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Xiao Li

Publications and source records attributed to Xiao Li.

At least 91 records · Page 5Linked to original sources
arXiv2026

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses

Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open-world, safety-critical environments. As these systems gain autonomy and enter domains such as transportation, healthcare, and industrial or assistive robotics, ensuring their safety becomes both technically challenging and socially indispensable. Unlike digital AI systems, embodied agents must act under uncertain sensing, incomplete knowledge, and dynamic human-robot interactions, where failures can directly lead to physical harm. This survey provides a comprehensive and structured review of safety research in embodied AI, examining attacks and defenses across the full embodied pipeline, from perception and cognition to planning, action and interaction, and agentic system. We introduce a multi-level taxonomy that unifies fragmented lines of work and connects embodied-specific safety findings with broader advances in vision, language, and multimodal foundation models. Our review synthesizes insights from over 500 papers spanning adversarial, backdoor, jailbreak, and hardware-level attacks; attack detection, safe training and robust inference; and risk-aware human-agent interaction. This analysis reveals several overlooked challenges, including the fragility of multimodal perception fusion, the instability of planning under jailbreak attacks, and the trustworthiness of human-agent interaction in open-ended scenarios. By organizing the field into a coherent framework and identifying critical research gaps, this survey provides a roadmap for building embodied agents that are not only capable and autonomous but also safe, robust, and reliable in real-world deployment.

cs.CR↗
arXiv2026

Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models

Modern visual generative models acquire rich visual knowledge through large-scale training, yet existing visual representations (such as pixels, latents, or tokens) remain external to the model and cannot directly exploit this knowledge for compact storage or reuse. In this work, we introduce a new visual representation framework that encodes a signal as a function, which is parametrized by low-rank adaptations attached to a frozen visual generative model. Such implicit representations of visual signals, \textit{e.g.}, an 81-frame video, can further be hashed into a single compact vector, achieving strong perceptual video compression at extremely low bitrates. Beyond basic compression, the functional nature of this representation enables inference-time scaling and control, allowing additional refinement on the compression performance. More broadly, as the implicit representations directly act as a function of the generation process, this suggests a unified framework bridging visual compression and generation.

cs.LG↗
arXiv2026

The FAST Hundred-Deg$^2$ HI Deep (HD$^2$) Survey: Early Results from the Pilot Survey

The Hundred-deg$^2$ HI Deep (HD$^2$) survey carried out with the Five-hundred-meter Aperture Spherical Telescope (FAST) is planned to map a contiguous region within the DESI DR1 footprint, achieving an effective integration time of 20 minutes for each pointing and a uniform detection sensitivity of 0.28 mJy beam$^{-1}$ at 4.8 km s$^{-1}$ resolution. We present early results from the pilot HD$^2$ survey: a 10 deg$^2$ field overlapping with HSC-SSP and the DESI EDR SV3, observed with an integration time of 7.3 minutes per beam and the rms of 0.45 mJy beam$^{-1}$ at 4.8 km s$^{-1}$ resolution. We identify 339 HI sources at $z<0.09$, corresponding to $\sim$34 detections per deg$^2$, nearly six times higher than the detection rate of the wide-field surveys. Optical counterparts are primarily identified using DESI redshifts, yielding a matching rate and correctness exceeding 90% for galaxies with $r<19.5$ mag, a substantial improvement over SDSS. Under the constraint of $r < 17.8$ mag and $0.01 < z < 0.05$, nearly 50% of galaxies in the DESI BGS samples have HI detections in this pilot survey. The optical properties of these HI-detected galaxies span nearly the entire parameter range of the DESI sample. The gas fraction scaling relations versus stellar mass, stellar mass surface density, NUV-r, and specific star formation rate are consistent with previous surveys, e.g., ALFALFA, DINGO, and xGASS. These results justify the feasibility of the full HD$^2$ survey, which will build a high-completeness HI census over a contiguous area to probe the cold gas scaling relations of galaxies over different scales.

