arXiv subjects

Xu Liu

Publications and source records attributed to Xu Liu.

At least 109 records · Page 6Linked to original sources

arXiv2025

ST-LoRA: Low-rank Adaptation for Spatio-Temporal Forecasting

Spatio-temporal forecasting is essential for understanding future dynamics within real-world systems by leveraging historical data from multiple locations. Existing methods often prioritize the development of intricate neural networks to capture the complex dependencies of the data. These methods neglect node-level heterogeneity and face over-parameterization when attempting to model node-specific characteristics. In this paper, we present a novel low-rank adaptation framework for existing spatio-temporal prediction models, termed \model, which alleviates the aforementioned problems through node-level adjustments. Specifically, we introduce the node-adaptive low-rank layer and node-specific predictor, capturing the complex functional characteristics of nodes while maintaining computational efficiency. Extensive experiments on multiple real-world datasets demonstrate that our method consistently achieves superior performance across various forecasting models with minimal computational overhead, improving performance by 7% with only 1% additional parameter cost. The source code is available at https://github.com/RWLinno/ST-LoRA.

cs.LG↗

arXiv2025

From Filamentation to Stratification: Instability Dynamics in Scissors-Shaped Relativistic Beam-Plasma System

Counter-streaming systems are a canonical model for beam-plasma instabilities, such as the filamentation instability, which is critical in high energy density physics. However, scenarios involving intersecting fast electron beams break the cylindrical symmetry inherent to such systems. Here, we introduce the scissors-shaped configuration, a fundamental multi-velocity-component system that captures this broken symmetry. Through theoretical analysis and particle-in-cell simulations, we reveal a dramatic shift in the instability dynamics: the system undergoes a stratification mode instead of filamentation. This mode is rapidly quenched by magnetic reconnection, leading to a quasi-stable state with magnetic energy two orders of magnitude lower than in the counter-streaming case. This discovery establishes a new principle of passive instability control via geometric configuration, offering a new perspective on beam-plasma interactions in astrophysics and inertial confinement fusion. The underlying physics is verifiable in upcoming multi-laser experiments.

physics.plasm-ph↗

arXiv2025

A polychromatic continuous-variable quantum communication network enabled by optical frequency combs

In classical communication, the introduction of polychromatic resources has rapidly boosted classical networks' rate and scale. Quantum communication is now at a similar critical stage in its development, and therefore, it is essential to investigate polychromatic quantum communication networks. In this letter, we report a polychromatic continuous-variable quantum communication network enabled by optical frequency combs. The multi-mode density matrices constituted by polychromatic quantum networks are studied. Considering the limited mode isolation, the maximum amount of information that eavesdroppers can obtain is recalculated, therefore, the total secret key rate is provided. We have also demonstrated that, compared to other multiplexing techniques, polychromatic quantum networks can theoretically achieve a secret key rate without decreasing with the increase in users. In the experiment, direct-transmission type and round-trip type quantum communication networks were built using optical frequency combs and dual-comb interference detection technology. The Gaussian-modulated continuous-variable quantum key distribution (CV-QKD) protocol has been validated, with a network capacity of 19 and a total secret key rate of 8.75 Gbps at a uniform distance of 5 km (asymptotic case), 0.82 Mbps at 120 km (finite-size effect), 89.10 Mbps at 40 km (compsable security), 13.66 Mbps at 40 km (compsable finite-size security). This implementation not only provides technical support for a high-speed multi-node quantum network, but also provides a solution for the future quantum Internet with continuous variables.

quant-ph↗

arXiv2025

CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models

Investigating hallucination issues in large language models (LLMs) within cross-lingual and cross-modal scenarios can greatly advance the large-scale deployment in real-world applications. Nevertheless, the current studies are limited to a single scenario, either cross-lingual or cross-modal, leaving a gap in the exploration of hallucinations in the joint cross-lingual and cross-modal scenarios. Motivated by this, we introduce a novel joint Cross-lingual and Cross-modal Hallucinations benchmark (CCHall) to fill this gap. Specifically, CCHall simultaneously incorporates both cross-lingual and cross-modal hallucination scenarios, which can be used to assess the cross-lingual and cross-modal capabilities of LLMs. Furthermore, we conduct a comprehensive evaluation on CCHall, exploring both mainstream open-source and closed-source LLMs. The experimental results highlight that current LLMs still struggle with CCHall. We hope CCHall can serve as a valuable resource to assess LLMs in joint cross-lingual and cross-modal scenarios.

cs.CL↗

arXiv2025

CCNU at SemEval-2025 Task 3: Leveraging Internal and External Knowledge of Large Language Models for Multilingual Hallucination Annotation

