toward general Physical Agentic AI.

cs.RO↗