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Xiaofeng Wang

Publications and source records attributed to Xiaofeng Wang.

At least 19 recordsLinked to original sources
arXiv2026

ReconDreamer#: Self-Evolving Closed-Loop Synergy of Reconstruction and Generation for Dynamic Driving Scenes

Dynamic driving scene reconstruction has made remarkable strides in recent years by leveraging generative priors, particularly in the quality of novel trajectory rendering. Nevertheless, a significant gap remains between the generated data and real-world sensor observations, severely hindering further improvements in reconstruction fidelity. In this paper, we present ReconDreamer#, a self-evolving framework that synergizes reconstruction and generation for dynamic driving scenes, which substantially bridges the gap between synthesized novel trajectory data and recorded real-world observations. Within this self-evolving closed-loop framework, the generative and reconstruction models mutually reinforce each other through iterative refinement, ultimately yielding a significant enhancement in the 3D representation capability of the reconstruction model. Additionally, we introduce a geometry-prior-based adaptive ground model to further enhance the rendering quality of the reconstruction model. To validate the effectiveness of the proposed method, we conduct comprehensive evaluations on Waymo, nuScenes, PandaSet, and EUVS benchmarks. Specifically on Waymo, at a 6-m lateral shift, ReconDreamer# improves NTA-IoU and NTL-IoU by 8.8% and 3.0%, respectively, and reduces FID by 15.1% relative to ReconDreamer++.

cs.CV↗
arXiv2026

SASFormer: Stage-Aware Selective Attention Transformer for All-in-One Blind Image Restoration

All-in-one image restoration aims to recover high-quality images from multiple degradation types using a single unified model. Recent methods improve unified restoration with hybrid attention, prompt learning, or routing mechanisms, but generally focus on enhancing degradation adaptivity while employing similar feature modeling strategies across network stages. Consequently, the distinct representation requirements of encoding and decoding under diverse degradations remain insufficiently explored. In this paper, we propose SASFormer, a Stage-Aware Selective Attention Transformer for all-in-one blind image restoration. SASFormer follows a stage-aware design principle: the encoder uses established hybrid channel-spatial attention to build robust content-preserving and degradation-aware representations, while the decoder employs adaptive selective attention for restoration-oriented reconstruction. To address the stage-specific requirements of decoder reconstruction, we develop Adaptive Selective Channel Self-Attention (ASCSA) and Adaptive Selective Spatial Self-Attention (ASSSA), enabling the decoder to dynamically identify restoration-relevant channel responses and spatial contexts. Specifically, ASCSA performs adaptive query-channel selection and attention masking, while ASSSA adaptively integrates spatial attention across multiple window sizes. Compact implicit prompts further modulate the selection process without requiring explicit degradation labels. Extensive experiments on 3-, 5-, and 11-degradation settings demonstrate that SASFormer achieves strong restoration performance with favorable parameter efficiency. Moreover, stage-wise representation analysis and ablation studies provide empirical support for the proposed stage-aware design and the effectiveness of decoder-side adaptive selection.

cs.CV↗
arXiv2026

$π$-StepNFT: Wider Space Needs Finer Steps in Online RL for Flow-based VLAs

Flow-based vision-language-action (VLA) models have emerged as a powerful paradigm for embodied control, but multi-step denoising makes policy-gradient online reinforcement learning (RL) difficult because the final-action likelihood induced by the sampler is not directly tractable. We identify an exploration--supervision mismatch within the denoising trajectory, where stochastic differential equation (SDE) sampling explores a wider local stochastic transition space while supervision derived from the final denoised output does not directly use the adjacent transitions observed during sampling. We propose $π$-StepNFT (Step-wise Negative-aware Fine-Tuning), a flow-native online RL framework that couples wider stochastic exploration with finer step-wise supervision without computing the final-action likelihood. $π$-StepNFT treats observed SDE transitions as supervision units, using variance-normalized transition errors to rank mirrored positive and negative velocity branches and map rollout feedback to local velocity updates. On LIBERO, starting from shared few-shot SFT initializations, $π$-StepNFT raises average success from 57.6\% to 92.0\% for $π_0$ and from 77.1\% to 94.0\% for $π_{0.5}$. On ManiSkill, it achieves the highest reported mean success among the evaluated RL baselines on five test-only visual perturbations. Our project homepage is available at https://wangst0181.github.io/pi-StepNFT/.