We present the system developed by the Central China Normal University (CCNU) team for the Mu-SHROOM shared task, which focuses on identifying hallucinations in question-answering systems across 14 different languages. Our approach leverages multiple Large Language Models (LLMs) with distinct areas of expertise, employing them in parallel to annotate hallucinations, effectively simulating a crowdsourcing annotation process. Furthermore, each LLM-based annotator integrates both internal and external knowledge related to the input during the annotation process. Using the open-source LLM DeepSeek-V3, our system achieves the top ranking (\#1) for Hindi data and secures a Top-5 position in seven other languages. In this paper, we also discuss unsuccessful approaches explored during our development process and share key insights gained from participating in this shared task.

cs.CL↗

arXiv2025

Latent Theory of Mind: A Decentralized Diffusion Architecture for Cooperative Manipulation

We present Latent Theory of Mind (LatentToM), a decentralized diffusion policy architecture for collaborative robot manipulation. Our policy allows multiple manipulators with their own perception and computation to collaborate with each other towards a common task goal with or without explicit communication. Our key innovation lies in allowing each agent to maintain two latent representations: an ego embedding specific to the robot, and a consensus embedding trained to be common to both robots, despite their different sensor streams and poses. We further let each robot train a decoder to infer the other robot's ego embedding from their consensus embedding, akin to theory of mind in latent space. Training occurs centrally, with all the policies' consensus encoders supervised by a loss inspired by sheaf theory, a mathematical theory for clustering data on a topological manifold. Specifically, we introduce a first-order cohomology loss to enforce sheaf-consistent alignment of the consensus embeddings. To preserve the expressiveness of the consensus embedding, we further propose structural constraints based on theory of mind and a directional consensus mechanism. Execution can be fully distributed, requiring no explicit communication between policies. In which case, the information is exchanged implicitly through each robot's sensor stream by observing the actions of the other robots and their effects on the scene. Alternatively, execution can leverage direct communication to share the robots' consensus embeddings, where the embeddings are shared once during each inference step and are aligned using the sheaf Laplacian. In our hardware experiments, LatentToM outperforms a naive decentralized diffusion baseline, and shows comparable performance with a state-of-the-art centralized diffusion policy for bi-manual manipulation. Project website: https://stanfordmsl.github.io/LatentToM/.

cs.RO↗

arXiv2025

Demystifying Diffusion Policies: Action Memorization and Simple Lookup Table Alternatives

Diffusion policies have demonstrated remarkable dexterity and robustness in intricate, high-dimensional robot manipulation tasks, while training from a small number of demonstrations. However, the reason for this performance remains a mystery. In this paper, we offer a surprising hypothesis: diffusion policies essentially memorize an action lookup table -- and this is beneficial. We posit that, at runtime, diffusion policies find the closest training image to the test image in a latent space, and recall the associated training action sequence, offering reactivity without the need for action generalization. This is effective in the sparse data regime, where there is not enough data density for the model to learn action generalization. We support this claim with systematic empirical evidence. Even when conditioned on wildly out of distribution (OOD) images of cats and dogs, the Diffusion Policy still outputs an action sequence from the training data. With this insight, we propose a simple policy, the Action Lookup Table (ALT), as a lightweight alternative to the Diffusion Policy. Our ALT policy uses a contrastive image encoder as a hash function to index the closest corresponding training action sequence, explicitly performing the computation that the Diffusion Policy implicitly learns. We show empirically that for relatively small datasets, ALT matches the performance of a diffusion model, while requiring only 0.0034 of the inference time and 0.0085 of the memory footprint, allowing for much faster closed-loop inference with resource constrained robots. We also train our ALT policy to give an explicit OOD flag when the distance between the runtime image is too far in the latent space from the training images, giving a simple but effective runtime monitor. More information can be found at: https://stanfordmsl.github.io/alt/.

cs.RO↗

arXiv2025

Boosting Statistic Learning with Synthetic Data from Pretrained Large Models

The rapid advancement of generative models, such as Stable Diffusion, raises a key question: how can synthetic data from these models enhance predictive modeling? While they can generate vast amounts of datasets, only a subset meaningfully improves performance. We propose a novel end-to-end framework that generates and systematically filters synthetic data through domain-specific statistical methods, selectively integrating high-quality samples for effective augmentation. Our experiments demonstrate consistent improvements in predictive performance across various settings, highlighting the potential of our framework while underscoring the inherent limitations of generative models for data augmentation. Despite the ability to produce large volumes of synthetic data, the proportion that effectively improves model performance is limited.

stat.ML↗

arXiv2025

SMILE: a universal tool for modulated-enhanced localization microscopy to achieve minimal three-dimensional resolution