astro-ph.GA↗
arXiv2026

Multimodal-NF: A Wireless Dataset for Near-Field Low-Altitude Sensing and Communications

Environment-aware 6G wireless networks demand the deep integration of multimodal and wireless data. However, most existing datasets are confined to 2D terrestrial far-field scenarios, lacking the 3D spatial context and near-field characteristics crucial for low-altitude extremely large-scale multiple-input multiple-output (XL-MIMO) systems. To bridge this gap, this letter introduces Multimodal-NF, a large-scale dataset and specialized generation framework. Operating in the upper midband, it synchronizes high-fidelity near-field channel state information (CSI) and precise wireless labels (e.g., Top-5 beam indices, LoS/NLoS) with comprehensive sensory modalities (RGB images, LiDAR point clouds, and GPS). Crucially, these multimodal priors provide spatial semantics that help reduce the near-field search space and thereby lower the overhead of wireless sensing and communication tasks. Finally, we validate the dataset through representative case studies, demonstrating its utility and effectiveness. The open-source generator and dataset are available at https://lmyxxn.github.io/6GXLMIMODatasets/.

eess.SP↗
arXiv2026

DSSP: Diffusion State Space Policy with Full-History Encoding

Diffusion-based imitation learning has shown strong promise for robot manipulation. However, most existing policies condition only on the current observation or a short window of recent observations, limiting their ability to resolve history-dependent ambiguities in long-horizon tasks. To address this, we introduce DSSP, a history-conditioned Diffusion State Space Policy that enables efficient, full-history conditioning for robot manipulation. Leveraging the continuous sequence modeling properties of State Space Models (SSMs), our history encoder effectively compresses the entire observation stream into a compact context representation. To ensure this context preserves critical information regarding future state evolution, the encoder is optimized with a dynamics-aware auxiliary training objective. This high-level context representation is then seamlessly fused with recent state observations to form a hierarchical conditioning mechanism for action generation. Furthermore, to maintain architectural consistency and minimize GPU memory overhead, we also instantiate the diffusion backbone itself using an SSM. Extensive experiments across simulation benchmarks and real-world manipulation tasks show that DSSP achieves state-of-the-art performance with a significantly smaller model size, demonstrating superior efficiency of the hierarchical conditioning in capturing crucial information as the history length increases.

cs.RO↗
arXiv2026

Propagation-Consistent Wireless Environment Digital Twin Construction Under Sparse Measurements

Digital twins (DTs) are promising for wireless deployment, optimization, and data generation, but building a propagation-faithful twin from sparse real measurements remains difficult. This paper proposes a wireless environment digital twin (WEDT) construction paradigm that evolves a reconstructed geometric DT into a propagation-consistent wireless environment representation through calibration of a scene-level electromagnetic (EM) property field. Instead of directly fitting link-specific channel responses, the proposed paradigm first constructs a geometry-prior Bayesian channel map (BCM) to convert sparse position-labeled channel state information (CSI) into dense probabilistic supervision with uncertainty estimates. It then embeds the learnable EM property field into differentiable ray tracing (RT) based channel computation, thereby enabling calibration through an explicit propagation chain. Experiments in both public and real-world scenes show that WEDT achieves accurate channel prediction, generalizes to unseen transceiver topologies, and remains effective across different sampling conditions. WEDT also offers utility for material-related environment sensing, more reliable physical-layer planning, and higher-quality synthetic data generation for wireless AI. These results demonstrate the value of the proposed paradigm for propagation-consistent WEDT construction and related wireless applications.

eess.SP↗
arXiv2026

Distributed Near-Field Channel Estimation for U6G XL-MIMO Systems under Beam Squint

