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Lin Wu

Publications and source records attributed to Lin Wu.

At least 19 recordsLinked to original sources
arXiv2026

GLaQ: Grounding Latent Queries in Visual Evidence for Multimodal Reasoning

Chain-of-thought reasoning has substantially improved the problem-solving capabilities of multimodal large language models. Fine-grained visual evidence, however, remains difficult to preserve and reuse across text-based reasoning steps. To address this limitation, tool-augmented thinking-with-images methods maintain visual access externally by revisiting or manipulating the image, but require predefined tools and additional inference-time processing. As an internal alternative, continuous visual latent reasoning retains intermediate computation in hidden states. However, its prevailing autoregressive construction makes each latent state depend on its predecessors, so later states may repeat information already present in the latent sequence rather than capture complementary visual details. We introduce GLaQ, a grounded latent-query framework that replaces sequential latent rollout with a fixed set of context-conditioned queries grounded in the original visual tokens. The grounded queries are reinjected for answer generation, providing direct and coordinated access to source visual evidence. We train GLaQ with localized-view supervision followed by reinforcement learning under task-level rewards. Across five benchmarks for fine-grained visual understanding and perception, GLaQ-7B gains 5.99--9.66\% over its base model and leads all compared visual latent methods, suggesting that direct query-to-image grounding can recover localized evidence from the full image without external visual operations or autoregressive latent rollouts.

cs.CV↗
arXiv2026

Nonlinearity-induced chirality in resonant metasurfaces

Chiral metasurfaces conventionally rely on structural or extrinsic symmetry breaking, while nonlinear circular dichroism is usually treated as a resonantly enhanced consequence of an already chiral linear response. Here, we show that chirality can instead be induced by the nonlinear susceptibility of an otherwise achiral resonant metasurface. We study a membrane metasurface composed of circular holes in a square lattice made of a cubic nonlinear material such as crystalline silicon. Under normal incidence, the structure is linearly achiral and supports high-Q quasi-guided resonances exhibiting identical responses to left- and right-circularly polarized light. Using quasi-normal-mode expansion and temporal coupled-mode theory extended to the nonlinear regime, we demonstrate that a relative rotation between the principal axes of the cubic nonlinear susceptibility tensor and the metasurface axes produces unequal third-harmonic generation for opposite circular polarizations. We formulate the resulting effect of nonlinear circular dichroism without geometrical chirality in terms of helicity phase-matching criteria and discuss why such a mechanism is forbidden for second-harmonic generation in materials with second-order nonlinearity. The resulting nonlinear circular dichroism reaches 99.5% and follows a simple dependence on the relative angle, switching from fourfold to eightfold periodicity when resonances at the harmonic frequency are additionally excited. Our results establish nonlinearity-induced chirality as a fundamentally new route to chiral photonic responses without geometrical symmetry breaking, opening opportunities for nonlinear chiral optics in planar CMOS-compatible metasurfaces.

physics.optics↗
arXiv2026

Temporal Fourier Optics Reveals Hidden Hybridized Light-Matter States

Spectral measurements provide fundamental insights into wave systems by revealing resonances, mode hybridization, and light-matter interactions. However, intrinsic dissipation and measurement-related spectral broadening often obscure the spectral signatures of the underlying hybridized light-matter states. Here, we establish a temporal Fourier optics framework based on a space-time Fourier correspondence, which interprets spectral broadening as the Fourier counterpart of temporal attenuation. This perspective introduces a temporal point-spread function (TPSF) that enables direct, synthesis-free reconstruction of the underlying spectral response from experimentally measured spectra by compensating for effective temporal decay before transformation back to the frequency domain. We experimentally validate the framework using deterministic single-molecule Au nanosphere dimers and open Au@Ag nanorod- and nanotriangle-based plasmonic nanocavities coupled to J-aggregate excitons. Across these distinct platforms, TPSF consistently resolves hidden upper and lower polaritonic branches, revealing hybridized light-matter states and strong coupling that remain inaccessible in conventional scattering spectra. The reconstructed spectra agree closely with the recently developed complex-frequency formalism while providing a substantially simpler and experimentally accessible implementation. More broadly, temporal Fourier optics establishes a general framework for recovering dissipation-obscured spectral information, opening new opportunities for spectroscopy, imaging, sensing, and inverse wave measurements across photonics and wave physics.

