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Xin Lu

Publications and source records attributed to Xin Lu.

At least 19 recordsLinked to original sources

Compound Climate Risk-Aware Electricity Dispatch: Trajectory-Based Modeling for Power System Resilience

In recent decades, emerging compound hazards in a changing climate have increasingly threatened power system resilience and posed new challenges to electricity dispatch. Conventional Value-at-Risk (VaR)-based risk-aware dispatch provides a tractable means of managing stochastic net demand deviations caused by distribution feeder outages. However, when applied to temporally compounding events such as tropical cyclone (TC)--blackout--heatwave, conventional net demand risk formulations may assign similar risk levels to a brief outage and an outage with the same instantaneous net demand impact that persists into a subsequent hazard, despite their different requirements for sustained operational flexibility. Moreover, directly incorporating compound risk into electricity dispatch models faces a structural mismatch: the risk is defined by the joint distribution of feeder restoration and subsequent hazards over exponentially many joint states, whereas dispatch models require risk formulations that are tractable in the scheduling decisions. To address these issues, this paper proposes a compound climate risk-aware dispatch framework with a trajectory-based compound risk model, which defines the loss on operating deviations determined by entire feeder connection trajectories, parameterized by uncertain restoration times and coupled with subsequent hazards. Analytical moment-generating functions and equivalent exponential-cone reformulations provide a compact representation of the resulting risk constraints, preserving the relevant dependencies without explicit joint-state enumeration. By leveraging a physics-based spatiotemporal TC--blackout--heatwave hazard model, case studies demonstrate the effectiveness of the proposed method with real-world time-series power outage data from Puerto Rico, including data collected during Hurricane Fiona in 2022.

eess.SY↗

AI for Science in Space: Orbital Laboratories, Planetary Fieldwork, and Embodied Autonomy

Artificial intelligence for science (AI4S) is extending from interpretation of existing data to the design and execution of experiments. Space offers distinctive scientific opportunities while restricting physical support. Microgravity and extraterrestrial environments expose phenomena that terrestrial experiments cannot fully reproduce; finite samples, constrained resources, and limited opportunities for intervention complicate sustained investigation. This critical review examines orbital laboratories and planetary in situ science across biology, materials, fluid and thermal physics, and adaptive exploration. We distinguish learning from space-derived data, selecting scientific actions, executing them, and preserving experimental validity. Terrestrial self-driving laboratories provide complementary evidence on optimization, dedicated automation, robotic manipulation, and the support needed for unattended operation. The synthesis connects scientific gains already demonstrated by bounded AI systems to gaps in observation, executable actions, and continuity of valid evidence. Space robots can expand the attainable scope of autonomous science when necessary interventions exceed validated instrument capabilities and timely human support is unavailable. Their value must be established against dedicated mechanisms, redundancy, and human-supported operation. We develop a framework connecting sample history, scientific decisions, physical execution, and claim-specific validity. A worked orbital design, a planetary extension, and matched evaluation schemes separate the contributions of sensing, decision policies, and physical actions. The resulting agenda measures autonomy through valid scientific return, task coverage, intervention costs, and evidence integrity across complete campaigns.

cs.RO↗

OneEEG: A Unified Signal-Visual Multimodal Language Model with Task-Aware Mixture of Projectors for Multi-Task EEG Understanding

Electroencephalography (EEG) is widely used in clinical assessment, brain-computer interfaces, and cognitive-state analysis, yet EEG modeling remains fragmented across task paradigms. Recent multimodal large language models (MLLMs) provide a promising interface for flexible multimodal reasoning, but their ability to understand EEG inputs remains limited. In this work, we propose OneEEG, a unified EEG-MLLM framework for multi-task EEG understanding. By training on our curated OneEEG-Instruct, a multi-task EEG instruction-tuning dataset, OneEEG jointly models numerical EEG representations and visual EEG representations, and introduces a task-aware Mixture of Projectors (MoP) to align heterogeneous EEG features across different task families with the LLM embedding space. We further establish OneEEG-Bench, a benchmark for evaluating current MLLMs and our method on multi-task EEG understanding. Experiments show that existing MLLMs have limited ability to interpret EEG inputs, while OneEEG achieves stronger performance by integrating numerical and visual EEG representations with task-aware EEG-to-LLM projection. Extensive ablations and analyses further reveal the effects of visual EEG renderings, MoP variants, router behavior, and feature dispersion across EEG tasks.

