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Wei Wang

Publications and source records attributed to Wei Wang.

At least 19 recordsLinked to original sources
arXiv2026

R2CC: Reliable and Resilient Collective Communication for Distributed Learning

Modern ML training and inference now span hundreds to tens of thousands of GPUs, where failures can waste 10-15% of GPU hours due to slow recovery. Common network errors and link fluctuations can cause up to 50% of job interruptions, triggering timeouts that often terminate entire jobs, forcing expensive checkpoint rollback during training and request reprocessing during inference. We present R2CC, a fault-tolerant communication library that provides lossless, low-overhead failover by exploiting multi-NIC hardware. R2CC performs rapid connection migration, bandwidth-aware load redistribution, and resilient collective algorithms to maintain progress under failures. We evaluate R2CC on four 8-GPU H100 InfiniBand servers and via large-scale ML simulators modeling up to 1024 GPUs with diverse failure patterns. Experiments show that R2CC is highly robust to inter-node network failures, incurring less than 1.1% training and 3% inference overhead under active failure scenarios. Compared to existing fault-tolerant approaches, R2CC reduces failure-induced overhead by up to 92% for training and 98% for inference. R2CC is open-sourced at https://github.com/r2cc-project/R2CC.

cs.DC↗
arXiv2026

ARCANA: A Benchmark for Abstraction and Analogical Reasoning

Analogical reasoning is widely considered a fundamental component of human intelligence. Few benchmarks are designed to explicitly test far analogical reasoning - reasoning between domains that are superficially dissimilar yet share deep relational or structural similarities. In this work, we introduce an analogical reasoning benchmark called Abstraction and Reasoning Corpus of ANAlogy (ARCANA). The tasks of ARCANA require performing far analogical mappings from real-world objects or physical phenomena to abstract grid-based transformations, different from the well-known ARC-AGI benchmark which primarily emphasized innate human priors. We validate ARCANA through a human study, demonstrating that humans solve these tasks reliably. In contrast, evaluations across a range of large language models show a distinct performance gap compared to humans, revealing systematic failure modes that remain challenging for current models. To address this gap, we propose ConceptSIR, a reasoning framework based on concept slippage, introspection, and reflection. Experiments demonstrate that ConceptSIR enables standard LLMs to solve several challenging ARCANA tasks that were unsolved by direct prompting in our evaluation. Overall, our results suggest that far analogical reasoning remains an open problem for contemporary LLMs. All the materials are available at sites.google.com/view/arcanacogtest. The complete codebase, including benchmark, and ConceptSIR, is publicly available at https://github.com/density-11/arcana-test. All ARCANA data and task instances can be accessed and explored at https://arcanacogtest.org/.

cs.AI↗
arXiv2026

Efficient Remote Sensing Image-Text Retrieval via Interaction-Free Recall and Text-Guided Reranking

Remote sensing (RS) image-text retrieval plays a critical role in understanding massive RS imagery. However, the dense multi-object distribution and complex backgrounds in RS imagery make it difficult to simultaneously achieve fine-grained cross-modal alignment and efficient retrieval. Existing methods either rely on complex cross-modal interactions that lead to low retrieval efficiency, or depend on large-scale vision-language adaptation, which requires massive data and computational resources. To address these issues, we develop a fast-then-fine (FTF) retrieval framework centered on interaction-free multi-granular recall and parameter-free text-guided cross-modal reranking with the Balanced Text-guided Interaction Block (BTIB). Specifically, interaction-free multi-granular recall pre-computes gallery embeddings and matches them with the corresponding query embedding without full-gallery token-level text-image interaction, while text-guided cross-modal reranking with BTIB refines local semantic correspondence within the top-K candidate set. Furthermore, an inter- and intra-modal loss is designed to jointly optimize cross-modal alignment across multi-granular representations. Extensive experiments on public benchmarks demonstrate that the FTF achieves competitive retrieval accuracy while significantly improving retrieval efficiency compared with existing methods.

