arXiv subjects

Wei Li

Publications and source records attributed to Wei Li.

At least 199 records · Page 11Linked to original sources

arXiv2026

Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality

Large Reasoning Models achieve strong performance on complex tasks but remain prone to hallucinations, particularly in long-form generation where errors compound across reasoning steps. Existing approaches to improving factuality, including abstention and factuality-driven optimization, follow a \emph{coupled exploration-commitment} paradigm, in which intermediate reasoning is unconditionally propagated to the final output, limiting fine-grained control over information selection and integration. In this paper, we propose an \textbf{Exploration-Commitment Decoupling} paradigm that disentangles knowledge exploration from final commitment, enabling models to explore with awareness while answering cautiously. We instantiate the paradigm with \textbf{Calibration-Aware Generation (CAG)}, a framework that equips models with end-to-end, calibration-aware generation capabilities, by augmenting intermediate reasoning with calibrated reliability estimates and prioritizing reliable content in final outputs. Across five long-form factuality benchmarks and multiple model families, CAG improves factuality by up to 13%, while reducing decoding time by up to 37%. Overall, our work highlights decoupling as a principled approach for more reliable long-form generation, offering directions for trustworthy and self-aware generative systems.

cs.CL↗

arXiv2026

Probe of Generic Quantum Contextuality and Nonlocal Resources for Qubits

We reveal that the entropic uncertainty relation with a quantum memory is able to intrinsically connect local generic contextuality addressed in the pioneering work by Spekkens and nonlocal quantum resources such as entanglement and Bell nonlocality. Based on the constructed optimal set for any given single-qubit state, we prove rigorously a faithful criterion to witness the generic contextuality in the scenario of local quantum state preparation. Furthermore, within the framework of quantum resource distribution, it is proved that there exist quantitative trade-off relations between local preparation contextuality and bipartite entanglement or Bell nonlocality in a shared quantum system, which are captured by two inequalities where the local and nonlocal quantum resources can coexist. The faithful criterion and quantitative inequalities are all experimentally testable, which are verified through two independent well-designed experiments on the Quafu quantum cloud platform.

quant-ph↗

arXiv2026

Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models

Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual-textual understanding, yet their reliability is critically undermined by hallucinations, i.e., the generation of factually incorrect or inconsistent responses. While recent studies using steering vectors demonstrated promise in reducing hallucinations, a notable challenge remains: they inadvertently amplify the severity of residual hallucinations. We attribute this to their exclusive focus on the decoding stage, where errors accumulate autoregressively and progressively worsen subsequent hallucinatory outputs. To address this, we propose Prefill-Time Intervention (PTI), a novel steering paradigm that intervenes only once during the prefill stage, enhancing the initial Key-Value (KV) cache before error accumulation occurs. Specifically, PTI is modality-aware, deriving distinct directions for visual and textual representations. This intervention is decoupled to steer keys toward visually-grounded objects and values to filter background noise, correcting hallucination-prone representations at their source. Extensive experiments demonstrate PTI's significant performance in mitigating hallucinations and its generalizability across diverse decoding strategies, LVLMs, and benchmarks. Moreover, PTI is orthogonal to existing decoding-stage methods, enabling plug-and-play integration and further boosting performance. Code is available at: https://github.com/huaiyi66/PTI.

cs.CV↗

arXiv2026

SVOM/VT: Flight Model Verification and Pre-launch Testing

This paper presents pre-launch testing and calibration results for the SVOM/VT (Space-based Variable Objects Monitor, Visible Telescope) Flight Model (FM), validating its performance under simulated space conditions through thermal vacuum cycling, energy concentration analysis, stray light suppression, and CCD/electronics calibrations (gain, noise, quantum efficiency). The results confirm full compliance with design requirements: stray light suppression achieves point-source transmittance $<10^{-7}$ at $30^\circ$ off-axis, thermal control maintains stable CCD temperatures ($-75^\circ$C for the red channel, $-65^\circ$C for the blue channel), and detection sensitivity meets the limiting magnitude of 22.50 (SNR $>$ 3 with 300 seconds exposure). Early in-orbit tests further validate performance, yielding limiting magnitudes of 22.70 (V-band, red) and 22.78 (blue), consistent with pre-launch specifications.