cs.LG↗

Direct Forcing: Single-Stage Training with Representation-Space Supervision for Few-Step Autoregressive Video Generation

Few-step autoregressive video generation enables low-latency streaming, yet prevailing training pipelines separate causal adaptation from distribution matching. Recent representation-based text-to-video methods retain this separation by starting with an ODE initialization stage, even when the target distribution is defined directly by reference videos. We introduce \method, the first single-stage framework that uses reference videos both as paired examples of how a history continues and as a target population for self-generated rollouts. Specifically, Direct Forcing is trained with two complementary signals: (1) Causal Continuation Alignment learns paired spatiotemporal correspondence under reference histories; and (2) Rollout Distribution Matching employs maximum mean discrepancy to align the representation distributions of self-generated and reference videos. To coordinate these signals, we introduce Dynamic Gradient Balancing, which adapts their relative gradient contributions during training. Experiments with Wan2.1-1.3B show that Direct Forcing learns a four-step autoregressive generator in 250 updates without a separate ODE initialization stage or online diffusion-score supervision, achieving 84.63 VBench Total and 86.01 Quality.

cs.CV↗

AstroBench: Benchmarking Free-Floating Capture and Space Robotic Manipulation

Capturing a loose object inside a spacecraft requires a robot to approach a target that can translate and rotate, and whose motion changes when contact is made. These conditions motivate evaluation beyond manipulation of supported objects. We introduce AstroBench, an open benchmark for free-floating capture and space robotic manipulation with a fixed-base arm. The task suite combines four initial-motion tracks with sample-storage operations based on MELFI hardware and cap and captive-screw servicing based on the Robotic Refueling Mission. The two workcells provide nine task definitions and 29 task-target combinations. A metadata-filtered Objaverse inventory supplies 8,414 object instances for asset expansion, alongside five cabin visual contexts and versioned task instructions. We report two acquisition-focused experiments using existing checkpoints. A frozen $π_{0.5}$ DROID joint-position policy captures 2 of 200 static targets and 4 of 200 moving targets across twenty object identities. A cube-specific ACT policy captures 8 of 10 static targets and 6 of 10 moving targets in a separate experiment. Each experiment retains its recorded observation, action and capture configuration. The results document difficult direct transfer for the tested generalist checkpoint and successful task-specific capture behavior, while identifying concrete settings for studying motion-aware perception and contact-sensitive control. Code, task definitions and editable workcell assets are available at https://github.com/AstroUpLab/AstroBench.

cs.RO↗

Compressing Onboard Language Models for Space Robots under a Behavioral-Fidelity Requirement

Language models are being explored as the interface through which space robots interpret operator commands and select among verified skills. Running them onboard calls for low-bit compression, and flight use adds requirements that generic post-training quantization leaves implicit: a fixed memory budget and storage format, a static artifact under configuration control, and behavior close to the full-precision model validated on the ground. We treat compression as a ground-segment step and study QSelect, which composes one static low-bit model from compatible AWQ, GPTQ, and AutoRound checkpoints by assigning each linear module to one quantizer family and scoring every substitution at the final next-token distribution. The default fidelity mode needs no labels or tuned thresholds and accepts a substitution only when the forward KL divergence from the BF16 reference decreases; an optional target-adapted mode adds answer likelihood on a small labeled set. On DeepSeek-R1-Distill-Qwen-7B, Llama-3.1-8B, and Llama-3.2-3B at 3 and 4 bits, composed models reach lower mean held-out perplexity than every single-family model in all six settings, and the fidelity mode lowers perplexity and KL further. In a matched scaffold, final-output scoring gives lower held-out KL than representation-similarity scoring in all 12 paired runs. With labels from training or development splits of the evaluated task families, the target-adapted mode raises 3-bit pooled accuracy on the two Llama models by 4.3 and 4.5 points over the best single-family model. At 3 bits, unquantized embedding and output layers make up 43-46% of the packed artifact. All evaluations use general-domain benchmarks on a ground GPU; space-domain tasks and flight hardware are left to future work.