cs.RO↗
arXiv2026

V$^3$AST-Bench: Verifiable, Valid, and Versatile Safety Evaluation for Coding Agents

Large language models increasingly act through code, modifying repositories, executing programs, and interacting with services in agent form. Their actions introduce risks that cannot be assessed from generated responses alone. Safety evaluation must verify actual consequences, distinguish harm avoidance from task incapability, and cover realistic workflows. We introduce V$^3$AST-Bench, a benchmark for Verifiable results, Valid interpretation, and Versatile risk coverage of Agent Safety. It comprises 212 risk tasks spanning 18 categories. Dedicated runtime environments verify harm and task outcomes through executable checks over states, records, and artifact behavior, while paired capability twin tasks verify workflow competence. Our experiments span 51 combinations of 11 models and five harnesses, spanning 86,496 trials and over 9 billion tokens of model interaction. Results show that V$^3$AST-Bench remains challenging even for frontier coding agents, as Claude Opus 4.8 with Claude Code still causes verified harm on 20.9% of tasks. Across model-harness combinations, agents with comparable competence exhibit markedly different safety outcomes, showing that task capability alone does not guarantee safe execution. More importantly, even explicit recognition of risks does not reliably prevent harmful actions, revealing a critical gap between risk awareness and safe execution. These findings highlight the need to move beyond risk recognition toward enforcing safety throughout execution.

cs.SE↗
arXiv2026

SEMPRE: Semantic-Aware Protocol Reverse Engineering via Consistency-Driven Joint Optimization

Protocol reverse engineering (PRE) is essential for understanding undocumented or proprietary communication formats, enabling security-critical tasks such as vulnerability discovery, malware C2 analysis, and IDS signature generation. However, existing PRE techniques rely on brittle hand-written rules, under-model contextual dependencies among neighboring fields, and often fail on messages with nested sub-protocols. We present SEMPRE, a fully automated PRE framework that replaces ad-hoc heuristics with recursive LLM reasoning grounded in fine-grained dynamic program analysis. SEMPRE uses taint-flow and loop traces to guide an LLM-driven tree-of-thought search that slices messages into fields while detecting nested sub-payloads, producing a hierarchical layout. It then infers each field's type and role and iteratively refines boundaries by freezing high-confidence segments and re-running detection under semantic constraints, yielding an EM-style procedure that monotonically improves structure and semantics until convergence. Evaluated on a broad corpus of mainstream protocols against six state-of-the-art (vs. <0.4 for all baselines) and 0.72 for semantic inference (vs. 0.13 for BinPRE). Ablations confirm the contribution of each component, and a case study on a prolific real-world botnet demonstrates practical utility.

cs.CR↗
arXiv2026

EMMA: Enhancing Robotic Control Robustness through a Generative Visual Model

The generalization of vision-language-action (VLA) models heavily relies on diverse training data. However, acquiring large-scale robot manipulation data across varied object appearances is costly and labor-intensive. To address this, we introduce Embodied Manipulation Media Adaptation (EMMA), a framework for augmenting VLA policies that combines a generative data engine with an effective training pipeline. We introduce DreamTransfer, a diffusion Transformer-based architecture for generating multi-view consistent and geometrically plausible embodied manipulation videos. It supports text-driven video editing on foreground, background and lighting while preserving 3D structure and geometric fidelity. We also utilize a hybrid training set of real and generated data and propose AdaMix to enhance the training process. AdaMix is a training strategy that adaptively weights samples according to policy performance to emphasize challenging samples. Evaluations show DreamTransfer outperforms previous video generation models in multi-view consistency, geometric fidelity, and text-conditioning precision. In real-world robotic tasks with zero-shot visual settings, our framework achieves a relative performance increase of over 92% compared to training with real data alone, and improves by an additional 17% with AdaMix.