astro-ph.IM↗

arXiv2026

MEASER: Malware embedding attacks on open-source LLMs

Open-source large language models (LLMs) have demonstrated considerable dominance over proprietary LLMs in resolving neural processing tasks, thanks to the collaborative and sharing nature. Although full access to source codes, model parameters, and training data lays the groundwork for transparency, we argue that such a full-access manner is vulnerable to MEAs, and their ill-effects are not fully understood. In this paper, we conduct a systematic formalization for MEAs on open-source LLMs by enumerating all possible threat models associated with adversary objectives, knowledge, and capabilities. Therein, the threat posed by adversaries with internal knowledge, who inject payloads and triggers during the model sharing phase, is of practical interest. We go even further and propose the first MEA against open-source LLMs, dubbed MEASER, which wields impacts through identifying targeted parameters, embedding payloads, injecting triggers, and executing payloads sequentially. Particularly, MEASER enhances the attack robustness against quantization and parameter-efficient fine-tuning (PEFT) by employing the Magnitude-Adaptive Relative Quantization Index Modulation (MAR-QIM) mechanism, synergized with LDPC codes and spread spectrum modulation. In addition, to achieve stealthiness, MEASER devises the performance-aware importance metric to identify targeted parameters with the least degradation of model performance. Extensive experiments on four popular open-source LLMs show that the stealth rate of MEASER outperforms existing MEAs (for general DNNs) significantly, while consistently achieving a 0 bit error rate (BER) in all settings. Moreover, MEASER also maintains superior stealthiness on quantized models. We appeal for investigations on countermeasures against MEASER in view of the significant attack effectiveness.

cs.CR↗

arXiv2026

Wafer-scale hybrid molecular beam epitaxy of BaTiO3 and SrTiO3 on silicon

The integration of epitaxial barium titanate (BTO) on silicon represents a highly promising pathway for next-generation, energy-efficient photonic integrated circuits due to BTO's exceptionally high Pockels coefficients. However, the scalable epitaxy of BTO on Si remains hindered by complex stoichiometric control and slow growth rates. In this work, we demonstrate the continuous, uniform wafer-scale growth of high-quality BTO films on SrTiO3 (STO)-buffered 4-inch Si(001) wafers using a fully hybrid molecular beam epitaxy (hMBE) approach. By utilizing titanium tetraisopropoxide as a titanium precursor, we achieve a self-regulating, adsorption-controlled layer-by-layer growth at rates exceeding 75 nm/h, while maintaining an atomically sharp and structurally coherent BTO/STO interface. We systematically compare the structural, ferroelectric, and electro-optic (EO) properties of fully hMBE-grown BTO with those deposited via pulsed laser deposition (PLD) on identical STO/Si templates. While both techniques yield high-quality c-domain dominated films, the optimized hMBE-grown BTO exhibits superior crystallinity and a larger effective EO coefficient of 248 pm/V, surpassing that of the PLD-grown films (220 pm/V). These results highlight the advantages of the fully hMBE approach as a scalable, deterministic, and high-performance materials platform for wafer-scale integrated ferroelectric photonics.

cond-mat.mtrl-sci↗

arXiv2026

Knowledge Visualization: A Benchmark and Method for Knowledge-Intensive Text-to-Image Generation

Recent text-to-image (T2I) models have demonstrated impressive capabilities in photorealistic synthesis and instruction following. However, their reliability in knowledge-intensive settings remains largely unexplored. Unlike natural image generation, knowledge visualization requires not only semantic alignment but also strict adherence to domain knowledge, structural constraints, and symbolic conventions, exposing a critical gap between visual plausibility and scientific correctness. To systematically study this problem, we introduce KVBench, a curriculum-grounded benchmark for evaluating knowledge-intensive T2I generation. KVBench covers six senior high-school subjects: Biology, Chemistry, Geography, History, Mathematics, and Physics. The benchmark consists of 1,800 expert-curated prompts derived from over 30 authoritative textbooks. Using this benchmark, we evaluate 14 state-of-the-art open- and closed-source models, revealing substantial deficiencies in logical reasoning, symbolic precision, and multilingual robustness, with open-source models consistently underperforming proprietary systems. To address these limitations, we further propose KE-Check, a two-stage framework that improves scientific fidelity via (1) Knowledge Elaboration for structured prompt enrichment, and (2) Checklist-Guided Refinement for explicit constraint enforcement through violation identification and constraint-guided editing. KE-Check effectively mitigates scientific hallucinations, narrowing the performance gap between open-source and leading closed-source models. Data and codes are publicly available at https://github.com/zhaoran66/KVBench.