cs.RO↗

Load Balancing with Evolving Workloads in Large-Scale Language Model Serving

Serving large language models requires assigning requests whose workloads grow during processing and whose completion times are unknown. With a synchronization barrier, lightly loaded workers wait for the slowest worker at every decode iteration. We model this loss through aggregate workload imbalance and formulate a joint admission and assignment problem with sticky requests and capacity limits. Short lookahead incorporates predicted completions of ongoing requests. For exact optimization without lookahead, we prove a comparison under independent geometric decode lengths, bounded independent nondegenerate prompt sizes, and batched releases satisfying a cumulative supply condition. The long-run expected imbalance improvement over first come, first served admission with request-count join-shortest-queue grows at least as the square root of the product of worker capacity and the logarithm of worker count, in the stated scaling regime. The comparison extends to capacity-aware round robin, random routing, and power of two choices. BalanceRoute approximations connect the formulation to practical routing. Experiments using up to 144 NPUs with proprietary and public traces show lower workload spread and higher throughput than the evaluated baselines. These empirical results complement the guarantee for exact optimization.

cs.DC↗

Ultra Flash: Scaling Real-Time Streaming Video Generation to High Resolutions

While recent autoregressive video diffusion models achieve remarkable streaming quality, they remain confined to low resolutions (e.g., 480P), leaving efficient, scalable, real-time high-resolution video generation a fundamental open challenge. To bridge this gap, we present Ultra Flash, a cascaded streaming framework capable of real-time high-resolution video generation. Ultra Flash achieves ~30 FPS at 1K resolution and ~18 FPS at 2K resolution on a single GPU through three key contributions: (1) an architecture-preserving T2V-to-TV2V super-resolution training paradigm coupled with an AIGC-oriented data degradation pipeline that effectively preserves the generative capability of the base model, enabling enhanced high-resolution detail when cascaded after mainstream low-resolution generative models; (2) a causal streaming latent upsampler paired with a high-resolution decoder, which enhances spatiotemporal coherence while enabling efficient latent spatial scaling and precise high-resolution decoding with negligible computational overhead; and (3) a cascade high-resolution streaming video generation optimization scheme that first performs hybrid-reward-enhanced sparse causalization and single-step distillation of the super-resolution model, then introduces cascaded streaming self-forcing preference optimization with dynamic cache management, jointly enhancing overall coherence, improving quality, and enabling real-time high-resolution streaming video generation. Extensive experiments demonstrate that Ultra Flash reliably produces ultra-high-resolution streaming video while maintaining state-of-the-art visual quality and superior efficiency. Project Page: https://xin1u.github.io/UltraFlash/

cs.CV↗

Foundation Models for Autonomous Space Robotics: Architectures, Physical Adaptation, and Validation

Space robots must interpret tasks and act despite unfamiliar dynamics, difficult sensing conditions, and limited opportunities for human intervention. Foundation models offer reusable perceptual and behavioral knowledge, but their value depends on how that knowledge reaches physical execution. This critical review follows space-specific requirements through perception, prediction, planning, and action to deployment and validation, emphasizing orbital manipulation and proximity operations. Terrestrial comparisons establish benefits of particular pretraining stages and model interfaces; their transfer to space dynamics and constrained onboard operation remains insufficiently tested. Event cameras offer timely motion observations across strong illumination contrasts, while spiking neural networks provide possible routes to sparse local computation. We examine how sensor degradation, state estimation and computing choices affect feedback and recovery. Higher action throughput need not yield fresher feedback; lower processor power can also coexist with higher energy per inference. We organize validation as an iterative sim-to-ground-to-space pathway, connecting physical tests with telemetry-based diagnosis and closed-loop revalidation. A satellite-capture example links observations to approach, contact and recovery, while a corresponding protocol separates generalization from adaptation with limited target-domain interaction. These comparisons identify what models already contribute and the physical evidence needed to support autonomous space operation.