Since the beam squint and near-field effects both inherently exist in upper-6 GHz (U6G) extremely large-scale multiple-input multiple-output (XL-MIMO) systems, wideband near-field channel estimation faces severe challenges, such as higher computational complexity, and higher pilot overhead particularly at hybrid architectures with fewer radio frequency (RF) chains. To precisely reduce the complexity and number of pilots, the parametric symmetry of wideband near-field channels is explored, such that the channel parameters, including angle, distance, and range, can be decoupled based on the delay variations observed by different antennas. Based on this, a distributed parametric symmetry-based (DPS) algorithm, applicable to U6G XL-MIMO, is proposed. The delays observed by different subarrays are estimated and extrapolated across the local processing units (LPUs) firstly, and then, the channel parameters are decoupled and estimated at the central processing unit (CPU), by only linearly combining the delays from different LPUs. The path gains are calculated at different LPUs, respectively, to reconstruct the channel with low complexity. Since the proposed algorithm does not rely on scanning the polar-domain dictionary, only a single pilot is required even with hybrid architectures. Furthermore, the computational complexity, multiple-path resolution, Cramer-Rao lower bound (CRLB) and lower bound (LB) of the estimates in hybrid architectures and the DPS algorithm, respectively, are analyzed, to evaluate the realizable potential of the proposed algorithm. The simulation results prove that the proposed algorithm has a higher estimation accuracy, while requiring less complexity and pilots.

eess.SP↗
arXiv2026

Towards Physically Consistent 4D Scene Reconstruction for Closed-loop Autonomous Driving Simulation

High-fidelity street scene reconstruction is pivotal for end-to-end autonomous driving simulation, where novel-view synthesis (NVS) and time-varying information modeling are two fundamental capabilities to facilitate closed-loop training. However, existing 3DGS methods and their 4D extensions fail to simultaneously achieve both. To bridge this gap, we establish an information-geometric diagnostic framework, revealing that this limitation stems from a credit assignment dilemma between spatial and temporal parameters. Specifically, the deterministic coupling between viewpoint and time in single-source observation creates a low-rank structure that induces massive null-space ambiguity between static view-dependent and dynamic time-varying components. Temporal information overshadows spatial cues, causing the estimation variance of spatial parameters to diverge. To address this issue, we propose Orthogonal Projected Gradient (OPG), a hierarchical training method designed to restore spatial identifiability. OPG prioritizes the integrity of spatial representations by securing them in an initial stage, then restricts temporal updates to the spatial null space, enabling proactive credit assignment. While OPG isolates temporal updates algebraically, Temporal Regularization Strategy is proposed to further refine the temporal solution space by imposing a smoothness constraint based on the physical prior of consistent appearance evolution, ensuring that the reconstructed scene remains physically consistent in closed-loop simulation. Extensive experiments demonstrate that our method not only maintains stable NVS capabilities but also demonstrates superior performance in traditional observation-reproducing metrics, which indirectly reflect the capability of modeling temporal dynamics.

cs.CV↗
arXiv2026

Faster-GCG: Efficient Discrete Optimization Jailbreak Attacks against Aligned Large Language Models

Aligned Large Language Models (LLMs) have attracted significant attention for their safety, particularly in the context of jailbreak attacks that attempt to bypass guardrails via adversarial prompts. Among existing approaches, the Greedy Coordinate Gradient (GCG) attack pioneered automated jailbreaks through discrete token optimization; however, its low sample efficiency limits practical applicability. In particular, GCG requires approximately 256K evaluations per harmful behavior to achieve a satisfactory jailbreak success rate, due to the inherent difficulty of the underlying discrete optimization problem. In this work, we identify three key factors that limit the sample efficiency of GCG: inaccurate gradient-based estimation, inefficient uniform sampling, and repeated evaluation of previously explored suffixes. To address these issues, we propose Faster-GCG, a streamlined variant of GCG that incorporates distance-based regularization for improved estimation, temperature-controlled sampling for more effective exploration, and a visited-suffix marking mechanism to avoid redundant evaluations. Faster-GCG reduced the required evaluations to 32K, achieving up to an $8\times$ improvement in sampling efficiency and a $7\times$ reduction in wall-clock time compared to GCG. Under this reduced budget, Faster-GCG attained an average jailbreak success rate of 78.1\% across five aligned LLMs, and achieved 88.7\% against Qwen3.5-4B, outperforming state-of-the-art white-box jailbreak methods.