physics.optics↗
arXiv2026

Twist Engineering for Reconfigurable Optical and Optoelectronic Devices

Reconfigurable optical and optoelectronic devices require compact tuning mechanisms capable of reshaping electronic, excitonic, polaritonic, and photonic responses without rebuilding the underlying nanostructure. Against this backdrop, twist has emerged as a powerful geometric degree of freedom that reconfigures interlayer coupling, momentum matching, symmetry, radiation channels, and chiral response by simply rotating adjacent two-dimensional layers or photonic lattices. In this Review, we survey twist-engineered optical and optoelectronic devices spanning van der Waals materials and photonic platforms. We first review the current landscape of twist-angle metrology, classifying existing characterization approaches into three complementary categories: direct structural imaging, methods based on moir\'e periodicity and morphological features, and techniques that infer the twist angle from spectroscopic or electronic responses. We then survey the principal technological routes for twist-angle control, including deterministic transfer and growth strategies, atomic force microscopy (AFM)-assisted manipulation, quantum twisting microscopy (QTM), microelectromechanical systems (MEMS), and emerging non-contact approaches, highlighting their respective capabilities, limitations, and prospects for programmable and scalable moir\'e photonic platforms. Finally, we discuss the future evolution of twist engineering from the fabrication of individual twisted structures toward dynamically reconfigurable, feedback-controlled, and manufacturable photonic systems. We further highlight MEMS-based rotation, piezoelectric actuation, and micro-LiDAR as representative enabling technologies and emerging applications within this broader landscape.

physics.optics↗
arXiv2026

Chiral-Structured Superconductors TrX4 (Tr = Rh, Ir; X = Ge, Si): A Platform for Mixed-Parity Pairing and Topological States

Chiral-structured superconductors, with simultaneous broken mirror and inversion symmetries, promote unconventional superconductivity through parity-mixing mechanisms. Yet a few bulk chiral-structured superconductors are known, partly due to the difficulty in directly determining their atomic-scale chirality. Here we report three chiral-structured superconductors, , RhGe4, IrGe4, and IrSi4, synthesized under high pressure, with Tc values of about 1.6 K, 1.1 K, and 2.5 K, respectively.Using atomic resolution Cs-corrected scanning transmission electron microscopy (STEM) combined with X-ray diffraction characterizations, we directly confirm their chiral structure (space group P3121). This real space imaging approach overcomes ambiguities in traditional diffraction based methods. These materials exhibit type-II superconductivity, and the enhancement of spin-orbit coupling (SOC) leads to the emergence of mixed parity pairing. Calculations also reveal symmetry protected Weyl points near the Fermi level, which is robust against the SOC. Our work not only expands the family of chiral-structured superconductors but also demonstrates the indispensable role of STEM in directly determining chiral crystal structures. These materials thus offer a clean platform to explore the interplay among structural chirality, SOC, mixed parity superconductivity, and topological quantum phenomena.

cond-mat.supr-con↗
arXiv2026

Beyond Global Replanning: Hierarchical Recovery for Cross-Device Agent Systems

Real-world computer-use tasks often span multiple applications and devices, requiring agents to coordinate heterogeneous environments under dynamic runtime failures. Existing multi-device agent systems support task decomposition and cross-device assignment, but recovery remains largely coarse-grained: when execution fails, they typically retry the same strategy, reassign the subtask, or revise the global plan, without systematically modeling the device-local strategy space. This limits their ability to distinguish failures that can be repaired within the current device from those that require cross-device replanning. We propose \textbf{H-RePlan}, a hierarchical replanning framework for multi-device agents with unified API--CLI--GUI execution. H-RePlan equips each device with interchangeable execution strategies and separates device-local strategy recovery from orchestrator-level global replanning through a compact cross-layer failure abstraction. To evaluate this capability, we introduce \textbf{HeraBench}, a fault-injected benchmark that constructs cross-device workflows over Linux and Android devices and injects strategy- and device-level failures. Experiments show that H-RePlan substantially outperforms single-strategy and coarse-grained multi-device baselines, achieving higher completion, instruction adherence, and perfect-pass rates while reducing the token cost required for reliable end-to-end success. These results demonstrate that scope-aware hierarchical recovery is essential for robust multi-device agent execution.