cs.RO↗

Transdimensional Chern insulators in rhombohedral multilayer graphene superlattices

Recent experiments on lightly electron-doped rhombohedral multilayer graphene (RMG) in the "transdimensional" regime revealed anomalous Hall hysteresis under both perpendicular and in-plane magnetic fields, pointing to the emergence of transdimensional orbital magnetism in an interaction-driven "Fermi-lune" metal. Here we demonstrate that, by coupling to the moiré potential, this transdimensional orbital ferromagnetic metal can evolve into quantized Chern insulator phases in twisted RMG/hBN moiré superlattices. Using unrestricted Hartree-Fock calculations, we find that interaction-driven reconstruction isolates a spin-valley-polarized $|C| = 1$ Chern band, leading to a "transdimensional Chern insulator" (TDCI) state exhibiting both in-plane and out-of-plane orbital magnetizations. A minimal two-band model is introduced to describe the unique properties of TDCI, which unveils the intimate connection between non-reciprocal velocity and in-plane orbital magnetization. We further analyze the collective excitations of TDCIs, and identify soft charge and spin modes that may lead to density wave instabilities in certain cases. An in-plane magnetic field can harden the soft density-wave mode and may drive a transition from density-wave state to TDCI. Finally, when the $|C| = 1$ band is fractionally filled, the many-body ground state may be a fractional Chern insulator with broken rotation and mirror symmetries.

cond-mat.mes-hall↗

Reciprocity coordinates a three-way trade-off in recurrent neural circuits

Biological neural circuits contain both unidirectional and reciprocal connections, but how reciprocity affects circuit computation remains unclear. We varied reciprocity \r{ho} across 21 levels in recurrent-network simulations while holding network size, connection density, and directed-edge count fixed. Low-reciprocity networks produced more diverse stimulus-driven activity and performed better on four continuous-input tasks, whereas higher reciprocity generally improved performance on three delayed-memory tasks. Increasing reciprocity reduced online memory capacity and accelerated recovery from perturbations. A post-cue noise experiment showed that higher reciprocity suppressed internal noise propagation but made cue-evoked states less distinguishable, and component matching separated the contributions of cue separation and within-cue dispersion to readout robustness. Within this ensemble, reciprocity thus mediates a three-way trade-off among representational capacity, local stability, and task-state separability: higher reciprocity lowers capacity, strengthens stability, and compresses separability. Whether it improves behaviour depends on whether the stability gain outweighs the lost separation between task states. Connectome and microcircuit datasets from five species showed intermediate reciprocity, while reciprocity varied substantially across adult Drosophila brain regions, providing compatible structural context and motivating circuit-specific functional tests.

q-bio.NC↗

Fermi lune and non-reciprocal transport in rhombohedral multilayer graphene

We develop a microscopic theory of Fermi-lune metals in rhombohedral multilayer graphene and investigate their non-reciprocal longitudinal transport. We show that tuning the layer number and displacement field drives Lifshitz transitions from circular to annular non-interacting Fermi surfaces accompanied by redistributions of electronic weight among layers. Combining renormalization-group corrections with unrestricted Hartree-Fock calculations, we further demonstrate how Coulomb interactions reconstruct the non-interacting annular Fermi surface into crescent-shaped Fermi lune through spontaneous breaking of time-reversal, mirror and rotation symmetries. We determine competing mean-field phases for four to nine layers and distinguish two types of transdimensional orbital magnetic orders. Beyond the Fermi-lune state that exhibits transdimensional anomalous Hall effect reported recently, we also identify a mirror-preserving Fermi-lune phase with spontaneous in-plane orbital magnetization but a symmetry-forbidden anomalous Hall response. The maximum calculated in-plane orbital moment increases approximately linearly with layer number. The asymmetric Fermi-velocity distributions of these states also lead to intrinsic non-reciprocal longitudinal transport. Measurements presented here in a nine-layer device show a non-reciprocal resistance ratio reaching 23% at a current magnitude of 17 nA. These results elucidate the formation mechanism, competing symmetries, and thickness dependence of Fermi-lune states, and connect their electronic structure to a longitudinal-transport signature beyond the previously reported anomalous Hall phenomenology.