cs.LG↗
arXiv2026

FPED: A Functional-Network Prior-Guided Mixture-of-Experts Framework for Interpretable Brain Decoding

Visual image reconstruction from functional Magnetic Resonance Imaging (fMRI) is a fundamental task in brain decoding, providing a crucial pathway for understanding human perceptual mechanisms and developing advanced brain-computer interfaces (BCIs). However, most current methods simply flatten fMRI signals from localized visual cortices into one-dimensional (1D) vectors, mapping them directly into latent spaces such as that of Contrastive Language-Image Pre-training (CLIP). This paradigm not only disrupts the inherent network topology of the brain-leading to limited neuroscientific interpretability-but also overlooks the synergistic contributions of other distributed functional networks in processing high-level visual semantics. To address these limitations, we propose FPED, a Functional-Network Prior-Guided Mixture of Experts (MoE) framework for interpretable brain decoding. FPED explicitly models different functional brain networks as specialized experts and employs adaptive routing to capture their complementary contributions to visual semantic understanding. Unlike conventional homogeneous decoding paradigms, our framework incorporates neurobiologically grounded priors to enable structured and interpretable network-level representation learning. Experimental results demonstrate that FPED achieves highly competitive semantic reconstruction performance with only 0.68B parameters. The learned routing dynamics reveal biologically meaningful correspondence between functional brain networks and modality-specific semantic processing, providing transparent neuroscientific interpretability. This suggests that brain network-aware expert modeling is a promising direction for bridging neural decoding and biologically inspired artificial intelligence.

cs.CV↗
arXiv2026

Toward Natural and Companionable Virtual Agents via Cross-Temporal Emotional Modeling

Recent advances in foundation models have enabled conversational agents that aim for sustained companionship rather than mere task completion. Yet most still remain unable to support natural, long-term companion-like interactions, resulting in experiences that feel episodic and inauthentic. We argue that current agents overlooked cross-temporal modeling of agents' social behaviors and internal emotions: generated behaviors rarely influence an agent's emotional state, and emotional states seldom shape subsequent behaviors. We present Cross-Temporal Emotion Modeling (CTEM), a framework that links long-term behavioral history to moment-to-moment emotional expression. CTEM establishes a closed loop where past experiences update an evolving emotional state; this state conditions immediate interactions; and user feedback continually revises both memory and emotional state, enabling reflection and anticipation. We instantiate CTEM as Auri, a companion agent on an instant-messaging platform, and report a 21-day in-the-wild study showing that CTEM shows improvements in perceived naturalness, coherence, and emotional harmony.

cs.HC↗
arXiv2026

Invariant ionic conductance in an atomically thin polar nanopore

Ion channels regulate many essential properties of biological cells, especially the membrane potential. Despite decades of efforts on artificial channels, it remains a great challenge to mimic the dipole potential-an indispensable constituent of the membrane potential, due to its angstrom-scale characteristic length. Here, we explore nanopores in monolayer molybdenum sulfide selenide (MoSSe) considering its intrinsic dipole and atomic thickness. Remarkably, an invariant ionic conductance was observed over salt concentrations spanning six orders of magnitude, distinct from all known conductance-concentration scaling laws and reminiscent of the current saturation in cell membranes at high concentrations. Molecular dynamics simulations revealed the fundamental role of the dipole-modulated dielectric properties of nanoconfined water. Our findings highlight an exotic conductance scaling law and open up a novel avenue for controlling ion transport in unprecedented ways.

