arXiv subjects

Yulin Shen

Publications and source records attributed to Yulin Shen.

12 recordsLinked to original sources

arXiv2026

Integrated yoctosecond-precision timing detector

Precise timing detection is essential for exploring ultrafast phenomena in fields ranging from free-electron lasers to ultra-high-power laser facilities. However, achieving simultaneous high resolution and large dynamic range remains a fundamental challenge, and state of the art systems are often constrained by their physical size, power requirements, and limited scalability. Here we introduce an integrated dual electro optic sub cycle timing detector (DEST) that overcomes these limitations. In a proof of principle measurement, the device resolves timing jitter as small as 11 yoctoseconds (ys, $10^{-24}$ s) at 1 MHz--equivalent to the transit time of light across two protons--while maintaining an unambiguous measurement range of 6.15 ps and a dynamic range exceeding 155 dB. The core detection unit is miniaturized to chip scale dimensions of 18 mm x 2 mm x 1 mm on a thin film lithium niobate platform, ensuring inherent stability and immunity to environmental disturbances. Moreover, the architecture naturally lends itself to massive parallelization through array integration, with the potential to push timing precision to the sub 10 rontosecond (rs, $10^{-27}$ s) level within a 1 $m^2$ footprint. This combination of extreme sensitivity, wide dynamic range, compact size, and scalability opens new avenues for detecting previously inaccessible weak signals, including those from gravitational waves, quantum vacuum fluctuations, and beyond.

physics.optics↗

arXiv2026

Intertwined magnetoresistance and Hall multifunctionality in a non-coplanar magnetic Weyl semimetal DyB4

Anomalous magneto-transport responses provide complementary probes of orbital motion, momentum-space topology, and real-space spin chirality, yet their integration into a single material remains rare because their underlying requirements often compete. A promising materials-design strategy is to realize a magnetic Weyl semimetal that combines linearly dispersive high-mobility bands with tunable non-coplanar magnetism while limiting spin-dependent scattering. Here we identify DyB4, a frustrated rare-earth tetraboride, as a magnetic Weyl semimetal candidate that embodies this strategy and hosts intertwined magnetoresistance and Hall multifunctionality. Neutron diffraction reveals a sequence of field-tunable magnetic states, including non-coplanar spin configurations and PT-symmetry-broken phases. First-principles calculations identify steep linear dispersions and field-induced Weyl points near the Fermi level. Magneto-transport measurements establish a rare fourfold combination of extremely large magnetoresistance, chiral-anomaly-like negative magnetoresistance, large anomalous Hall conductivity arising from cooperative intrinsic Berry curvature and skew scattering, and scalar-spin-chirality-driven topological Hall responses. This multifunctionality arises from the distinct yet weakly coupled roles of itinerant carriers and localized 4f moments, which enable high-mobility transport, field-induced Weyl topology, and non-coplanar magnetism. DyB4 therefore provides a 4f-electron platform for correlating orbital transport, momentum-space Berry curvature, and real-space spin chirality, suggesting a route toward multifunctional magnetic topological materials.

cond-mat.mtrl-sci↗

arXiv2026

Non-covalent Interactions at cm$^{-1}$ Accuracy: Data Efficient Physics-Informed Distillation for Machine Learning Interatomic Potentials

Foundation models in atomistic machine learning encode interaction physics across diverse atomic environments, but whether that structure can be transferred when building specialist potentials at quantum-chemical accuracy remains open. Here we show that knowledge distillation from a pretrained universal machine-learning interatomic potential (MLIP), followed by coupled-cluster fine-tuning with single and double excitations and perturbative triples [CCSD(T)], transfers not only low-cost labels but a physically meaningful prior on interaction length scales, anisotropy, and the repulsive-dispersive balance, which CCSD(T) data then sharpens to quantum-chemical accuracy. For He--benzene, fine-tuning with 30% of the CCSD(T) data outperforms direct training using the full 80%; a 60% reduction in the high-fidelity compute budget. A symmetry-adapted perturbation theory (SAPT)-informed adaptive short-range/long-range architecture further lowers the validation MAE from 0.75 1/cm to 0.49 1/cm. Across a circumarene series of polycyclic aromatic hydrocarbons (PAHs), swapping the MLIP teacher under an otherwise identical pipeline changes the coronene error by an order of magnitude while leaving the larger PAHs stable, direct evidence that distillation transfers physical structure, not labels alone. Together, these results identify the choice of pretrained teacher as a primary design axis for data-efficient quantum-chemical-accuracy potentials, alongside architecture and training protocol.

physics.chem-ph↗

arXiv2026

Invisible Threats from Model Context Protocol: Generating Stealthy Injection Payload via Tree-based Adaptive Search