cs.CV↗
arXiv2026

Structured Representation Probing for Risk Anticipation in End-to-End Autonomous Driving

End-to-end driving policies form structured internal representations of traffic, ego behavior, and road constraints. These representations offer a monitoring interface for anticipating failures, complementing risk signals from trajectories, controls, and manually designed features. We introduce the Structured Representation Probe (SRP), a lightweight monitor that reads actor, ego, and map representations from a frozen policy. SRP learns a separate linear risk direction for each source and aggregates element scores through cardinality-normalized log-mean-exp pooling, retaining sensitivity to salient evidence while reducing dependence on token count. Event-window supervision links the readout to upcoming hard failures, and auxiliary event type heads jointly train the source directions across failure modes. Online alerting uses a single generic score without modifying the driving policy. Bench2Drive experiments on DriveTransformer, UniAD, and VAD show higher frame-level PR-AUC and ROC-AUC than the evaluated runtime baselines. On DriveTransformer at a horizon of 2 seconds, ROC-AUC reaches 0.833 versus 0.715 for temporal consistency, with 2.67% readout runtime overhead. The trained probes also retain the ability to distinguish impending failures on new routes.

cs.LG↗
arXiv2026

Where the Error Lives: Spectral Representations of Learned Kohn-Sham Hamiltonians

Kohn-Sham density functional theory (DFT) is the standard method for computing the electronic structure of molecules and materials, but its self-consistent-field (SCF) iterations make it expensive for large systems and high-throughput screening. Machine-learned Hamiltonian models offer a shortcut: an equivariant network predicts the Kohn-Sham Hamiltonian matrix in one forward pass, and diagonalizing the prediction yields orbital energies, the HOMO-LUMO gap, molecular orbitals and the electron density. These models are trained to match the reference matrix entry by entry, yet a small entry-wise error does not guarantee accurate eigenvalues or eigenvectors. Two physics-informed alternatives go partway: a recently proposed wavefunction-alignment loss adds a spectral penalty without diagonalizing the prediction, and matching the electron density of the diagonalized prediction supervises it directly. Neither is complete: by representing the prediction error in the eigenbasis of the reference Hamiltonian, we show that each sees only part of the error, and that shifting the predicted orbital energies leaves the electron density exactly unchanged. The same analysis identifies a complete training signal: the cumulative local density of states (cumulative LDOS), the number of electrons at each point in space with energy below a given threshold. The cumulative LDOS is a single physical observable that contains the electron density as a special case, responds to every component of the error, determines the Hamiltonian uniquely, and yields gradients that do not vanish for large orbitalenergy errors. Fine-tuning three Hamiltonian networks on QH9 in this way, which we call FermiScope, reduces the HOMO-LUMO gap error without increasing the element-wise error. The gains carry over to out-of-distribution and molecular-dynamics splits, where the wavefunction-alignment loss helps less.

physics.chem-ph↗
arXiv2026

Venus-DefakerOne2: A Vision-Forensics Foundation Model with Broader Coverage

Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as physical spoofing, and is carried not only by images but also by videos. Despite substantial progress, a unified anti-forgery foundation model covering such a broad threat spectrum remains absent. In this paper, we present Venus-DefakerOne2, which substantially extends the forensic capability boundary along both the forgery-form and media-type dimensions. Trained on tens of millions of open-source and in-house samples, it incorporates two key designs. Tide-Style-Tuning alternates LoRA and full-parameter fine-tuning to balance computational efficiency, generalization, and hard-sample fitting while mitigating catastrophic forgetting. Entropy-based Test Adaptation determines the optimal prediction modality by analyzing prediction entropy. Venus-DefakerOne2 achieves new state-of-the-art performance across a comprehensive suite of benchmarks, including DeepFake faces (96.7%), AI-generated images (93.3%), tampered documents (87.6%), manipulated natural images (89.6%), physical spoofs (93.1%), and AI-generated videos (96.0%). It significantly outperforms both specialized expert models and its predecessor, DefakerOne, establishing itself as a powerful and versatile foundation model for binary-decision forensic tasks.