cond-mat.mes-hall↗
arXiv2026

XFreq-GS: Cross-Frequency Wireless Radiation Field Reconstruction with 3D Gaussian Splatting

Channel modeling is fundamental to the analysis, design, and optimization of wireless communication systems, which, however, accurate wireless channel modeling remains challenging, especially given the increasingly complex wireless environments. As an emerging paradigm, 3D Gaussian Splatting (3DGS)-based channel modeling methods achieve accurate wireless radiation field (WRF) reconstruction and high-fidelity spatial spectrum synthesis. However, existing works only consider a single carrier frequency and fail to adapt to wide-range cross-frequency channels. To address this challenge, we propose XFreq-GS, a cross-frequency Gaussian splatting framework for WRF reconstruction. It employs 3D Gaussian primitives with shared geometry and frequency-adaptive radio frequency (RF) attributes to reconstruct cross-frequency WRF, and synthesizes power angular spectrum (PAS) maps for wireless channel modeling. Experiments show that XFreq-GS outperforms state-of-the-art 3DGS-based methods in PAS synthesis and achieves superior cross-frequency generalization. Code is available at https://github.com/KINGAZ1019/XFreq-GS.

eess.SP↗
arXiv2026

Valley-contrasting Spin Textures in Janus Metal Phosphochalcogenides

Momentum-resolved spin textures and potential valley-contrasting physical properties in the momentum space are two intriguing characteristics of noncentrosymmetric materials, and they have broad applications in spintronics and valleytronics. The realization of diverse spin textures within a single material, along with their further coupling to the valley degree of freedom, is highly desirable. Via first-principles calculations, we investigate electronic properties of Janus MP$_2$S$_3$Se$_3$ monolayers, which exhibits distinct spin textures at different valleys. While Ising-type spin textures are located at $K_\pm$ valleys, the symmetry breaking from the Janus structure brings about a coexistence of Weyl-type and Rashba-type spin textures at $Γ$ valley. In addition to valley-contrasting spin textures, valley dependence also occurs in Berry-curvature-driven anomalous Hall currents and optical selectivity. Besides, energy differences between $Γ$ and $K_\pm$, as well as band gaps, are highly tunable by applied strain. These findings present an intriguing coupling between diverse spin textures and multiple valleys, and pave the way for designing advanced electronic devices that leverage spin and valley degrees of freedom.

cond-mat.mtrl-sci↗
arXiv2026

Usability as a Weapon: Attacking the Safety of LLM-Based Code Generation via Usability Requirements

Large Language Models (LLMs) are increasingly used for automated software development, making their ability to preserve secure coding practices critical. In practice, however, many security requirements are implicit or underspecified, whereas usability requirements are explicit and high-signal. This asymmetry motivates our investigation of usability pressure as a practical attack surface: realistic usability-oriented requirements (e.g., new features, performance constraints, or simplicity demands) can cause coding LLMs to satisfy explicit usability goals while silently dropping implicit security constraints -- a form of reward hacking. We formalize this threat as UPAttack and propose U-SPLOIT, an automated framework to craft UPAttack that (i) selects tasks where a model is initially secure, (ii) synthesizes usability pressures by identifying usability rewards of insecure alternatives across three vectors (Functionality, Implementation, Trade-off), and (iii) verifies security regression via both existing test cases and dynamically generated exploit payloads. Across 75 seed scenarios (25 CWEs x 3 cases), spanning multiple languages (Python, C, and JavaScript), U-SPLOIT achieves attack success rates up to 98.1% on multiple state-of-the-art models (e.g., GPT-5.2-chat and Gemini-3-Flash-Preview).

