Searcharxiv⌕ Search

arXiv subjects

Lei Lei

Publications and source records attributed to Lei Lei.

At least 19 recordsLinked to original sources
arXiv2026

Learned Smartphone ISP on Mobile GPUs: Mobile AI 2026 Challenge Report

With the increasing importance of mobile camera quality in modern smartphones, growing attention has been directed toward computational photography and advanced image signal processing (ISP) algorithms aimed at enhancing various perceptual attributes of captured images. This creates a need for efficient deep learning models for this problem that are not only performant but can also be deployed on constrained mobile hardware. In this Mobile AI challenge, we address this problem and propose the participants to design fast solutions that can reconstruct RGB photos from RAW Bayer camera data while running on popular smartphone GPUs from Qualcomm and Arm. For this, the participants were provided with a large-scale Fujifilm UltraISP dataset consisting of RAW-RGB image pairs captured with the Sony IMX586 Quad Bayer mobile sensor and a professional 102MP medium format FujiFilm GFX100 camera. The runtime of all models was evaluated on the Snapdragon 8 Elite Gen 5 and Dimensity 9500 chipsets with the Adreno and Mali GPUs, respectively. Top solutions proposed in this challenge are able to reconstruct high-quality 5MP RGB image from raw data on both platforms under 100 milliseconds. A comprehensive description of the models developed in the challenge is provided in this paper.

cs.CV↗
arXiv2026

From Fragmentation to Fusion: A Survey and Roadmap for Omnimodal Embeddings

Embedding models underpin virtually every modern AI system, from search engines to recommendation systems. Despite shared mathematical foundations, research on embedding models remains fragmented by modality: the communities develop largely in isolation, seldom exchanging insights. We argue that this fragmentation obscures a deeper convergence. Driven by contrastive learning, Transformer architectures, and a granularity shift, embedding research across modalities is converging toward a common set of principles, even as modality-specific challenges remain underexplored. To reason across this convergence without conflating it, we propose a framework of three progressively harder capabilities: coverage, comparability, and compositionality. Three paths toward unification satisfy these in turn: transformation broadens coverage, alignment enables comparability, and joint embedding, the terminus we advocate, aims at compositionality. This position paper traces the resulting evolution, providing the first unified, cross-cutting treatment of omnimodal embedding research.

cs.CL↗
arXiv2026

Move-Then-Operate: Behavioral Phasing for Human-Like Robotic Manipulation

We present Move-Then-Operate, a Vision language action framework that explicitly decouples robotic manipulation into two distinct behavioral phases: coarse relocation (move) and contact-critical interaction (operate). Unlike monolithic policies that conflate these heterogeneous regimes, our architecture employs a dual-expert policy routed by a learnable phase selector, introducing a structural inductive bias that isolates phase-specific dynamics. Phase labels are automatically generated via an MLLM-based pipeline conditioned on lightweight contextual cues such as end-effector velocity and subtask decomposition to ensure alignment with human motor patterns. Evaluated on the RoboTwin2 benchmark, our method achieves an average success rate of $68.9\%$, outperforming the monolithic $π_0$ baseline by $24\%$. It matches or exceeds models trained on $10\times$ more data and reaches peak performance in $40\%$ fewer training steps, demonstrating that architectural disentanglement of move and operate phases is a highly effective and efficient strategy for mastering high-precision manipulation. The code is available at: https://github.com/healenrens/Move-then-Operate

cs.RO↗
arXiv2026

Black Holes in the Red-sequence Elliptical Galaxies at Redshifts $\sim 0.7-2.5$: Not Dark Energy Source but Remnants of Little Red Dots

The nature of dark energy remains one of the most profound mysteries in modern cosmology. One intriguing proposal is that black holes (BHs) could be the astrophysical source of dark energy through a cosmological coupling mechanism, and strong evidence has been claimed via analyzing the growth of the black hole masses in the red-sequence elliptical galaxies at redshifts $\leq 2.5$. In this work, with a group of very high redshift AGNs detected by the James Webb Space Telescope (JWST) in the red-sequence elliptical galaxies, we show that the possibility of BHs being the astrophysical source of dark energy has been rejected at a confidence level exceeding 10$σ$. Moreover, it turns out that the Little Red Dots recently discovered by JWST, characterized by the low accretion rates, can naturally evolve into the red-sequence elliptical galaxies hosting the relatively low mass black holes at the redshifts of $\sim 0.7-2.5$, without the need of black hole cosmological coupling.