Recent advances in the Model Context Protocol (MCP) have enabled large language models (LLMs) to invoke external tools with unprecedented ease. This creates a new class of powerful and tool augmented agents. Unfortunately, this capability also introduces an under explored attack surface, specifically the malicious manipulation of tool responses. Existing techniques for indirect prompt injection that target MCP suffer from high deployment costs, weak semantic coherence, or heavy white box requirements. Furthermore, they are often easily detected by recently proposed defenses. In this paper, we propose Tree structured Injection for Payloads (TIP), a novel black-box attack which generates natural payloads to reliably seize control of MCP enabled agents even under defense. Technically, We cast payload generation as a tree structured search problem and guide the search with an attacker LLM operating under our proposed coarse-to-fine optimization framework. To stabilize learning and avoid local optima, we introduce a path-aware feedback mechanism that surfaces only high quality historical trajectories to the attacker model. The framework is further hardened against defensive transformations by explicitly conditioning the search on observable defense signals and dynamically reallocating the exploration budget. Extensive experiments on four mainstream LLMs show that TIP attains over 95% attack success in undefended settings while requiring an order of magnitude fewer queries than prior adaptive attacks. Against four representative defense approaches, TIP preserves more than 50% effectiveness and significantly outperforms the state-of-the-art attacks. By implementing the attack on real world MCP systems, our results expose an invisible but practical threat vector in MCP deployments. We also discuss potential mitigation approaches to address this critical security gap.

cs.CR↗

arXiv2026

RetimeGS: Continuous-Time Reconstruction of 4D Gaussian Splatting

Temporal retiming, the ability to reconstruct and render dynamic scenes at arbitrary timestamps, is crucial for applications such as slow-motion playback, temporal editing, and post-production. However, most existing 4D Gaussian Splatting (4DGS) methods overfit at discrete frame indices but struggle to represent continuous-time frames, leading to ghosting artifacts when interpolating between timestamps. We identify this limitation as a form of temporal aliasing and propose RetimeGS, a simple yet effective 4DGS representation that explicitly defines the temporal behavior of the 3D Gaussian and mitigates temporal aliasing. To achieve smooth and consistent interpolation, we incorporate optical flow-guided initialization and supervision, triple-rendering supervision, and other targeted strategies. Together, these components enable ghost-free, temporally coherent rendering even under large motions. Experiments on datasets featuring fast motion, non-rigid deformation, and severe occlusions demonstrate that RetimeGS achieves superior quality and coherence over state-of-the-art methods.

cs.CV↗

arXiv2026

Direct vs. Score-based Selection: Understanding the Heisenberg Effect in Target Acquisition Across Input Modalities in Virtual Reality

Target selection is a fundamental interaction in virtual reality (VR). But the act of confirming a selection, such as a button press or pinch, can disturb the tracked pose and shift the intended target, which is referred to as the Heisenberg Effect. Prior research has mainly investigated controller input. However, it remains unclear how the effect manifests in the bare-hand input and how score-based techniques may mitigate the effect in different spatial variations. To fill the gap, we conduct a within-subject study to examine the Heisenberg Effect across two input modalities (i.e., controller and hand) and two selection mechanisms (i.e., direct and score-based). Our results show that hand input is more susceptible to the Heisenberg Effect, with direct selection more influenced by target width and score-based selection more sensitive to target density. Based on previous vote-oriented technique and our temporal analysis, we introduce weighted VOTE, a history-based intention accuracy model for target voting, that reweights recent interaction intent to counteract input disturbances. Our evaluation shows the method improves selection accuracy compared to baseline techniques. Finally, we discuss future directions for adaptive selection methods.

cs.HC↗

arXiv2026

MirrorGuard: Toward Secure Computer-Use Agents via Simulation-to-Real Reasoning Correction

Large foundation models are integrated into Computer Use Agents (CUAs), enabling autonomous interaction with operating systems through graphical user interfaces (GUIs) to perform complex tasks. This autonomy introduces serious security risks: malicious instructions or visual prompt injections can trigger unsafe reasoning and cause harmful system-level actions. Existing defenses, such as detection-based blocking, prevent damage but often abort tasks prematurely, reducing agent utility. In this paper, we present MirrorGuard, a plug-and-play defense framework that uses simulation-based training to improve CUA security in the real world. To reduce the cost of large-scale training in operating systems, we propose a novel neural-symbolic simulation pipeline, which generates realistic, high-risk GUI interaction trajectories entirely in a text-based simulated environment, which captures unsafe reasoning patterns and potential system hazards without executing real operations. In the simulation environment, MirrorGuard learns to intercept and rectify insecure reasoning chains of CUAs before they produce and execute unsafe actions. In real-world testing, extensive evaluations across diverse benchmarks and CUA architectures show that MirrorGuard significantly mitigates security risks. For instance, on the ByteDance UI-TARS system, it reduces the unsafe rate from 66.5% to 13.0% while maintaining a marginal false refusal rate (FRR). In contrast, the state-of-the-art GuardAgent only achieves a reduction to 53.9% and suffers from a 15.4% higher FRR. Our work proves that simulation-derived defenses can provide robust, real-world protection while maintaining the fundamental utility of the agent. Our code and model are publicly available at https://bmz-q-q.github.io/MirrorGuard/.