cs.CR↗
arXiv2026

HyNeuralMap: Hyperbolic Mapping of Visual Semantics to Neural Hierarchies

Understanding the intricate mappings between visual stimuli and neural responses is a fundamental challenge in cognitive neuroscience. While current approaches predominantly align images and functional magnetic resonance imaging (fMRI) responses in Euclidean space, this geometry often struggles to preserve fine-grained semantic relationships and latent hierarchical structures across visual and neural modalities. To overcome this, we propose HyNeuralMap, a framework that employ hyperbolic Lorentz model to map visual semantics into a shared, cross-subject neural hierarchy. By leveraging the negative curvature of hyperbolic space as an inductive bias, the proposed framework better captures hierarchical semantic organization and cross-subject neural similarities. Specifically, visual and neural embeddings are jointly optimized through hyperbolic geometric alignment, where geodesic distances preserve semantic proximity and hierarchical relationships more effectively than Euclidean embeddings. Experiments demonstrate that HyNeuralMap consistently outperforms state-of-the-art Euclidean baselines in both multi-label semantic prediction and cross-modal retrieval tasks. This confirms hyperbolic geometry's superiority for cross-modal semantic alignment and hierarchical modeling, providing a new avenue for vision-neural representation learning.

cs.CV↗
arXiv2026

Discrete time crystals enabled by Floquet strong Hilbert space fragmentation

Discrete time crystals (DTCs) are non-equilibrium phases of matter that break the discrete time-translation symmetry and is characterized by a robust subharmonic response in periodically driven quantum systems. Here, we explore the DTC in a disorder-free, periodically kicked XXZ spin chain, which is stabilized by the Floquet strong Hilbert space fragmentation. We numerically show the period-doubling response of the conventional DTC order, and uncover a multiple-period response with beating dynamics due to the coherent interplay of multiple $π$-pairs in the Floquet spectrum of small-size systems. The lifetime of the DTC order exhibits independence of the driving frequency and a power-law dependence on the ZZ interaction strength. It also grows exponentially with the system size, as a hallmark of the strong fragmentation inherent to the Floquet model. We analytically reveal the approximate conservation of the magnetization and domain-wall number in the Floquet operator for the emergent strong fragmentation, which is consistent with numerical results of the dimensionality ratio of symmetry subspaces. The rigidity and phase regime of the DTC order are identified through finite-size scaling of the Floquet-spectrum-averaged mutual information, as well as via dynamical probes. Our work establishes the Floquet Hilbert space fragmentation as a disorder-free mechanism for sustaining nontrivial temporal orders in out-of-equilibrium quantum many-body systems.

quant-ph↗
arXiv2026

PowerSINDy: Identifying Nonlinear Time-Dependent Dynamics in Power Grid Frequency

System identification plays a crucial role in physics and machine learning for discovering governing equations directly from data. A powerful approach is the Sparse Identification of Nonlinear Dynamics (SINDy) method, which assumes that only a few dominant terms drive the essential behavior of a nonlinear dynamical system. While SINDy methods have shown excellent results, they are most often illustrated on synthetic or simulated systems, leaving open the question of how well they perform on complex, noisy, real-world data. Power grid frequency dynamics provide a highly relevant and challenging environment for advancing system identification methods. In this work, we propose PowerSINDy as a framework for empirical power system data. We apply this framework to empirical frequency data from the Continental Europe (CE) and South Korea (SK) synchronous grids, two major power systems with distinct dynamical characteristics. PowerSINDy, which also includes time-dependent terms, can identify the dynamics of these complex real-world systems. Furthermore, we benchmark three sparsity-promoting regression strategies: Sequentially Thresholded Least Squares (STLSQ), Least Absolute Shrinkage and Selection Operator (LASSO), and Sparse Relaxed Regularized Regression (SR3) to evaluate trade-offs between accuracy, sparsity, and robustness. Results show that LASSO consistently achieves the lowest stable RMSEs, reaching 0.0101 for the CE, while STLSQ provides the best balance between accuracy and stability. SR3 exhibits higher variability and sensitivity to regularization, with L0 and L1 producing nearly indistinguishable outcomes.

eess.SY↗