cond-mat.mes-hall↗

ScribbleEdit: A Benchmark for Scribble-Only Image Editing

Scribble-based interaction provides a lightweight and intuitive way for users to specify image editing intents in interactive editing tools. However, current image editing models based on VLMs or LLMs struggle to understand and execute edits based solely on scribble inputs. To systematically study this problem, we construct a new benchmark, ScribbleEdit, that evaluates the ability of image editing models to perform image editing conditioned on scribbles. This task requires both a deep understanding of the intention of the scribble and an accurate interpretation of its spatial information. In ScribbleEdit, we design an automated data construction pipeline and introduce a dedicated evaluation protocol that explicitly measures intention alignment. Our analysis reveals that existing VLM/LLM-based editing models fail to accurately capture scribble intentions. To guide future progress on scribble-only image editing, we propose a simple yet effective soft-token baseline, which enhances the model's understanding of scribble semantics and outperforms standard image editing models on our benchmark. Our evaluation and baseline together provide a concrete foundation for assessing and improving the scribble-driven image editing.

cs.GR↗

ContraSafe-VL: A Contrastive Safety Preference Dataset for Vision-Language Models

Vision-Language Models (VLMs) are increasingly deployed as general-purpose multimodal assistants, making multimodal safety alignment an important challenge. Safety depends on the interplay between textual and visual content, with visual context often playing a critical role. However, VLMs may fail to use visual information appropriately, either overlooking safety-relevant visual content or over-interpreting sensitive visual content, leading to unsafe responses or over-refusal. Existing multimodal safety alignment datasets largely follow the response-preference paradigm, which teaches models how to respond but provides limited supervision on how visual context should inform the safety judgment, making it difficult to balance safety and over-refusal. To address this limitation, we introduce ContraSafe-VL, a Contrastive Safety preference dataset for Vision-Language Models. For each fixed textual query, ContraSafe-VL constructs paired visual contexts that result in multimodal queries with different safety labels, providing direct supervision for how visual context should inform multimodal safety judgments. It further provides response preference pairs based on harmlessness and helpfulness to encourage models to generate responses that are both safe and helpful. Experiments across three VLM architectures and multiple benchmarks demonstrate that training with ContraSafe-VL improves cross-modal safety recognition and achieves a better safety--over-refusal trade-off while preserving general multimodal capabilities. The dataset will be publicly released.

cs.CV↗

OmniVR: Audio-Video Conditional Generation for Archival Footage Restoration

Archival footage often suffers from coupled visual and acoustic degradations, yet most restoration systems process the two modalities separately. To address this problem, we present OmniVR, the first systematic framework for joint audio-video restoration, covering data construction, model adaptation, efficient inference, and evaluation. We construct a high-quality audio-video corpus with detailed captions and use a joint degradation pipeline to produce aligned clean and degraded pairs. Using these pairs, we adapt a pretrained text-to-audio-video model (T2AV) by introducing degraded audio-video conditions (TAV2AV), then progressively replace sample captions with a fixed restoration prompt while retaining caption/null rehearsal. The resulting AV2AV model requires no user-provided text. Under a compatible residual-learning model, we prove that this condition-annealing schedule reduces gradient variance and expected restoration risk relative to direct fixed-prompt adaptation at the same training budget. For efficient deployment, OmniVR-Flash combines reduced-resolution video conditioning, MeanFlow-based one-step distillation, and Turbo VAE, achieving approximately 38 fps at 1K and 18 fps at 2K on a single B200 GPU. We further introduce OmniVRBench to evaluate four complementary dimensions: visual quality, audio quality, temporal consistency, and audio-visual synchrony. OmniVR achieves state-of-the-art results on public benchmarks and OmniVRBench. Data, code, and model weights will be released. Project Page: https://xin1u.github.io/OminiVR_PAGE/

cs.CV↗

Realistic modeling of Photonic Terahertz Communication based on a real-world 30 km 30 Gbps experimental demonstration