cs.AI↗
arXiv2026

State World Models: Execute the Rules, Generate the Pixels

Existing world models can generate action-conditioned video from a single image. Many focus on camera movement, allowing users to explore the generated scene. However, navigation alone does not ensure that an action follows persistent rules when its outcome depends on state that is invisible in the current frame. We present Stateful Harness, a framework that separates world evolution from video rendering to support stateful and rule-governed interaction. It maintains an evolving world state under specified rules, renders each resulting event with a frozen video model, and uses the generated clip to update the world state for the next turn. At each turn, the harness resolves the action selected by the user and compiles the resulting event into an instruction, while a preview clip from an editable 3D scene provides spatial and camera guidance. To evaluate rule compliance, we build WorldState-Bench, a benchmark of 50 counterfactual twin pairs that share an initial frame and action but differ in hidden state and required outcome. Under rule-only prompting, the best of the 11 evaluated video backends produces the required endings for both cases in 32% of pairs. For MiniMax-H3, specifying the required endings raises this rate from 32% to 64%, while the full harness reaches 92% with the same frozen model. These results support separating rule execution from video rendering for world interactions with hidden state.

cs.CV↗
arXiv2026

UDL-RT: A unified differentiable all-sky microwave radiative transfer model with learnable parameterizations

Radiance-based retrievals and data assimilation require radiative transfer models (RTMs) to simulate radiances and quantify their sensitivities to atmospheric and surface conditions. Conventional RTMs often rely on separately maintained forward, tangent-linear, and adjoint codes, complicating model development and extension. Here, we present the Unified Differentiable and Learnable Radiative Transfer model (UDL-RT), a PyTorch-based framework for all-sky microwave radiative transfer that couples neural-network (NN) parameterizations of gaseous absorption, hydrometeor bulk scattering properties (BSPs), and ocean surface emissivity with an explicit radiative transfer solver in a unified differentiable computational graph. Automatic differentiation directly computes Jacobians with respect to input variables, reducing the model maintenance burden. The NN parameterizations offer flexible representations of complex nonlinear relationships. For example, the NN gas-absorption parameterization reduces the standard deviation of brightness-temperature (TB) errors by approximately 60-85% in the water-vapour channels compared with linear regression. Moreover, UDL-RT propagates loss gradients to trainable parameters to optimize physical parameterizations against observations, with GPU-accelerated batched computation supporting efficient iterative learning. Using Microwave Humidity Sounder-2 (MWHS-2) observations, we optimize the snow BSP parameterization through gradient-based learning. The optimized parameterization improves agreement between simulated and observed TB distributions and reduces the systematic bias and skewness of observation-minus-background (O-B) departures. Overall, UDL-RT integrates radiance simulation, sensitivity analysis, and parameterization optimization within a unified framework, supporting future remote sensing retrieval and data assimilation applications.

physics.ao-ph↗
arXiv2026

Fast X-ray Transient EP260321a: Shock Breakout through an Extended Circumstellar Matter

The Einstein Probe (EP) has discovered a fast X-ray transient, EP260321a, whose optical counterpart was subsequently classified as a broad-lined Type Ic supernova. Its observed rise time of about 690 seconds is inconsistent with theoretical predictions for shock breakout (SBO) from a Wolf-Rayet star, which is typically regarded as the progenitor of envelope-stripped supernovae. In this paper, we propose that EP260321a arose from a SBO within extended circumstellar matter (CSM), where breakout photons were initially trapped, then diffused out of the unshocked CSM as the ejecta expanded and the opacity decreased. We modeled this photon diffusion process using both Monte Carlo simulations and an analytical approach to describe the temperature evolution. Through Markov Chain Monte Carlo sampling, we obtain the best-fit CSM parameters of log10(Mcsm/Msun) = -3.3 for the total CSM mass, Rin = 160 Rsun for the inner CSM radius, and Rout = 612 Rsun for the outer CSM radius. The presence of such a dense CSM indicates that the progenitor of EP260321a underwent episodic mass loss just days before the explosion, providing new observational constraints on the late-stage evolution of massive stars.