astro-ph.CO↗
arXiv2026

A dense dark matter core of the subhalo in the strong lensing system JVAS B1938+666

The nature of dark matter remains unknown, motivating the study of fuzzy/wave dark matter (FDM/$ψ$DM) and self-interacting dark matter (SIDM) as alternative frameworks to address small-scale discrepancies in halo profiles inferred from observations. This study presents a non-parametric reconstruction of the mass distribution of the previously-found, dark subhalo in the strong-lensing system JVAS B1938+666. Compared with the standard Navarro-Frenk-White (NFW) profile, both SIDM and $ψ$DM ($m_ψ=1.32^{+0.22}_{-0.31}\times 10^{-22} \, \rm eV$) provide significantly better fits to the resulting density profile. Moreover, the SIDM model is favored over $ψ$DM with a Bayes factor of 14.44. The reconstructed density profile features a characteristic kiloparsec-scale core ($r_c \approx 0.5 \, \rm kpc$) with central density $ρ_c \approx 2.5\times 10^{7}\, \rm M_{\odot} \, kpc^{-3} $, exhibiting remarkable consistency with the core-halo mass scaling relations observed in Local Group dwarf spheroidals. These findings offer insights that may help address the core-cusp discrepancy in $Λ$CDM substructure predictions.

astro-ph.CO↗
arXiv2026

Transport-Coupled Bayesian Flows for Molecular Graph Generation

Molecular graph generation (MGG) is essentially a multi-class generative task, aimed at predicting categories of atoms and bonds under strict chemical and structural constraints. However, many prevailing diffusion paradigms learn to regress numerical embeddings and rely on a hard discretization rule during sampling to recover discrete labels. This introduces a fundamental discrepancy between training and sampling. While models are trained for point-wise numerical fidelity, the sampling process fundamentally relies on crossing categorical decision boundaries. This discrepancy forces the model to expend efforts on intra-class variations that become irrelevant after discretization, ultimately compromising diversity, structural statistics, and generalization performance. Therefore, we propose TopBF, a unified framework that (i) performs MGG directly in continuous parameter distributions, (ii) learns graph-topological understanding through a Quasi-Wasserstein optimal-transport coupling under geodesic costs, and (iii) supports controllable, property-conditioned generation during sampling without retraining the base model. TopBF innovatively employs cumulative distribution function (CDF) to compute category probabilities induced by the Gaussian channel, thereby unifying the training objective with the sampling discretization operation. Experiments on QM9 and ZINC250k demonstrate superior structural fidelity and efficient generation with improved performance.

cs.LG↗
arXiv2026

Searching for Solar-Basin Axionlike-Particle Decay with XMM-Newton Blank-Sky Observations

Axion-like particles (ALPs) bound in the solar gravitational field form the so-called ALP solar-basin. Since the two-photon decay of non-relativistic particles is approximately isotropic, this population can be searched for using observations in the anti-solar direction. In this work, we propose a search strategy for narrow decay-line signals from the ALP solar basin using \textit{XMM-Newton} blank-sky observations (XMM-BSOs) stacked spectra data taken in directions opposite to the Sun. By jointly fitting the signal and background model, we obtain limits on $g_{aγγ}^{95}$ in the mass range $m_a=1.4\text{--}16~{\rm keV}$, with typical sensitivities of $g_{aγγ}\sim10^{-10}\text{--}10^{-11}~{\rm GeV}^{-1}$. We have implemented the first anti-solar search for the solar basin, demonstrating that this strategy can exploit the stacked exposure of a large number of X-ray observations and provide a scalable analysis framework for future searches.

hep-ph↗
arXiv2026

Robust Bimanual Vision-Language-Action Models via Embarrassingly Simple Modality Masking