cs.CV↗
arXiv2026

JevVLA: Jev-Inspired Energy-Based Adaptation for Vision-Language-Action Policies

Vision-language-action (VLA) policies can select among discrete candidates for pick-and-place manipulation or generate continuous action chunks for motion control. Improving both forms of decision making through a common mechanism while retaining the capabilities of a pretrained policy is an important adaptation problem. Inspired by Jev's structured, probabilistic decision interface, we propose JevVLA, a lightweight energy-based adaptation framework for VLA policies. JevVLA instantiates this inspiration by modeling the relative quality of action proposals: with the base policy frozen, it learns a differentiable scalar energy conditioned on observations, task context, and actions, using execution feedback, expert corrections, or demonstration-derived preferences. JevVLA uses this single learned action preference function in two ways: its values compare discrete candidates, while its action gradients correct continuous actions or guide their generation. Candidate selection and continuous control thus share one trainable decision core. Each benchmark uses independently trained weights and fixed input/output encodings, while retaining the same core network with approximately 80,000 trainable parameters. On the held-out test sets, JevVLA improves task success over the respective matched base-policy controls by 1.28, 2.31, 1.60, 1.52, and 1.28 percentage points on MetaWorld MT50, VIMA, LanguageTable, LIBERO Plus, and CLIPort, respectively. These results support the feasibility of a lightweight, Jev-inspired energy model for adapting both discrete action selection and continuous action guidance. The JevVLA implementation will be open-sourced at https://github.com/ZJU4EmbodiedAI/JevVLA.

cs.RO↗
arXiv2026

MANTIS: Multi-Axis News Transformations for Instance-Wise Stress Testing and Worst-Case Training in Fake News Detection

Automated fake news detection is vital on social media, where fake news is easily rewritten to fool detectors. However, existing approaches typically rely on superficial writing cues or single-style text rewrites, resulting in poor robustness under diverse cosmetic modifications. We construct MANTIS-Bench, a multi-axis presentation-attack benchmark that pairs original news with rewritten views across writing style, emotional expression, and dissemination framing. We further propose MANTIS, an instance-adaptive adversarial framework enabling dynamic worst-case training tailored to each news article with zero inference-time overhead. This reframes fake news defense from heuristic format smoothing into targeted, fact-invariant credibility assessment. Experiments on MANTIS-Bench show that MANTIS reduces the Union Attack Success Rate (UASR) by 3.55 percentage points relative to SheepDog on GossipCop and consistently outperforms prior single-axis defenses on Macro-F1 while maintaining competitive clean accuracy, demonstrating the effectiveness of multi-axis and instance-adaptive worst-case training.

cs.CL↗
arXiv2026

Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning

Industrial anomaly detection demands precise reasoning over fine-grained defect patterns. However, existing multimodal large language models (MLLMs), pretrained on general-domain data, often struggle to capture category-specific anomalies, thereby limiting both detection accuracy and interpretability. To address these limitations, we propose Reason-IAD, a knowledge-guided dynamic latent reasoning framework for explainable industrial anomaly detection. Reason-IAD comprises two core components. First, a retrieval-augmented knowledge module incorporates category-specific textual descriptions into the model input, enabling context-aware reasoning over domain-specific defects. Second, an entropy-driven latent reasoning mechanism conducts iterative exploration within a compact latent space using optimizable latent think tokens, guided by an entropy-based reward that encourages confident and stable predictions. Furthermore, a dynamic visual injection strategy selectively incorporates the most informative image patches into the latent sequence, directing the reasoning process toward regions critical for anomaly detection. Extensive experimental results demonstrate that Reason-IAD consistently outperforms state-of-the-art methods across multiple tasks. The code will be publicly available at https://github.com/chenpeng052/Reason-IAD.

cs.CV↗
arXiv2026

Synergizing Wireless Foundation Models and LLMs: A Multi-Task Framework for MA Systems