cs.CL↗
arXiv2026

Beyond Consistency: Preserving Temporal Structure in Zero-Shot Video Editing

Existing zero-shot video editing methods rely on pre-trained diffusion models, successfully achieving spatial control and basic temporal consistency but fundamentally fail to preserve the video's original temporal structure.This distinction is critical: temporal consistency ensures visual smoothness, but temporal structure dictates the video's high-level narrative, rhythm, and semantic flow. Without this preservation, the edited output, especially for long videos with complex semantic variations, becomes narratively incoherent and semantically ambiguous. To address this limitation, we introduce a novel zero-shot editing approach that, for the first time, explicitly focuses on preserving the source video's temporal structure. We achieve this by adaptively partitioning the video into semantically distinct clips based on feature similarity and selecting a representative anchor frame for each clip. To enhance both intra-clip fidelity and computational efficiency, we design a clip-adaptive token merging strategy which leverages the anchor's semantic dominance to stabilize the editing. Furthermore, we employ an alternating combination strategy that ensures seamless inter-clip transitions while maintaining semantic distinction. Extensive experiments demonstrate that our method achieves state-of-the-art results, successfully balancing the preservation of original temporal structure with computational efficiency, and setting a new benchmark for zero-shot video editing fidelity.

cs.CV↗
arXiv2026

Observation of the Inherent Chiral Smith-Purcell Effect via Symmetry Breaking

The Smith-Purcell effect arises when charged particles move near a periodic structure, emitting radiation. Conventional approaches for generating chiral Smith-Purcell radiation rely on metasurface phase engineering or resonant mode interference, typically producing narrow-band, weakly chiral emission. Here, we introduce a resonance-interference-free mechanism that leverages the properties of the charged particles themselves. Using a non-chiral, non-resonant silicon grating, we demonstrate broadband, tunable Smith-Purcell radiation with high chirality, achieving a record-high degree of polarization of 0.87. This is enabled by converting the transverse spin angular momentum of electron-induced evanescent waves into a longitudinal form, producing opposite chirality at different azimuthal angles. Beam twisting or displacement offers precise control over chirality, paving the way for compact chiral light sources, advanced X-ray imaging, and integrated particle diagnostics platforms.

physics.optics↗
arXiv2026

ChartAct: A Benchmark for Dynamic Chart Understanding

Charts are widely used to present complex data for analysis and decision making. Existing chart understanding benchmarks mainly focus on static charts, but real-world charts are often dynamic and interactive. Key information may only appear after actions such as hovering, clicking, zooming, or dragging. Dynamic chart understanding therefore requires models to identify visible content, choose proper interactions, and reason over changing chart states. To evaluate this ability, we propose ChartAct, an interactive benchmark for dynamic chart understanding. ChartAct collects and filters 673 dynamic charts from 8 real chart websites, covers 7 common chart types, and constructs 1,440 high-quality question-answer samples. Each sample is instantiated in two environments, Dynamic Chart and Dashboard Chart, to evaluate dynamic chart understanding under different contexts. Based on ChartAct, we systematically evaluate 11 advanced multimodal models and GUI agents. Experimental results show that existing models still have clear limitations in dynamic chart understanding. The strongest model, Claude-Opus-4.7, achieves an average success rate of 84.5\%, while most models remain below 60\%. We also conduct detailed failure attribution and case analysis. ChartAct provides a new benchmark for studying chart understanding in real interactive environments. Codes at https://github.com/wulin-wulin/OSWorld_Chart

cs.CV↗
arXiv2026

Dual-state control of lasing and absorption via conjugate exceptional points

Lasing and coherent perfect absorption (CPA) are time-reversed manifestations of non-Hermitian light-matter interactions. While exceptional points (EPs) have been extensively explored for controlling lasing dynamics, their role in the concurrent manipulation of lasing and absorption remains largely unexplored. Here, we demonstrate the emergence of a pair of conjugate second-order EPs (EP2s) in a gain-loss-engineered Fabry-P\'erot microcavity that enables dual-state operation involving both coherent amplification and absorption. By spatially tailoring gain and loss, we realize two such EP2s: one associated with the coalescence of coupled scattering-matrix poles and the other, its conjugate, arising from the coalescence of corresponding zeros, thereby directly linking the amplifying and absorbing branches of the system. Leveraging the branch-point topology of these conjugate EP2s, we adiabatically encircle them in the gain-loss parameter space and achieve deterministic state permutation, enabling multiple reconfigurable switching schemes for the controlled generation and manipulation of threshold lasing and CPA. Notably, simultaneous encirclement of these conjugate EP2s yields a coordinated dual-state switching protocol, resulting in a frequency-matched coexistence of lasing and absorption responses within the same cavity. Our results establish an EP-based framework for unified and flexible control of lasing and absorption in non-Hermitian photonic systems.