Query-based Vision-Language-Action (VLA) models offer low-latency inference that is attractive for bimanual robotic manipulation, but we observe that they can still exhibit discontinuous actions and execution failures in complex dual-arm tasks. We hypothesize that unstable multi-view and language fusion is one contributing factor in these failures, often coinciding with attention spreading to distracting regions. To improve robustness, we introduce the Modality Masking Mechanism (M3), an embarrassingly simple, training-only strategy that requires no architectural changes or large-scale robot pretraining. M3 stochastically masks subsets of modality channels during training, exposing the policy to controlled partial observations and encouraging it to rely less on distracting cues and more on evidence that remains reliable. We evaluate M3 on ten bimanual tasks from RoboTwin 2.0 and on three long-horizon real-world tasks. Compared with the Adapter baseline, M3 improves average success by 21.7% in the Clean setting and 11.4% in Clean2Rand, where policies are trained on clean demonstrations and evaluated on randomized scenes, while also improving averaged real-world full-task success by over 30%. These results suggest that structured training-time masking is a practical way to improve the robustness of query-based VLA policies for bimanual manipulation.

cs.RO↗
arXiv2026

NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge

This paper presents a review of the NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge. The objective of the competition was to merge a set of misaligned smartphone images in the raw domain, captured in low-light conditions, into a single, clean image. Introduced setup simultaneously addresses two problems of low-light photography: visual degradations such as high noise and mixed scene illuminants, and the geometric inconsistencies caused by hand movement during multi-frame capture. To advance research in low-light and nighttime computational photography, a challenging dataset was collected comprising 585 real-world scenes, spanning indoor low-light and outdoor nighttime conditions, for training and benchmarking participant solutions. The competition employed a three-stage evaluation protocol: automatic validation via the CodaBench platform in stages one and two, followed by blind assessment on a private test set for the final ranking. Ten teams surpassed the established baseline, achieving improvements of up to +6.49 dB in PSNR and +0.0101 in SSIM, thereby establishing new state-of-the-art performance for burst-based low-light image enhancement. These results demonstrate significant progress in handling real-world noise, motion, and illumination variability in the low-light setting. Comprehensive results, leaderboards, and additional information are publicly available at https://nightimaging.org.

cs.CV↗
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

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking

Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications. Multi-agent reinforcement learning (MARL) has emerged as a promising approach for this problem. However, key MARL challenges, including non-stationarity, coordination difficulty, large action space, partial observability, and limited robustness and generalization, are often intertwined, making it difficult to assess their individual impact on performance in vehicular environments. Moreover, existing studies primarily focus on developing new algorithms, while systematic benchmarking and comparative analyses remain limited. To address this gap, we formulate C-V2X RRA as a hierarchy of multi-agent interference games that progressively introduce key MARL challenges. Based on this framework, we develop a suite of benchmark learning tasks and construct training and testing datasets from SUMO-generated highway traces with diverse vehicular topologies and interference conditions. Using the proposed benchmark, we evaluate representative MARL algorithms spanning value-based, actor-critic, Independent Learning (IL), and Centralized Training with Decentralized Execution (CTDE) paradigms. The results identify robustness and generalization across diverse vehicular topologies as the dominant challenge among those considered in this work, reducing average normalized return by up to 59 percentage points, and show that, on the most challenging task, the best actor-critic method outperforms the best value-based method by 42\%. By revealing the relative strengths and limitations of different MARL paradigms and open-sourcing the code, datasets, and benchmark suite, this work provides a systematic and reproducible foundation for evaluating and advancing MARL algorithms in vehicular networks.

cs.MA↗
arXiv2026

Self-Driven Atomic Dispersion in Graphitic Layers

Carbon-supported single-atom catalysts maximize metal utilization, but how metal nanoparticles transform into isolated atoms within carbon remains unclear. We show that metal nanoparticles can undergo a self-driven dispersion process under hydrocarbon oxidation conditions, transforming into single atoms that are confined in carbon matrix. Using Pt-catalysed hydrocarbon oxidation as a model, we combine operando electron microscopy, near-ambient-pressure X-ray photoelectron spectroscopy and mass spectrometry to track coupled structural and chemical evolution. Graphitic carbon grows at step edges of Pt nanoparticle, continuously reconstructing Pt surface and generating undercoordinated sites for atom release. In-situ generated CO accumulates at the metal-carbon interface, weakening bonding and facilitating self-amplified atom release and migration. Defective carbon overlayers then trap, stabilize and transport liberated atoms, while oxidative etching preserves interfacial access of reaction-gas. Similar behaviour across other metals suggests a general atomization pathway for single-atom catalyst synthesis, yielding products with electrocatalytic hydrogen production activity beyond standard commercial benchmarks.