While movable antennas (MAs) provide enhanced spatial flexibility through dynamic repositioning, they confront critical challenges in acquiring complete channel state information (CSI) and efficiently optimizing antenna positions. Encouragingly, wireless foundation models (WFMs) capture generalizable channel priors for high-quality CSI extrapolation, while large language models (LLMs) enable parameter-efficient, token-level optimization for wireless tasks. In this paper, we synergistically integrate a WFM and an LLM into a multi-task framework for MA systems. First, to address the mismatch between existing WFM pre-training and MA channel characteristics, we develop an MA-oriented ``ExtLWM'' based on the large wireless model (LWM) architecture. Second, we design a unified ExtLWM-Connector-LLM architecture that flexibly adapts to different MA configurations without structural redesign. Third, we reformulate discrete MA positioning as a sequential decision-making problem, where candidate positions are represented by numerical tokens and optimized using group relative policy optimization (GRPO) to exploit the serial generation capability of the LLM. Finally, a multi-stage training strategy is developed to maintain multi-task performance under limited training samples while mitigating catastrophic forgetting. Simulation results show that ExtLWM can produce high-quality channel embeddings while outperforming various benchmarks in CSI extrapolation. Meanwhile, the proposed LLM-based approach achieves a favorable trade-off between performance and computational complexity.

eess.SP↗
arXiv2026

Bidirectional Cross-frequency Domain Interaction Network for UHD Low-Light Image Enhancement

Existing Ultra-High-Definition (UHD) low-light image enhancement (LLIE) methods typically rely on either spatial- or frequency-domain modeling, or independently process different frequency components for efficient restoration. However, they often fail to jointly exploit complementary spatial--frequency information and the inherent relationship between illumination and structural information across frequencies, leading to inconsistent illumination, residual noise, and detail artifacts. In this paper, we propose a novel Bidirectional Cross-frequency Domain Interaction Network (BiFreqNet) for UHD LLIE. Specifically, BiFreqNet incorporates a Dual-domain Feature Interaction (DFI) module into the multi-scale representations of a pretrained variational autoencoder (VAE) to perform reciprocal enhancement between low- and high-frequency features. DFI consists of a Spatial and Spectral-Aware Low-Frequency Enhancement (S$^2$LFE) block and a Cross-Frequency Enhancement (CFE) block. S$^2$LFE exploits high-frequency structural cues to guide low-frequency restoration and jointly models spatial and spectral information to recover global exposure and spatially varying illumination while suppressing sensor noise. CFE further leverages the enhanced low-frequency contextual information to guide adaptive high-frequency refinement, enabling faithful reconstruction of structures and fine details. Extensive experiments on four general and UHD LLIE benchmarks demonstrate that BiFreqNet achieves competitive or superior performance compared with state-of-the-art methods.

cs.CV↗
arXiv2026

A Unified Analysis of Importance-Corrected Momentum Actor-Critic over a Markovian Trajectory

We present a generic actor-critic method that combines a ridge-regularized pathwise least-squares temporal-difference (LSTD) critic with importance-corrected momentum (ICM) actors. The critic solves a ridge-regularized system per frame by Newton-Schulz inversion and needs no stepsize, no knowledge of the mixing time, and no rank condition. Averaging stochastic policy gradients over frames of $T$ consecutive transitions, the ICM actors incur a frame-dependent gradient bias of order $\frac{1}{T}$. To our knowledge, we establish the first finite-time bounds for ICM actor-critic methods with normalized and unnormalized steps along a single Markovian trajectory. Up to a stationary-to-discounted distribution mismatch, the averaged gradient norm over $K$ updates is bounded by ${\cal O}(K^{-1/3}+\frac{1}{T})$ for normalized steps and by ${\cal O}(K^{-1/4}+\frac{1}{T})$ for unnormalized steps. We specialize these bounds to Muon, normalized stochastic gradient descent (SGD) with momentum, SGD with momentum, and AdaNorm, and compare them in the nuclear norm. With a dimension-free stepsize, Muon confines the factor $\min\{m,n\}^{1/2}$ associated with an $m\times n$ actor parameter matrix to the noise and correction terms. The comparison also characterizes when orthogonalization guarantees a larger noiseless ascent than normalization: exactly when the gradient's nuclear rank exceeds the spectral-to-Frobenius curvature ratio.