cs.CV↗

arXiv2026

Magnon Damping as a Probe of Kondo Coupling in Magnetically Ordered Systems

In $d$-electron systems, there can also be intricate interplay between Kondo coupling and magnetic interactions as that in $f$-electron systems, but the underlying mechanism remains elusive. Here, using inelastic neutron scattering, we investigate the temperature evolution of the low-energy spin waves (magnons) in a metallic van der Waals ferromagnet Fe$_{3-x}$GeTe$_{2}$, and observe that the magnon damping diverges at both low and high temperatures and exhibits a minimum at an intermediate temperature. These behaviours are described by a formula that combines logarithmic and power-law terms, representing the dominant contributions from Kondo coupling and thermal fluctuations, respectively. These findings can be explained by considering electron-magnon scattering of spin-flip type within the ferromagnetic Kondo-Heisenberg lattice model, distinct from the original Kondo effect which only considers the coupling between itinerant electrons and isolated impurity spins. These results unveil the intriguing interplay between itinerant electrons and spin waves in metallic 3$d$-electron systems with magnetic order, and provide magnon damping as a new effective probe of Kondo coupling in metallic quantum magnets, thereby opening new avenues for exploring Kondo physics from the magnon perspective.

cond-mat.str-el↗

arXiv2026

Robust Principal Component Completion

Robust principal component analysis (RPCA) seeks a low-rank component and a sparse component from their summation. Yet, in many applications of interest, the sparse foreground actually replaces, or occludes, elements from the low-rank background. To address this mismatch, a new framework is proposed in which the sparse component is identified indirectly through determining its support. This approach, called robust principal component completion (RPCC), is solved via variational Bayesian inference applied to a fully probabilistic Bayesian sparse tensor factorization. Convergence to a hard classifier for the support is shown, thereby eliminating the post-hoc thresholding required of most prior RPCA-driven approaches. Experimental results reveal that the proposed approach delivers near-optimal estimates on synthetic data as well as robust foreground-extraction and anomaly-detection performance on real color video and hyperspectral datasets, respectively. Source implementation and Appendices are available at https://github.com/WongYinJ/BCP-RPCC.

cs.CV↗

arXiv2026

Atomic-scale visualization of d-wave altermagnetism

Altermagnetism is a newly identified magnetic phase, distinct from conventional ferromagnetism and antiferromagnetism. It exhibits no net magnetization while breaking time-reversal symmetry. Although its momentum-space signatures are established, direct real-space visualization of its defining rotational-symmetry breaking remains missing. Here, using scanning tunnelling microscopy, we provide atomic-scale real-space evidence for altermagnetism in CsV2Se2O. Utilizing intrinsic spin defects as probes, we directly visualize the hallmark symmetry breaking through unidirectional electronic patterns and elliptical charging rings, both tied to the alternating spin texture. Moreover, adjacent spin-defect lines exhibit opposite spins and long-range antiferromagnetic coupling, hinting at a novel spin order. Our work moves the field from momentum-space probes to direct real-space visualization, opening a path to explore how this unconventional magnetic order couples to other quantum states.

cond-mat.mtrl-sci↗

arXiv2026

V2E: Validating Smart Contract Vulnerabilities through Profit-driven Exploit Generation and Execution