cond-mat.mtrl-sci↗
arXiv2026

DISCOVER: A Solver for Distributional Counterfactual Explanations

Counterfactual explanations (CE) explain model decisions by identifying input modifications that lead to different predictions. Most existing methods operate at the instance level. Distributional Counterfactual Explanations (DCE) extend this setting by optimizing an optimal transport objective that balances proximity to a factual input distribution and alignment to a target output distribution, with statistical certification via chance constrained bounds. However, DCE relies on gradient based optimization, while many real-world tabular pipelines are dominated by non-differentiable models. We propose DISCOVER, a model-agnostic solver for distributional counterfactual explanations. DISCOVER preserves the original DCE objective and certification while replacing gradient descent with a budgeted propose-and-select search paradigm. It exploits a sample-wise decomposition of the transport objective to compute per-row impact scores and enforce a top-k intervention budget, focusing edits on the most influential samples. To guide candidate generation without predictor gradients, DISCOVER introduces an OT-guided cone sampling primitive driven by input-side transport geometry. Experiments on multiple tabular datasets demonstrate strong joint alignment of input and output distributions, extending distributional counterfactual reasoning to modern black box learning pipelines. A code repository is available at: https://github.com/VALHALLA9/Discover.

cs.LG↗
arXiv2026

How is Water released in Hydrogen-Based Metal Oxide Reduction? Unraveling the Kinetic Bottleneck in Sustainable Metal Production

Hydrogen-based direct reduction of metal oxides is a ubiquitous solid-gas redox process central to geophysics, sustainable metallurgy, redox energy cycles and catalysis. During this process, hydrogen removes lattice oxygen to form water, yet product water has long been regarded as a passive exhaust, and its nanoscale formation, trapping and removal remain poorly understood. Here, we directly observe redox-product water release from iron oxide during hydrogen-based direct reduction. Because water removal emerges from coupled structural, chemical and crystallographic evolution across multiple length-scales under realistic non-equilibrium reaction-conditions, we establish a correlative multiscale in-situ approach that links pore evolution, molecular water signatures, phase transformation and chemical-state evolution during hematite reduction. We uncover a mechanism in which oxygen removal induces closed nanopores spatially delocalized from reaction surfaces, causing transient trapping of water vapor. Water is released only when these pores coalesce into a percolating network connected to the surface, coinciding with and accelerating the onset of the hematite-to-magnetite transformation. These findings show that dynamically evolving pore topology governs mass transport and redox kinetics in solid-gas reactions, closing a critical mechanistic gap in product-water removal and providing nanoscale guidance for hydrogen-based metal extraction, reactor design, and sustainable redox energy technologies under practical conditions.

physics.chem-ph↗
arXiv2026

RATIO: Redundancy-Controlled Stochastic Routing for Reliable Vehicular Multi-Hop Networking

Reliable, low-latency multi-hop data delivery in vehicular networks is increasingly demanded, yet remains challenging due to frequent route failures caused by high mobility and intermittent blockage. While redundancy-based routing enhances robustness by forwarding packets over multiple paths, over-replication intensifies contention and introduces additional delay, highlighting the need to carefully managing redundancy--reliability trade-off. However, conventional deterministic multi-path replication typically duplicates packets to an integer number of branches, making the redundancy level hard to tune and adapt to time-varying network dynamics in vehicular networks. To this end, Redundancy-Controlled Stochastic (RATIO) routing is proposed in this paper. For each active flow, RATIO constructs a weighted reduced directed acyclic graph (DAG) as the routing structure, where edge weights specify per-link forwarding probabilities. At fork nodes, the aggregate outgoing forwarding probability is allowed to exceed one and a modulo-based stochastic forwarding rule is employed to guarantee feasible forwarding, thereby enabling continuously controllable redundancy. An idealized RATIO design is formulated as a load-minimizing optimization subject to per-flow timely-reliability and link-capacity constraints, but the problem is generally intractable under time-varying wireless dynamics. Accordingly, a practical heuristic, termed H-RATIO, is developed. H-RATIO constructs a compact reduced DAG by taking the union of candidate paths and optimizes forwarding probabilities via local scoring and replication-adjustment iterations. Extensive trace-driven SUMO/ns-3 co-simulations demonstrate that RATIO/H-RATIO consistently achieves the highest timely PDR compared to baselines, while providing substantially better delivery efficiency, especially under high-load scenarios.

