OpenRaiser/Intern-Atlas huggingface dataset:
Sparse4DGS: 4D Gaussian Splatting for Sparse-Frame Dynamic Scene Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

Dynamic Gaussian Splatting approaches have achieved remarkable performance for 4D scene reconstruction. However, these approaches rely on dense-frame video sequences for photorealistic reconstruction. In real-world scenarios, due to equipment constraints, sometimes only sparse frames are accessible. In this paper, we propose Sparse4DGS, the first method for sparse-frame dynamic scene reconstruction. We observe that dynamic reconstruction methods fail in both canonical and deformed spaces under sparse-frame settings, especially in areas with high texture richness. Sparse4DGS tackles this challenge by focusing on texture-rich areas. For the deformation network, we propose Texture-Aware Deformation Regularization, which introduces a texture-based depth alignment loss to regulate Gaussian deformation. For the canonical Gaussian field, we introduce Texture-Aware Canonical Optimization, which incorporates texture-based noise into the gradient descent process of canonical Gaussians. Extensive experiments show that when taking sparse frames as inputs, our method outperforms existing dynamic or few-shot techniques on NeRF-Synthetic, HyperNeRF, NeRF-DS, and our iPhone-4D datasets.
Saliency Map-Guided Knowledge Discovery for Subclass Identification with LLM-Based Symbolic Approximations
Venue: arXiv cs.AI 2025
Year: 2025

This paper proposes a novel neuro-symbolic approach for sensor signal-based knowledge discovery, focusing on identifying latent subclasses in time series classification tasks. The approach leverages gradient-based saliency maps derived from trained neural networks to guide the discovery process. Multiclass time series classification problems are transformed into binary classification problems through label subsumption, and classifiers are trained for each of these to yield saliency maps. The input signals, grouped by predicted class, are clustered under three distinct configurations. The centroids of the final set of clusters are provided as input to an LLM for symbolic approximation and fuzzy knowledge graph matching to discover the underlying subclasses of the original multiclass problem. Experimental results on well-established time series classification datasets demonstrate the effectiveness of our saliency map-driven method for knowledge discovery, outperforming signal-only baselines in both clustering and subclass identification.
Generating Novel and Realistic Speakers for Voice Conversion
Venue: arXiv cs.SD 2025
Year: 2025

Voice conversion models modify timbre while preserving paralinguistic features, enabling applications like dubbing and identity protection. However, most VC systems require access to target utterances, limiting their use when target data is unavailable or when users desire conversion to entirely novel, unseen voices. To address this, we introduce a lightweight method SpeakerVAE to generate novel speakers for VC. Our approach uses a deep hierarchical variational autoencoder to model the speaker timbre space. By sampling from the trained model, we generate novel speaker representations for voice synthesis in a VC pipeline. The proposed method is a flexible plug-in module compatible with various VC models, without co-training or fine-tuning of the base VC system. We evaluated our approach with state-of-the-art VC models: FACodec and CosyVoice2. The results demonstrate that our method successfully generates novel, unseen speakers with quality comparable to that of the training speakers.
A Copula-based Semantics-Structure Minimization Framework for QoS Guaranteed Wireless Communications
Venue: arXiv cs.IT 2025
Year: 2025