Smart contracts are a critical component of blockchain systems. Due to the large amount of digital assets carried by smart contracts, their security is of critical importance. Although numerous tools have been developed for detecting smart contract vulnerability, their effectiveness remains limited, particularly due to the high false positives included in the reported results. Therefore, developers and auditors are often overwhelmed with manually verifying the reported issues. A fundamental reason behind this is that while a reported vulnerability satisfies specific vulnerable patterns, it may not actually be exploitable, either because the vulnerable code cannot be triggered or it does not result in any financial loss. In this paper, we propose V2E, a new framework for validating whether a reported vulnerability is truly exploitable. The core idea of V2E is to automatically generate executable Proof-of-Concept Exploit (PoC for short), and then assess if the vulnerability could be triggered and incur any real damage (i.e., causing financial loss) by the PoC. While LLMs have shown proficiency in PoC generation, achieving our task is by no means trivial. In detail, it is difficult for LLM to: (1) generate and update PoC to trigger a specific vulnerability, (2) evaluate the PoC's effectiveness to validate exploitable vulnerability. To this end, V2E automates the whole process through a novel combination of PoC generation, validation, and refinement: (1) Firstly, V2E generates targeted PoCs by analyzing potential vulnerability paths. (2) Then, V2E verifies the validity of PoCs through triggerability and profitability analysis. (3) In addition, V2E iteratively refines the generated PoC based on PoC execution feedback, therefore, increasing the chance to confirm the vulnerability. Evaluation on 264 manually labeled contracts shows that V2E outperforms the baseline approach.

cs.SE↗

arXiv2026

SignDPO: Multi-level Direct Preference Optimisation for Skeleton-based Gloss-free Sign Language Translation

We present SignDPO, a novel multi-level Direct Preference Optimisation (DPO) framework designed to enhance the alignment of skeleton-based Sign Language Translation. While current skeleton-based models have made significant progress using Maximum Likelihood Estimation, they are primarily constrained by an imitation-based paradigm that lacks discriminative sensitivity to the fine-grained spatio-temporal nuances of sign language, often leading to semantic drift. To address this, SignDPO shifts the optimisation goal from simple sequence mimicry to structured preference alignment across spatial, temporal, and linguistic dimensions. Our framework involves three key designs. First, we introduce a hierarchical perturbation strategy to construct spatial and temporal non-preferred samples at both global and local granularities automatically. Second, we propose a self-guiding mechanism that leverages decoder cross-attention scores to identify and perturb semantically salient skeletal regions, forcing the model to distinguish genuine sign signals from structural distortions. Third, we establish an automated language-level preference generator by fine-tuning a dedicated perturbation model, capturing complex output-level failure modes without manual annotation. Extensive experiments on three widely adopted benchmarks, CSL-Daily, How2Sign, and OpenASL, demonstrate that SignDPO consistently outperforms state-of-the-art gloss-free methods and even rivals established gloss-based ones. Our results suggest that multi-level preference alignment is a powerful paradigm for bridging the gap between high-entropy skeletal trajectories and discrete linguistic semantics.

cs.CL↗

arXiv2026

The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview

This paper provides a review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes. The challenge aims to recover high-resolution (HR) images from low-resolution (LR) counterparts generated through unknown degradations with a x4 scaling factor while ensuring the models remain executable on mobile devices. The objective is to develop effective and efficient network designs or solutions that achieve state-of-the-art real-world image super-resolution performance. The track of the challenge evaluates performance using a weighted combination of image quality assessment (IQA) score and speedup ratios. The competition attracted 108 registrants, with 16 teams achieving a valid score in the final ranking. This collaborative effort advances the performance of mobile real-world image super-resolution while offering an in-depth overview of the latest trends in the field.

cs.CV↗

arXiv2026

LongBench: Evaluating Robotic Manipulation Policies on Real-World Long-Horizon Tasks

Robotic manipulation policies often degrade over extended horizons, yet existing benchmarks provide limited insight into why such failures occur. Most prior benchmarks are either simulation-based or report aggregate success, making it difficult to disentangle the distinct sources of temporal difficulty in real-world execution. We introduce LongBench, a real-world benchmark for evaluating long-horizon manipulation. LongBench consists of over 1,000 real-world episodes, covering two complementary regimes: Context-Independent (fully observable) and Context-Dependent (ambiguity-driven). By organizing tasks into capability- and ambiguity-specific subsets, LongBench enables mechanism-aware evaluation of execution robustness, temporal consistency, and context-dependent reasoning. Evaluating six state-of-the-art policies reveals that long-horizon performance is not governed by a single factor. We observe that performance in fully observable settings is more strongly associated with execution robustness, while contextual difficulty varies across tasks and is not consistently improved by memory-based methods. We hope that LongBench serves as a useful benchmark for studying long-horizon manipulation and for developing policies with stronger robustness across both execution and contextual challenges.

cs.RO↗

arXiv2026

Inductive Convolution Nuclear Norm Minimization for Tensor Completion with Arbitrary Sampling