astro-ph.HE↗
arXiv2026

Training-Data Extraction from Genomic Language Models

Genomic language models generate long nucleotide continuations from short prefixes, exposing a direct route for studying training-sequence extraction through the generation interface. We present an output-only black-box attack on Evo 2 that selects convergent groups of repeated generations without gradients, token likelihoods, or training-corpus access during candidate selection. Recovered content is then scored by an evaluator combining aligned length, nucleotide identity, match multiplicity, and sequence complexity. Across 304 runs on Evo 2 7B with bacteriophage and plasmid prefixes, the attack yields 888 candidate clusters, of which 85 match training segments of at least 500 bp, giving a cluster-level attack success rate of 9.57%. Final filtering retains 22 uniquely matched extraction events at the 500-bp threshold and 15 at 1,000 bp, covering 14 and 7 distinct training-sequence regions, respectively. Within-category source-homology screening retains 11 of the 22 events at a 99% exclusion threshold and four at 98%, with the latter mapping to two reference regions. The results establish convergence among repeated nucleotide generations as a practical output-only signal for recovering long training-sequence segments and characterize how recovery depends on prefix length, cluster size, perplexity, early stopping, and sequence domains.

cs.CR↗
arXiv2026

RobotAPO: Adversarial Physics Preference Optimization for Robotic Manipulation Video Generation

Robotic manipulation videos are increasingly used as visual plans for embodied agents, but optimizing purely for visual plausibility often fails to capture the fragile physical manifold of real-world interactions. Even minor physics-violating errors at the interaction boundary, such as interpenetration or premature object motion, can completely invalidate the inferred timing and pose needed for downstream execution. Because standard supervised fine-tuning lacks the direct pressure to penalize these localized failures, we introduce AgiBot-PhysPref. This rigorously curated 10,000-sample preference dataset isolates condition-matched physics violations, turning the generator's own failure distribution into a foundational signal for physical consistency. Building upon this, we propose RobotAPO, an adversarial physics preference optimization framework operating in the continuous flow-matching denoising space. To prevent the policy from merely memorizing static curated failures, RobotAPO employs a lightweight adversarial counterfactual proposer that learns a condition-dependent, physical-failure-biased direction in denoising space. This encourages the model to explore and better respect the physical interaction boundary, all while maintaining a pure prompt-and-reference inference interface without requiring external structural conditioning. Comprehensive evaluations demonstrate that explicitly correcting these localized physics violations improves downstream robot execution from generated videos. On held-out AgiBot conditions, RobotAPO outperforms the strongest controlled internal baseline in physical consistency by 6.8% hard score and 10.0% soft score. Crucially, in real-robot replay, it translates these physical-consistency gains into a 37.4% relative improvement in task success over the strongest controlled internal baseline.

cs.RO↗
arXiv2026

LEO-based Carrier-Phase Positioning for 6G: Design Insights and Comparison with GNSS

The integration of non-terrestrial networks (NTN) into 5G new radio (NR) enables a new class of positioning capabilities based on cellular signals transmitted by Low-Earth Orbit (LEO) satellites. In this paper, we investigate joint delay-and-carrier-phase positioning for LEO-based NR-NTN systems and provide a convergence-centric comparison with Global Navigation Satellite Systems (GNSS). We show that the rapid orbital motion of LEO satellites induces strong temporal and geometric diversity across observation epochs, thereby improving the conditioning of multi-epoch carrier-phase models and enabling significantly faster integer-ambiguity convergence. To enable robust carrier-phase tracking under intermittent positioning reference signal (PRS) transmissions, we propose a dual-waveform design that combines wideband PRS for delay estimation with a continuous narrowband carrier for phase tracking. Using a realistic simulation framework incorporating LEO orbit dynamics, we demonstrate that LEO-based joint delay-and-carrier-phase positioning achieves cm-level accuracy with convergence times on the order of a few seconds, whereas GNSS remains limited to meter-level accuracy over comparable short observation windows. These results establish LEO-based cellular positioning as a strong complement and potential alternative to GNSS for high-accuracy positioning, navigation, and timing (PNT) services in future wireless networks.