cs.AI↗

arXiv2024

GS-ID: Illumination Decomposition on Gaussian Splatting via Adaptive Light Aggregation and Diffusion-Guided Material Priors

Gaussian Splatting (GS) has emerged as an effective representation for photorealistic rendering, but the underlying geometry, material, and lighting remain entangled, hindering scene editing. Existing GS-based methods struggle to disentangle these components under non-Lambertian conditions, especially in the presence of specularities and shadows. We propose \textbf{GS-ID}, an end-to-end framework for illumination decomposition that integrates adaptive light aggregation with diffusion-based material priors. In addition to a learnable environment map for ambient illumination, we model spatially-varying local lighting using anisotropic spherical Gaussian mixtures (SGMs) that are jointly optimized with scene content. To better capture cast shadows, we associate each splat with a learnable unit vector that encodes shadow directions from multiple light sources, further improving material and lighting estimation. By combining SGMs with intrinsic priors from diffusion models, GS-ID significantly reduces ambiguity in light-material-geometry interactions and achieves state-of-the-art performance on inverse rendering and relighting benchmarks. Experiments also demonstrate the effectiveness of GS-ID for downstream applications such as relighting and scene composition.

cs.CV↗

arXiv2024

Nonlinear photocurrent in quantum materials for broadband photodetection

Unlocking the vast potential of optical sensing technology has long been hindered by the challenges of achieving fast, sensitive, and broadband photodetection at ambient temperatures. In this review, we summarize recent progress in the study of nonlinear photocurrent in topological quantum materials, and its application in broadband photodetection without the use of p-n junction based semiconductor diodes. The intrinsic quadratic transverse current-input voltage relation is used to rectify the alternating electric field from incident radio, terahertz or infrared waves into a direct current, without a bias voltage and at zero magnetic field. We review novel photocurrents in several material systems, including topological Weyl semimetals, chiral crystals, ferroelectric materials, and low dimensional topological insulators. These quantum materials hold tremendous promise for broadband high-frequency rectification and photodetection, featuring substantial responsivity and detectivity.

cond-mat.mes-hall↗

arXiv2021

Geometry Attention Transformer with Position-aware LSTMs for Image Captioning

In recent years, transformer structures have been widely applied in image captioning with impressive performance. For good captioning results, the geometry and position relations of different visual objects are often thought of as crucial information. Aiming to further promote image captioning by transformers, this paper proposes an improved Geometry Attention Transformer (GAT) model. In order to further leverage geometric information, two novel geometry-aware architectures are designed respectively for the encoder and decoder in our GAT. Besides, this model includes the two work modules: 1) a geometry gate-controlled self-attention refiner, for explicitly incorporating relative spatial information into image region representations in encoding steps, and 2) a group of position-LSTMs, for precisely informing the decoder of relative word position in generating caption texts. The experiment comparisons on the datasets MS COCO and Flickr30K show that our GAT is efficient, and it could often outperform current state-of-the-art image captioning models.

cs.CV↗

arXiv2021

When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions

Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description. Since a scenario contains both keyphrases for retrieval and much noise, retrieval for SQA is extremely difficult. Moreover, it can hardly be supervised due to the lack of relevance labels of paragraphs for SQA. To meet the challenge, in this paper we propose a joint retriever-reader model called JEEVES where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. JEEVES significantly outperforms a variety of strong baselines on multiple-choice questions in three SQA datasets.

cs.CL↗

arXiv2019

GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level

Scenario-based question answering (SQA) has attracted increasing research attention. It typically requires retrieving and integrating knowledge from multiple sources, and applying general knowledge to a specific case described by a scenario. SQA widely exists in the medical, geography, and legal domains---both in practice and in the exams. In this paper, we introduce the GeoSQA dataset. It consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level, where diagrams (e.g., maps, charts) have been manually annotated with natural language descriptions to benefit NLP research. Benchmark results on a variety of state-of-the-art methods for question answering, textual entailment, and reading comprehension demonstrate the unique challenges presented by SQA for future research.

cs.CL↗