Experimental studies have demonstrated that THz links can support multi-kilometer, gigabit-per-second wireless transmission. However, few studies have effectively validated channel models through high-capacity, long-distance experiments. In this work, we experimentally demonstrate a 30 km ultra-long-haul photonic THz wireless communication system. 16 Gbaud quadrature phase shift keying (QPSK) signals are transmitted and tested, achieving a maximum data rate of 30 Gbps. For the first time, we validate two widely used THz channel models using a fully implemented 30 km experimental setup, providing a systematic and thorough analysis that integrates theoretical modeling with experimental results. We also perform an analysis of power consumption in photonic THz communication systems and provide a detailed comparison between simulated and experimental power consumption results. Finally, a detailed performance comparison between the simulation and experimental results, including signal-to-noise ratio (SNR), bit error rate (BER) and data rate, are provided. Our work provides essential insights for the design and energy-efficient deployment of future high-capacity THz networks, establishing a benchmark for subsequent research in channel modeling, experimental verification, and system-level energy efficiency.

eess.SP↗

DyMD: Preserving Interaction Dynamics through Distribution Matching Distillation in Few-Step Video World Models

Large video diffusion models offer expressive priors for embodied prediction and learning, yet their many-step sampling remains costly for interactive downstream use. Distribution Matching Distillation (DMD) enables few-step video generation, but can suppress robot--object motion while preserving visual quality. Examining DMD's teacher and fake-score signals, we find that weak re-noising keeps the teacher posterior concentrated near motion-deficient rollouts, limiting motion-restoring guidance. Meanwhile, stronger-motion rollouts tend to incur larger fake-score fitting errors, which can hinder the generator's learning of interaction dynamics. We propose DyMD, a DMD framework that adapts both teacher supervision and critic fitting to the evolving student. Temporal affinity--conditioned re-noise sampling adapts the timestep distribution to each rollout's current interaction fidelity by mixing the base schedule with a teacher prior motivated by local posterior variation, thereby balancing motion recovery and appearance refinement. To better track stronger-motion rollouts, dynamics-guided fake-score tracking uses a noise-conditioned predictor to estimate noise-relative fitting difficulty from latent temporal dynamics, then upweights predicted-hard rollouts in the critic loss. Using DyMD, we distill a 14B teacher into a four-step 1.3B student with no auxiliary modules at inference. On embodied-video benchmarks, the student improves R-Bench task adherence by $9.6$ percentage points and PAI-Bench-G Domain score by $5.1$ points over Base DMD while maintaining comparable visual quality. As a backbone for downstream action planning, our student achieves 34% mean success across two WorldArena tasks, compared with 16% for Base DMD.

cs.CV↗

Hidden magnetic order within the pressure induced superconducting dome of UTe2

Unconventional superconductivity typically occurs near magnetic instabilities, and the corresponding spin fluctuations are widely believed to play a crucial role in mediating electron pairing. UTe$_2$ is a promising candidate for exhibiting multiple spin-triplet superconducting phases when tuning with applied pressure and magnetic fields, but the nature of the magnetism driving these unconventional pairing states is undetermined. Our measurements of UTe$_2$ under applied pressures and magnetic fields reveal the presence of a magnetic order hidden within the pressure-induced superconducting dome, which vanishes together with the superconductivity once there is sufficiently high pressure to induce the three-dimensional antiferromagnetic phase. Extrapolation of the phase boundary of the hidden magnetic order, which is most likely antiferromagnetic in nature, points to a zero-temperature quantum critical point that coincides with the maximum transition temperature of the pressure-induced superconducting dome, suggesting that it could corresponds to the parent magnetic phase of the critical antiferromagnetic spin fluctuations driving the triplet superconductivity. These findings advance the understanding of the interplay of magnetism and superconductivity in an exemplar candidate triplet superconductor, which is necessary for revealing the microscopic origin of the different unconventional superconducting phases.

cond-mat.supr-con↗