cs.IT↗
arXiv2026

GEM-Occ: From Visual Geometry Evidence to Embodied Semantic Occupancy Memory

Embodied agents exploring indoor environments require reliable semantic occupancy memory that persists across observations and revisits. Building such memory is challenging because each observation provides incomplete and uncertain geometric and semantic evidence. We introduce GEM-Occ, a Gaussian Evidence Memory framework that consolidates evidence accumulated over time into persistent semantic occupancy memory. Local predictions are converted into occupied semantic Gaussians and free-space ray evidence. Confidence- and visibility-aware causal updates integrate supporting observations, suppress occupancy contradicted by observed free space, and preserve previously observed structures through occlusion. A hierarchical memory organization supports continued mapping and efficient queries across connected indoor spaces. To evaluate this capability, we introduce HIOcc, a unified benchmark for embodied semantic occupancy memory. HIOcc establishes a shared semantic label space and evaluation framework spanning local prediction, room-level online mapping, and building-level mapping, while accommodating perspective and panoramic observations. Experiments on HIOcc demonstrate that GEM-Occ outperforms existing methods, enabling accurate semantic occupancy prediction and consistent online mapping across spatial scales with efficient memory usage and fast occupancy queries.

cs.RO↗
arXiv2026

JWST Nebular Spectroscopy of SN 2023qov: Circumstellar Dust Emission in a Normal Type Ia Supernova

We present panchromatic observations of the Type Ia supernova (SN Ia) 2023qov, ranging from $\sim$2 weeks before to $\sim$1 year after maximum light. JWST near- and mid-infrared spectra at $+$276 and $+$363~days show $\sim$400 K dust emission that cools by $\sim$75 K between epochs, the first unambiguous spectroscopic detection of dust emission in a normal SN Ia. We find that the emission is well described by models of carbonaceous dust placed within $\sim$1 light year of the SN, with a dust mass of $\sim$$10^{-4}$ M$_{\odot}$. We do not see evidence of active dust creation, suggesting an infrared light echo by pre-existing circumstellar dust as the likely source of the emission. The JWST nebular line profiles suggest asymmetric, stratified ejecta, similar to other normal SNe Ia, though a slight double-horn structure in the argon lines indicate a toroidal enhancement. SN 2023qov exhibits a slightly red, fast-declining early light curve ($Δm_{15}(B) = 1.47 \pm 0.05$ mag), from which we determine a $^{56}$Ni mass of $M_{56} = 0.21 \pm 0.04$ M$_{\odot}$, and a distance of $d = 36.0 \pm 1.8$ Mpc to the SN and its host, NGC 7029.

astro-ph.HE↗
arXiv2026

SimFuse3D: Source-Guided Target Simulation and Confidence-Guided Multi-Stage Localization Reweighting for Cross-Platform 3D Object Detection

Changes in sensor height and viewpoint alter object-level point distributions, making cross-platform LiDAR unsupervised domain adaptation (UDA) difficult. Self-training uses labeled source scans and unlabeled target scans, yet a retained prediction may provide a useful target location while enclosing sparse foreground returns, background clutter, or points inconsistent with the predicted box. We refer to this mismatch as box-point inconsistency. We introduce SimFuse3D, which preserves the target placement and repairs the associated pseudo object using measured geometry from labeled source scans. Object Memory retrieves a similar labeled source instance. Target Simulation places the retrieved source geometry at the target location, aligns its points with the target viewing geometry, and filters the aligned crop to approximate the target observation. Confidence-Guided Multi-Stage Localization Reweighting (CMLR) maps each target pseudo-object confidence score to a bounded weight shared by RPN localization and R-CNN box regression. All components operate only during adaptation, leaving the detector architecture and inference graph unchanged. Across six cross-platform transfers, SimFuse3D consistently outperforms Pi3DET-Net and achieves the best performance among the compared adaptation methods on nearly all metrics. On nuScenes-to-KITTI, it ranks first among the compared adaptation methods with both evaluated detectors.