physics.optics↗
arXiv2026

PRISM: A Benchmark for Programmatic Spatial-Temporal Reasoning

Programmatic video generation through code offers geometric precision and temporal coherence beyond pixel-level diffusion models, yet rigorously evaluating whether language models can produce spatially correct animated outputs remains an open problem. We introduce PRISM, a large-scale benchmark of 10,372 human-calibrated instruction-code pairs (20 times larger than prior programmatic video generation benchmarks), grounded in real-world knowledge visualization scenarios across English and Chinese and spanning 437 subject categories. We further propose a funnel-style evaluation framework with four complementary metrics: Code-Level Reliability for executability, Spatial Reasoning for layout correctness over full animation sequences, and Prompt-Aware Dynamic Visual Complexity (PADVC) and Temporal Density (TD) for diagnosing dynamic expression and temporal activity. Systematic evaluation of seven mainstream LLMs reveals a striking Execution-Spatial Gap: the average drop from execution success rate to spatial pass rate is approximately 41%, showing that runnable code does not necessarily yield spatially coherent visual output. These findings show that programmatic video generation evaluation should go beyond executability. PRISM provides a principled benchmark for advancing spatially coherent code generation.

cs.AI↗
arXiv2026

Kernel of Scott modules and Brauer indecomposability

Let $k$ be an algebraically closed field of prime characteristic $p$. Let $G$ be a finite group. We investigate the Brauer indecomposability of Scott $kG$-modules in relation to the kernel of modules. We generalize a criterion for Brauer indecomposability. We also prove that, in certain cases, Brauer indecomposability of a Scott $kG$-module can be lifted from that of a Scott module over a $p$-local subgroup.

math.RT↗
arXiv2026

Plug-and-play Class-aware Knowledge Injection for Prompt Learning with Visual-Language Model

Prompt learning has become an effective and widely used technique in enhancing vision-language models (VLMs) such as CLIP for various downstream tasks, particularly in zero-shot classification within specific domains. Existing methods typically focus on either learning class-shared prompts for a given domain or generating instance-specific prompts through conditional prompt learning. While these methods have achieved promising performance, they often overlook class-specific knowledge in prompt design, leading to suboptimal outcomes. The underlying reasons are: 1) class-specific prompts offer more fine-grained supervision compared to coarse class-shared prompts, which helps prevent misclassification of data from different classes into a single class; 2) compared to class-specific prompts, instance-specific prompts neglect the richer class-level information across multiple instances, potentially causing data from the same class to be divided into multiple classes. To effectively supplement the class-specific knowledge into existing methods, we propose a plug-and-play Class-Aware Knowledge Injection (CAKI) framework. CAKI comprises two key components, i.e., class-specific prompt generation and query-key prompt matching. The former encodes class-specific knowledge into prompts from few-shot samples that belong to the same class and stores the learned prompts in a class-level knowledge bank. The latter provides a plug-and-play mechanism for each test instance to retrieve relevant class-level knowledge from the knowledge bank and inject such knowledge to refine model predictions. Extensive experiments demonstrate that our CAKI effectively improves the performance of existing methods on base and novel classes. Code is publicly available at \href{https://github.com/yjh576/CAKI}{this https URL}.

cs.CV↗
arXiv2026

ESIA: An Energy-Based Spatiotemporal Interaction-Aware Framework for Pedestrian Intention Prediction

Recent advances in autonomous driving have motivated research on pedestrian intention prediction, which aims to infer future crossing decisions and actions by modeling temporal dynamics, social interactions, and environmental context. However, existing studies remain constrained by oversimplified multi-agent interaction patterns, opaque reasoning logic, and a lack of global consistency in behavioral predictions, which compromise both robustness and interpretability. In this work, we propose ESIA (Energy-based Spatiotemporal Interaction-Aware framework), a novel Conditional Random Field (CRF)-based paradigm. We cast the intention prediction task as a structured prediction problem over a unified graph-based representation, treating pedestrians and the environment as spatiotemporal nodes. To characterize their distinct roles, we assign unary potentials to nodes to capture individual intentions, and pairwise potentials to edges to encode social and environmental interactions. These potentials are integrated into a unified global energy function to ensure scene-level consistency across behavioral predictions. To further constrain inference without ground-truth supervision, we introduce structural consistency terms to penalize logical contradictions. This optimization is efficiently solved via a novel Unary-Seeded Simulated Annealing (U-SSA) algorithm, which leverages high-confidence unary priors to rapidly converge to a high-quality solution. Extensive experiments on standard benchmarks demonstrate that ESIA achieves state-of-the-art performance with improved interpretability over existing methods.

cs.CV↗
arXiv2026

Personalized Cell Segmentation: Benchmark and Framework for Reference-Guided Cell Type Segmentation