Modulation-enhanced localization microscopy (MELM) has demonstrated significant improvements in both lateral and axial localization precision compared to conventional single-molecule localization microscopy (SMLM). However, lateral modulated illumination based MELM (MELMxy) remains fundamentally limited to two-dimensional imaging. Here we present three-dimensional Single-Molecule Modulated Illumination Localization Estimator (SMILE) that synergistically integrates lateral illumination modulation with point spread function engineering. By simultaneously exploiting lateral modulation patterns and an accurate point spread function (PSF) model for 3D localization, SMILE achieves near-theoretical-minimum localization uncertainty, demonstrating an average 4-fold enhancement in lateral precision compared to conventional 3D-SMLM. Crucially, SMILE exhibits exceptional compatibility with diverse PSFs and different illumination patterns with various structures including 4Pi configurations, making it a versatile tool that can be easily adapted for different experimental setups. When integrated with 4Pi microscopy, 4Pi-SMILE shows particular promise for achieving sub-10 nm axial resolution and approaching isotropic resolution. From the simulations and proof-of-concept experiments, we verified the superiority of SMILE over 3D-SMLM and ordinary MELM. We highlight SMILE as a novel methodology and robust framework that holds great potential to significantly promote the development of MELM.

physics.optics↗

arXiv2025

Spatio-Temporal Metric-Semantic Mapping for Persistent Orchard Monitoring: Method and Dataset

Monitoring orchards at the individual tree or fruit level throughout the growth season is crucial for plant phenotyping and horticultural resource optimization, such as chemical use and yield estimation. We present a 4D spatio-temporal metric-semantic mapping system that integrates multi-session measurements to track fruit growth over time. Our approach combines a LiDAR-RGB fusion module for 3D fruit localization with a 4D fruit association method leveraging positional, visual, and topology information for improved data association precision. Evaluated on real orchard data, our method achieves a 96.9% fruit counting accuracy for 1,790 apples across 60 trees, a mean fruit size estimation error of 1.1 cm, and a 23.7% improvement in 4D data association precision over baselines. We publicly release a multimodal dataset covering five fruit species across their growth seasons at https://4d-metric-semantic-mapping.org/

cs.RO↗

arXiv2025

Task-Agnostic Learning to Accomplish New Tasks

Reinforcement Learning (RL) and Imitation Learning (IL) have made great progress in robotic decision-making in recent years. However, these methods show obvious deterioration for new tasks that need to be completed through new combinations of actions. RL methods suffer from reward functions and distribution shifts, while IL methods are limited by expert demonstrations which do not cover new tasks. In contrast, humans can easily complete these tasks with the fragmented knowledge learned from task-agnostic experience. Inspired by this observation, this paper proposes a task-agnostic learning method (TAL for short) that can learn fragmented knowledge only from task-agnostic data to accomplish new tasks. TAL consists of four stages. First, the task-agnostic exploration is performed to collect data from interactions with the environment. The collected data is organized via a knowledge graph. Second, an action feature extractor is proposed and trained using the collected knowledge graph data for task-agnostic fragmented knowledge learning. Third, a candidate action generator is designed, which applies the action feature extractor on a new task to generate multiple candidate action sets. Finally, an action proposal network is designed to produce the probabilities for actions in a new task according to the environmental information. The probabilities are then used to generate order information for selecting actions to be executed from multiple candidate action sets to form the plan. Experiments on a virtual indoor scene show that the proposed method outperforms the state-of-the-art offline RL methods and IL methods by more than 20%.

cs.AI↗

arXiv2025

PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild

This report provides a comprehensive overview of the 4th Pixel-level Video Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025. It summarizes the challenge outcomes, participating methodologies, and future research directions. The challenge features two tracks: MOSE, which focuses on complex scene video object segmentation, and MeViS, which targets motion-guided, language-based video segmentation. Both tracks introduce new, more challenging datasets designed to better reflect real-world scenarios. Through detailed evaluation and analysis, the challenge offers valuable insights into the current state-of-the-art and emerging trends in complex video segmentation. More information can be found on the workshop website: https://pvuw.github.io/.

cs.CV↗

arXiv2025

NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results

This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement. The challenge comprises two tracks: (i) Efficient Video Quality Assessment (KVQ), and (ii) Diffusion-based Image Super-Resolution (KwaiSR). Track 1 aims to advance the development of lightweight and efficient video quality assessment (VQA) models, with an emphasis on eliminating reliance on model ensembles, redundant weights, and other computationally expensive components in the previous IQA/VQA competitions. Track 2 introduces a new short-form UGC dataset tailored for single image super-resolution, i.e., the KwaiSR dataset. It consists of 1,800 synthetically generated S-UGC image pairs and 1,900 real-world S-UGC images, which are split into training, validation, and test sets using a ratio of 8:1:1. The primary objective of the challenge is to drive research that benefits the user experience of short-form UGC platforms such as Kwai and TikTok. This challenge attracted 266 participants and received 18 valid final submissions with corresponding fact sheets, significantly contributing to the progress of short-form UGC VQA and image superresolution. The project is publicly available at https://github.com/lixinustc/KVQE- ChallengeCVPR-NTIRE2025.