cs.CV↗
arXiv2026

Effective Contact Theory for Exotic Loosely Bound States in Strongly Interacting Expanding Matter

Short-distance correlations generate universal relations that can be independent of microscopic details. Tan's contact is a prominent example, connecting close pairs, high-momentum constituents, and bound-state formation across atomic, condensed-matter, and nuclear systems. Whether an analogous universality governs the nonequilibrium freeze-out of relativistic QCD matter is an open question. We develop an effective contact theory that relates the production of loosely bound states to continuum two-particle correlations in heavy-ion collisions. The construction replaces the hard relative-momentum cutoff of conventional coalescence by a regulated Bethe--Peierls kernel: freeze-out localization fixes its momentum scale, while the composite yield fixes its contact residue. Bound and continuum observables then become projections of a common two-particle density matrix rather than independent phenomena. Coulomb-bound $Kμ$ atoms provide an ideal realization because their atomic, production, and source scales are widely separated. A contact-theory analysis of STAR $dΛ$ correlations supplies a first bound--continuum consistency test and demonstrates how inferred near-threshold parameters can depend on the finite expanding source. The framework establishes a general strategy for relating coalescence, correlations, and exotic bound states in strongly interacting expanding matter.

nucl-th↗
arXiv2026

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models

World-action (WA) models can generate long-horizon action chunks for general-purpose robotic manipulation, but they remain vulnerable to calibration, perception, and contact-dynamics errors in real-world precision tasks, often failing in the final few millimeters of alignment or insertion. We propose HALO-WA, a hybrid-attention latent-guided online reinforcement learning (RL) framework for WA models, which leverages latent features and action priors from the WA generation process through a lightweight actor-critic adapter to enable fast online adaptation to real deployment errors. HALO-WA introduces a hybrid-attention structure that preserves the temporal consistency of action chunks while reading task-relevant information from WA latents conditioned on visual context and end-stage correction requirements, thereby producing refined action chunks. We validate HALO-WA on four real-world precision manipulation tasks, where it improves the average success rate from 26.4\% for WA-base to 87.1\%, outperforming the strongest baseline by 19.2 percentage points while requiring only 45--75 minutes of online training per task. To facilitate reproducibility, we further conduct supplementary simulation experiments in RoboTwin and release the code at https://github.com/YeanRoot/HALO-WA.

cs.RO↗
arXiv2026

Closed Range and Essential Norms of Composition Operators on Weighted Dirichlet Spaces

We study closed range and essential norms of bounded composition operators $C_φ$ on weighted Dirichlet spaces $\mathcal{D}_α$. For $-1<α<0$, we first consider maps whose images are obtained by removing a compact subset from a simply connected subdomain of $\mathbb{D}$. In this setting, closed range is equivalent to the Reverse Carleson property and the Reverse Carleson Disk Condition for the counting measure $μ_{φ,α}$, uniform weighted area density of the image, $\mathbb{T}\subset\overline{φ(\mathbb{D})}$, and the inclusion of an outer annulus in the image. We also obtain a closed range characterization under the Uniform Tail Condition. For all $α>-1$, we establish two-sided essential norm estimates in terms of local averages of the generalized Nevanlinna counting function and an exact formula using boundary tail integrals. If the normalized counting density has vanishing oscillation at the boundary, we obtain exact formulas in terms of its boundary limsup, Berezin transform, and local averages.

math.FA↗