cs.LG↗
arXiv2026

Uniform boundary observability for periodic waves, I: up to the $\varepsilon^{-1}$ eigenvalue scale

Let $\mathcal{L}_\varepsilon=-\operatorname{div}(A(x/\varepsilon)\nabla)$ be a scalar divergence-form operator on a bounded $C^3$ domain, where $A$ is symmetric, uniformly elliptic, Lipschitz continuous, and periodic. For initial data spectrally truncated at the eigenvalue level $N$, we establish whole-boundary observability and projected exact controllability, uniformly in the range $N\leqδ\varepsilon^{-1}$. This extends the cutoff $N\lesssim\varepsilon^{-1/2}$ obtained by Lin and Shen (J. Eur. Math. Soc. 2022). The key input is a two-sided boundary estimate for fixed-width spectral packets of $\sqrt{\mathcal{L}_\varepsilon}$ at frequencies that satisfy $\varepsilonμ^2\ll1$. Its lower bound combines quantitative Dirichlet corrector estimates with a forcing-stable microscopic boundary Cauchy estimate. Miller's spectral packet criterion and the Hilbert uniqueness method then convert this packet estimate into the corresponding wave results, with constants independent of $\varepsilon$ and $N$.

math.AP↗
arXiv2026

RAPAC-DP: Response-Aligned Pending-Action Compensation for Diffusion Policies under Delayed Execution

Cloud-side inference expands the computational resources available to imitation-learning policies, but communication and inference latencies create a mismatch between prediction and execution. We propose RAPAC-DP, a response-aligned pending-action compensation framework for diffusion- and flow-based action generators. By conditioning on actions committed during inference, RAPAC-DP generates a full-horizon action chunk aligned with the scheduled handover time. A lightweight pending-action encoder and LoRA adapters learn this compensation while keeping the pretrained base policy frozen. We construct delay-conditioned training samples from delay-free demonstrations, requiring neither an explicit dynamics model nor additional data collected under delayed execution. In simulation benchmarks at the largest evaluated delays, RAPAC-DP retains 81.4\% of its overall delay-free performance on Kinetix and achieves a mean success rate of 63.3\% across three RoboMimic tasks. Across 450 physical trials on a Franka Panda manipulator, RAPAC-DP increases the high-delay mean success rate from 28.9\% for RTC to 60.0\% across static and conveyor-based pick-and-place tasks.

cs.RO↗
arXiv2026

Finite-horizon maintenance under geometric-process models: Quantifying model and decision uncertainty

Geometric process (GP) models are widely used for repairable systems whose inter-failure times evolve over successive failure-repair cycles. Maintenance decisions under GP models are often made with limited failure data and are subject to uncertainty in baseline selection and the resulting maintenance decision. Existing approaches typically rely on model ranking and plug-in estimates, which can understate uncertainty surrounding next-cycle replacement decisions. This paper develops a unified generalized-inference framework for finite-horizon, event-driven maintenance decision making under GP models. The proposed framework quantifies uncertainty in GP model parameters, cycle-specific reliability, hazard rate, one-cycle cost rate, and the associated optimal replacement time. For exponential and gamma baselines, the framework yields generalized confidence intervals through generalized pivotal quantities. For the Weibull baseline, it is implemented through a generalized fiducial approach. It also extends to a finite-sample discrimination procedure for quantifying baseline-selection uncertainty. Theoretical results establish the asymptotic validity of the proposed procedures and their interval estimates. Simulation study shows reliable finite-sample coverage for model parameters and nonlinear maintenance-relevant quantities, and satisfactory discrimination performance. A case study on aircraft air-conditioning failures shows that, in early cycles, replacement schedules with similar expected cost can imply materially different risk profiles, so a single plug-in recommendation may be misleading.