Accurate cell segmentation is critical for biological and medical imaging studies. Although recent deep learning models have advanced this task, most methods are limited to generic cell segmentation, lacking the ability to differentiate specific cell types. In this work, we introduce the Personalized Cell Segmentation (PerCS) task, which aims to segment all cells of a specific type given a reference cell. To support this task, we establish a benchmark by reorganizing publicly available datasets, yielding 1,372 images and over 110,000 annotated cells. As a pioneering solution, we propose PerCS-DINO, a framework built on the DINOv2 backbone. By integrating image features and reference embeddings via a cross-attention transformer and contrastive learning, PerCS-DINO effectively segments cells matching the reference. Extensive experiments demonstrate the effectiveness of the proposed PerCS-DINO and highlight the challenges of this new task. We expect PerCS to serve as a useful testbed for advancing research in cell-based applications.

cs.CV↗
arXiv2026

Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science

Recent advancements in Large Language Models (LLMs) have greatly extended the capabilities of Multi-Agent Systems (MAS), demonstrating significant effectiveness across a wide range of complex and open-ended domains. However, despite this rapid progress, the field still relies heavily on empirical trial-and-error. It lacks a unified and principled scientific framework necessary for systematic optimization and improvement. This bottleneck stems from the ambiguity of attribution: first, the absence of a structured taxonomy of factors leaves researchers restricted to unguided adjustments; second, the lack of a unified metric fails to distinguish genuine collaboration gain from mere resource accumulation. In this paper, we advocate for a transition to design science through an integrated framework. We advocate to establish the collaboration gain metric ($\Gamma$) as the scientific standard to isolate intrinsic gains from increased budgets. Leveraging $\Gamma$, we propose a factor attribution paradigm to systematically identify collaboration-driving factors. To support this, we construct a systematic MAS factor library, structuring the design space into control-level presets and information-level dynamics. Ultimately, this framework facilitates the transition from blind experimentation to rigorous science, paving the way towards a true science of Collective AI.

cs.CL↗
arXiv2026

Enhancing Volumetric Optical Chirality through 2D-3D Structural Design Evolution

Circular dichroism (CD) sensing plays a pivotal role in probing molecular chirality in biomedical sciences. However, engineering superchiral electromagnetic fields that can reliably amplify the faint signatures of chiral analytes remains profoundly challenging. Central to this difficulty is the need to balance two competing demands: maximizing the enhancement of chiral fields while maintaining a sufficiently large interaction volume for effective molecular interrogation. Here, we introduce a figure of merit (FOM) that captures the enhancement and spatial coverage of superchiral fields to benchmark different chiral-field configurations. We examine the effects of helix-geometry evolution on the FOM, including 2D to 3D chirality induction, winding-number escalation, helical-order enhancement, and transverse dilation. By tuning these structural degrees of freedom, the sensing volume can be enlarged without compromising the distribution and enhancement strength of fields. The optimized triple-strand helix markedly enhanced the analyte CD signal, yielding a FOM of 2.43*10^10 nm3, which surpassed prior 2D and 3D configurations by over an order of magnitude. The proposed FOM exhibits a strong linear correlation (R^2 = 0.9256) with the analyte CD signal. Our findings provide a systematic design framework for 3D chiral structures and a robust metric for assessing their chiroptical sensing performance, particularly in scenarios involving clusters of randomly oriented small molecules or a large chiral molecule.

physics.optics↗
arXiv2025

SegMo: Segment-aligned Text to 3D Human Motion Generation

Generating 3D human motions from textual descriptions is an important research problem with broad applications in video games, virtual reality, and augmented reality. Recent methods align the textual description with human motion at the sequence level, neglecting the internal semantic structure of modalities. However, both motion descriptions and motion sequences can be naturally decomposed into smaller and semantically coherent segments, which can serve as atomic alignment units to achieve finer-grained correspondence. Motivated by this, we propose SegMo, a novel Segment-aligned text-conditioned human Motion generation framework to achieve fine-grained text-motion alignment. Our framework consists of three modules: (1) Text Segment Extraction, which decomposes complex textual descriptions into temporally ordered phrases, each representing a simple atomic action; (2) Motion Segment Extraction, which partitions complete motion sequences into corresponding motion segments; and (3) Fine-grained Text-Motion Alignment, which aligns text and motion segments with contrastive learning. Extensive experiments demonstrate that SegMo improves the strong baseline on two widely used datasets, achieving an improved TOP 1 score of 0.553 on the HumanML3D test set. Moreover, thanks to the learned shared embedding space for text and motion segments, SegMo can also be applied to retrieval-style tasks such as motion grounding and motion-to-text retrieval.

cs.CV↗