cs.NI↗
arXiv2026

Controllable Radar Simulation with Waveform Parameter Embedding

Autonomous driving simulators still lack high-fidelity radar, even though radar is critical for robust perception in adverse weather. A key obstacle is that raw radar point clouds are extremely sparse and stochastic, making it difficult to model; we argue that simulating the full range-azimuth-Doppler cube is a more principled target. Existing radar cube simulators either rely purely on neural generators, which are opaque and offer little control over sensor attributes, or on detailed electromagnetic pipelines, which are slow, require proprietary hardware specifications, and still struggle to capture real-world complexity. We introduce Ctrl-RS, a controllable radar cube simulation framework that combines the strengths of both worlds. First, we build an environment reflection tensor from diverse sensor sources (including LiDAR, monocular cameras, and existing radar). Second, we abstract radar physics into a compact set of waveform parameters that characterize the 3D point spread function, yielding an intuitive embedding of radar attributes such as range resolution, Doppler broadening, and azimuth beam shape. Third, we train a WARP-Net on a large mixed dataset that fuses real, analytically synthesized, and simulator-generated radar cubes to cover a wide distribution of radar attributes. Ctrl-RS supports viewpoint changes, actor removal, and attribute editing. Experiments on RADDet, Carrada, and nuScenes show that our simulated data can match or surpass real radar in 2D detection and semantic segmentation, and consistently boosts performance in 3D detection when combined with real data. The Project is available at https://github.com/zhuxing0/Ctrl-RS.

eess.SP↗
arXiv2026

StepAudio 2.5 Technical Report

Unified audio-language modeling has emerged as a prominent trend in modern speech systems, promising to bring the reasoning capabilities of large language models to auditory tasks. However, existing unified foundations often struggle to match the depth of specialized systems across automatic speech recognition (ASR), text-to-speech synthesis (TTS), and realtime spoken interaction. Bridging this gap remains an open challenge. This report presents StepAudio 2.5, a unified audio-language foundation model that matches or exceeds specialized systems across all three capabilities. Rather than treating these tasks as architecturally distinct, we operate on the premise that once text and audio share a multimodal representational space, task specialization becomes a matter of operational regimes: data construction, optimization targets, and decoding constraints. Guided by this insight, we advance the post-training paradigm from standard supervised learning to task-tailored Reinforcement Learning from Human Feedback (RLHF), using it as the primary mechanism to define complex optimization targets. We leverage this RLHF-centric alignment, alongside specialized decoding, to shape a shared backbone into three distinct operational modes. Concretely, the ASR branch advances transcription efficiency via verifiable multi-token decoding; the TTS branch achieves controllable, expressive synthesis through preference-based RLHF and context-rich supervision; and the Realtime branch realizes low-latency, persona-consistent dialogue via generative reward modeling within an RLHF framework. On standard benchmarks, StepAudio 2.5 achieves state-of-the-art results across ASR, TTS, and Realtime, demonstrating that a singular audio-language foundation can successfully internalize the distinct deployment objectives of speech understanding, generation, and live interaction.

eess.AS↗
arXiv2026

DeformMaster: An Interactive Physics-Neural World Model for Deformable Objects from Videos

World models for deformable objects should recover not only geometry and appearance, but also underlying physical dynamics, interaction grounding, and material behavior. Learning such a model from real videos is challenging because deformable linear, planar, and volumetric objects evolve under high-dimensional deformation, noisy interactions, and complex material response. The model must therefore infer a physical state from visual observations, roll it forward under new interactions, and render the resulting dynamics with high visual fidelity. We present DeformMaster, a video-derived interactive physics-neural world model that turns real interaction videos into an online interactive model of deformable objects within a unified dynamics-and-appearance framework. DeformMaster preserves structured physical rollout while using a neural residual to compensate for unmodeled effects, grounds sparse hand motion as distributed compliant actuator for hand-continuum interaction, represents material response with spatially varying constitutive experts, and drives high-fidelity 4D appearance from the predicted physical evolution. Experiments on real-world deformable-object sequences demonstrate DeformMaster's ability to roll out future dynamics and render dynamic appearance, outperforming state-of-the-art baselines while supporting novel action rollout, material-parameter variation, and dynamic novel-view synthesis. Project page: https://can-lee.github.io/deformmaster-web/

cs.CV↗