eess.IV↗

arXiv2025

Logits DeConfusion with CLIP for Few-Shot Learning

With its powerful visual-language alignment capability, CLIP performs well in zero-shot and few-shot learning tasks. However, we found in experiments that CLIP's logits suffer from serious inter-class confusion problems in downstream tasks, and the ambiguity between categories seriously affects the accuracy. To address this challenge, we propose a novel method called Logits DeConfusion, which effectively learns and eliminates inter-class confusion in logits by combining our Multi-level Adapter Fusion (MAF) module with our Inter-Class Deconfusion (ICD) module. Our MAF extracts features from different levels and fuses them uniformly to enhance feature representation. Our ICD learnably eliminates inter-class confusion in logits with a residual structure. Experimental results show that our method can significantly improve the classification performance and alleviate the inter-class confusion problem. The code is available at https://github.com/LiShuo1001/LDC.

cs.CV↗

arXiv2025

FVOS for MOSE Track of 4th PVUW Challenge: 3rd Place Solution

Video Object Segmentation (VOS) is one of the most fundamental and challenging tasks in computer vision and has a wide range of applications. Most existing methods rely on spatiotemporal memory networks to extract frame-level features and have achieved promising results on commonly used datasets. However, these methods often struggle in more complex real-world scenarios. This paper addresses this issue, aiming to achieve accurate segmentation of video objects in challenging scenes. We propose fine-tuning VOS (FVOS), optimizing existing methods for specific datasets through tailored training. Additionally, we introduce a morphological post-processing strategy to address the issue of excessively large gaps between adjacent objects in single-model predictions. Finally, we apply a voting-based fusion method on multi-scale segmentation results to generate the final output. Our approach achieves J&F scores of 76.81% and 83.92% during the validation and testing stages, respectively, securing third place overall in the MOSE Track of the 4th PVUW challenge 2025.

cs.CV↗

arXiv2025

High-Dimensional Encoding Computational Imaging

High-dimensional imaging technology has demonstrated significant research value across diverse fields, including environmental monitoring, agricultural inspection, and biomedical imaging, through integrating spatial (X*Y), spectral, and polarization detection functionalities. Here, we report a High-Dimensional encoding computational imaging technique, utilizing 4 high-dimensional encoders (HDE1-4) and a high-dimensional neural network (HDNN) to reconstruct 80 high-dimensional images of the target. The system efficiently acquires spectral-polarization information, spanning a wavelength range of 400-800 nm at intervals of 20 nm, obtaining 20 spectral datasets. Each dataset contains images captured at 4 polarization angles (0°, 45°, 90°, and -45°), and the image resolution can reach up to 1280 * 960 pixels. Achieving a reconstruction ratio 1:20. Experimental validation confirms that the spectral reconstruction error consistently remains below 0.14%. Extensive high-dimensional imaging experiments were conducted under indoor and outdoor conditions, showing the system's significant adaptability and robustness in various environments. Compared to traditional imaging devices, such as hyperspectral cameras that could only acquire spectral information, while polarization cameras are limited to polarization imaging, this integrated system successfully overcomes these technological constraints, providing an innovative and efficient solution for high-dimensional optical sensing applications.

physics.optics↗

arXiv2025

Classification of unitary operators by local generatability

Periodically driven (Floquet) systems can exhibit possibilities beyond what can be obtained in equilibrium. Both in Floquet systems and in the related problems of discrete-time quantum walks and quantum cellular automata, a basic distinction arises among unitary time evolution operators: while all physical operators are local, not all are locally generated (i.e., generated by some local Hamiltonian). In this paper, we define the notion of equivalence up to a locally generated unitary in all Altland-Zirnbauer symmetry classes. We then classify noninteracting unitaries in all dimensions on this basis by showing that equivalence up to a locally generated unitary is identical to homotopy equivalence.

cond-mat.mes-hall↗

arXiv2025

Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

Time series analysis is crucial for understanding dynamics of complex systems. Recent advances in foundation models have led to task-agnostic Time Series Foundation Models (TSFMs) and Large Language Model-based Time Series Models (TSLLMs), enabling generalized learning and integrating contextual information. However, their success depends on large, diverse, and high-quality datasets, which are challenging to build due to regulatory, diversity, quality, and quantity constraints. Synthetic data emerge as a viable solution, addressing these challenges by offering scalable, unbiased, and high-quality alternatives. This survey provides a comprehensive review of synthetic data for TSFMs and TSLLMs, analyzing data generation strategies, their role in model pretraining, fine-tuning, and evaluation, and identifying future research directions.

cs.LG↗