The recently established Convolution Nuclear Norm Minimization (CNNM) addresses the problem of \textit{tensor completion with arbitrary sampling} (TCAS), which involves restoring a tensor from a subset of its entries sampled in an arbitrary manner. Despite its promising performance, the optimization procedure of CNNM needs performing Singular Value Decomposition (SVD) multiple times, which is computationally expensive and hard to parallelize. To address the issue, we reformulate the optimization objective of CNNM from the perspective of convolution eigenvectors. By introducing pre-learned convolution eigenvectors which are shared among different tensors, we propose a novel method called Inductive Convolution Nuclear Norm Minimization (ICNNM), which bypasses the SVD step so as to decrease significantly the computational time. In addition, due to the extra prior knowledge encoded in the pre-learned convolution eigenvectors, ICNNM also outperforms CNNM in terms of recovery performance. Extensive experiments on video completion, prediction and frame interpolation verify the superiority of ICNNM over CNNM and several other competing methods.

cs.CV↗

arXiv2026

Convolutionally Low-Rank Models with Modified Quantile Regression for Interval Time Series Forecasting

The quantification of uncertainty in prediction models is crucial for reliable decision-making, yet remains a significant challenge. Interval time series forecasting offers a principled solution to this problem by providing prediction intervals (PIs), which indicates the probability that the true value falls within the predicted range. We consider a recently established point forecasts (PFs) method termed Learning-Based Convolution Nuclear Norm Minimization (LbCNNM), which directly generates multi-step ahead forecasts by leveraging the convolutional low-rankness property derived from training data. While theoretically complete and empirically effective, LbCNNM lacks inherent uncertainty estimation capabilities, a limitation shared by many advanced forecasting methods. To resolve the issue, we modify the well-known Quantile Regression (QR) and integrate it into LbCNNM, resulting in a novel interval forecasting method termed LbCNNM with Modified Quantile Regression (LbCNNM-MQR). In addition, we devise interval calibration techniques to further improve the accuracy of PIs. Extensive experiments on over 100,000 real-world time series demonstrate the superior performance of LbCNNM-MQR.

cs.LG↗

arXiv2026

Fluctuating Pair Density Wave in Finite-temperature Phase Diagram of the $t$-$t^\prime$ Hubbard Model

The Hubbard model and its extensions are canonical theoretical frameworks for understanding correlated electronic states, including those in high-$T_c$ cuprates. Here, we use state-of-the-art thermal tensor network method to map out the temperature-doping phase diagram of the $t$-$t^\prime$ Hubbard model. On the electron-doped side, we find a $d$-wave superconducting (dSC) regime, supporting the scenario of high-$T_c$ superconductivity. In contrast, on the hole-doped side, no robust dSC phase is detected. Instead, a finite-temperature regime dominated by strong pair-density-wave (PDW) fluctuations emerges, which may eventually give way to charge density wave order upon further cooling. The PDW state exhibits inter-arc pairing with net momentum near $(0, π)$, distinct from the zero-momentum pairing in conventional dSC. Furthermore, these fluctuating PDW states occupy the lower portion of the pseudogap regime on the hole-doped side. We provide a comprehensive finite-temperature perspective consistent with previous ground-state studies, shedding new light on pairing instabilities and exotic electronic states in high-$T_c$ superconductors.

cond-mat.str-el↗

arXiv2026

Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets?

While recent video world models can generate highly realistic videos, their ability to perform semantic reasoning and planning remains unclear and unquantified. We introduce Target-Bench, the first benchmark that enables comprehensive evaluation of video world models' semantic reasoning, spatial estimation, and planning capabilities. Target-Bench provides 450 robot-collected scenarios spanning 47 semantic categories, with SLAM-based trajectories serving as motion tendency references. Our benchmark reconstructs motion from generated videos with a metric scale recovery mechanism, enabling the evaluation of planning performance with five complementary metrics that focus on target-approaching capability and directional consistency. Our evaluation result shows that the best off-the-shelf model achieves only a 0.341 overall score, revealing a significant gap between realistic visual generation and semantic reasoning in current video world models. Furthermore, we demonstrate that fine-tuning process on a relatively small real-world robot dataset can significantly improve task-level planning performance.

cs.CV↗