stat.AP↗
arXiv2026

SCI-AGENT-EXAM: Benchmarking Agents on Scientific Research Tasks

Large language model (LLM) agents are increasingly capable of supporting scientific workflows, but their capabilities across diverse research activities remain difficult to assess systematically. Existing benchmarks often focus on selected scientific capabilities or task types, limiting a comprehensive characterization of scientific agents' strengths and weaknesses in realistic research workflows. We introduce SCI-AGENT-EXAM, a workflow-oriented benchmark comprising 120 expert-curated tasks across 11 scientific fields and 7 research task types defined by the Scientific Workflow Task Taxonomy (SWTT). Each task requires both executable artifacts (e.g., code and scripts) and textual artifacts (e.g., reports and explanations). To evaluate the heterogeneous task outputs, we develop a hybrid evaluation protocol combining deterministic programmatic verification for executable artifacts with a calibrated, expert-validated LLM-based judge for textual artifacts. Beyond outcome assessment, we analyze progress and solution stability along execution trajectories to characterize how agents reach their final results. Experiments with 10 frontier LLM agents reveal substantial performance gaps: GPT-6 Astra achieves the highest overall score of 80.9, yet only 26.0% of its runs exceed the 0.95 success threshold. Further analyses uncover distinct capability profiles across scientific task types and substantial variation in execution efficiency and stability. Our code and data are available at https://github.com/SKYLENAGE-AI/Sci-Agent-Exam.

cs.CL↗
arXiv2026

J-Comm: Jacobian-Guided Latent Communication between LLM Agents

Latent communication requires both an effective continuous reasoning process and an interface through which an external model can leverage its intermediate computation. We present J-Comm, a framework comprising two core components. First, a training-free Geometric Agreement for Latent Reasoning (GEAR) reweights token candidates over existing model embeddings, which monotonically optimizes geometric consensus among candidate tokens in embedding space at each step. Second, we introduce an H+J (Hidden-and-Jacobian) communication interface that gives the Receiver two complementary views of the Sender's reasoning. The H channel supplies continuous inputs derived from feedback distributions, while the J channel conveys intermediate-state cues in vocabulary-indexed Jacobian-lens coordinates. Intuitively, H provides inputs for the Receiver to reason from, while J offers additional guidance within its computation. The Receiver reconstructs these cues using its own dictionary and injects them into intermediate layers during prefill, enabling communication without sharing key-value caches or requiring identical model widths. In Qwen3-8B to Qwen3-4B communication, J-Comm achieves the highest mean score across seven benchmarks, including 94.80% on MATH500, 54.55% on GPQA-Diamond, 73.33% on AIME2024+2025, and 62.93% success on ALFWorld. Compared with TextMAS, it improves performance by 1.29-8.59 percentage points and reduces generated text tokens by 19.2-74.8%.

cs.CL↗
arXiv2026

From entropy waves to vortex sheets for the Euler equations

We construct piecewise smooth solutions of the full compressible Euler equations that evolve from entropy waves into vortex sheets while retaining a strictly positive Taylor jump. For every integer $κ\geq1$, we obtain solutions satisfying initial material compatibility for pressure and velocity through order $κ$, whose tangential velocity jump has a nonzero leading term of order $t^{κ+1}$. These solutions are periodic in the tangential variables and admit a smooth realization in a slab with impermeable walls. We also construct solutions on a local nonperiodic tangential patch that satisfy initial material compatibility at every order, yet have a tangential velocity jump that is nonzero for every sufficiently small positive time and flat at $t=0$. The finite-order construction uses a normal Cauchy-Kowalevski problem for velocity potentials, whereas the all-order construction uses a homogeneous reduction to a smooth hyperbolic Cauchy problem.

math.AP↗