Current empirically driven research on semantic communication lacks a unified theoretical foundation, preventing quantifiable Quality of Service guarantees, particularly for transmitting minimal structural semantics in emergency scenarios. This deficiency limits its evolution into a predictable engineering science. To address this, we establish a complete theoretical axiomatic basis for this problem. We propose four axioms and rigorously prove that the family of pairwise rank-Copulas is the minimal sufficient representation for minimal structural semantics. Based on this, we construct a semantic distortion metric, centered on the Jensen-Shannon divergence. We then establish the core theoretical boundaries of the framework: sample complexity bounds; rate-distortion bounds; an end-to-end Service Level Agreements theorem; and a semantic source-channel separation theorem, which provides a provable Quality of Service guarantee. Finally, we validate our framework through decoupled experiments, empirically demonstrating that our core metric strictly adheres to our foundational axioms while standard perceptual metrics fail to do so.
TCM-Eval: An Expert-Level Dynamic and Extensible Benchmark for Traditional Chinese Medicine
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated remarkable capabilities in modern medicine, yet their application in Traditional Chinese Medicine (TCM) remains severely limited by the absence of standardized benchmarks and the scarcity of high-quality training data. To address these challenges, we introduce TCM-Eval, the first dynamic and extensible benchmark for TCM, meticulously curated from national medical licensing examinations and validated by TCM experts. Furthermore, we construct a large-scale training corpus and propose Self-Iterative Chain-of-Thought Enhancement (SI-CoTE) to autonomously enrich question-answer pairs with validated reasoning chains through rejection sampling, establishing a virtuous cycle of data and model co-evolution. Using this enriched training data, we develop ZhiMingTang (ZMT), a state-of-the-art LLM specifically designed for TCM, which significantly exceeds the passing threshold for human practitioners. To encourage future research and development, we release a public leaderboard, fostering community engagement and continuous improvement.
LEAD: LLM-enhanced Engine for Author Disambiguation
Venue: arXiv cs.DL 2025
Year: 2025

Author Name Disambiguation (AND) is a long-standing challenge in bibliometrics and scientometrics, as name ambiguity undermines the accuracy of bibliographic databases and the reliability of research evaluation. This study addresses the problem of cross-source disambiguation by linking academic career records from CercaUniversità, the official registry of Italian academics, with author profiles in Scopus. We introduce LEAD (LLM-enhanced Engine for Author Disambiguation), a novel hybrid framework that combines semantic features extracted through Large Language Models (LLMs) with structural evidence derived from co-authorship and citation networks. Using a gold standard of 606 ambiguous cases, we compare five methods: (i) Label Spreading on co-authorship networks; (ii) Bibliographic Coupling on citation networks; (iii) a standalone LLM-based approach; (iv) an LLM-enriched configuration; and (v) the proposed hybrid pipeline. LEAD achieves the best performance (F1 = 96.7%, accuracy = 95.7%) with lower computational cost than full LLM models. Bibliographic Coupling emerges as the fastest and strongest single-source method. These findings demonstrate that integrating semantic and structural signals within a selective hybrid strategy offers a robust and scalable solution to cross-database author identification. Beyond the Italian case, this work highlights the potential of hybrid LLM-based methods to improve data quality and reliability in scientometric analyses.
LLMServingSim2.0: A Unified Simulator for Heterogeneous Hardware and Serving Techniques in LLM Infrastructure
Venue: arXiv cs.DC 2025
Year: 2025

This paper introduces LLMServingSim2.0, a system simulator designed for exploring heterogeneous hardware in large-scale LLM serving systems. LLMServingSim2.0 addresses two key limitations of its predecessor: (1) integrating hardware models into system-level simulators is non-trivial due to the lack of a clear abstraction, and (2) existing simulators support only a narrow subset of serving techniques, leaving no infrastructure that captures the breadth of approaches in modern LLM serving. To overcome these issues, LLMServingSim2.0 adopts trace-driven performance modeling, accompanied by an operator-level latency profiler, enabling the integration of new accelerators with a single command. It further embeds up-to-date serving techniques while exposing flexible interfaces for request routing, cache management, and scheduling policies. In a TPU case study, our profiler requires 18.5x fewer LoC and outperforms the predecessor's hardware-simulator integration, demonstrating LLMServingSim2.0's low-effort hardware extensibility. Our experiments further show that LLMServingSim2.0 reproduces GPU-based LLM serving with 1.9% error, while maintaining practical simulation time, making it a comprehensive platform for both hardware developers and LLM service providers.
Mapping Reduced Accessibility to WASH Facilities in Rohingya Refugee Camps With Sub-Meter Imagery
Venue: arXiv cs.CV 2025
Year: 2025

Access to Water, Sanitation, and Hygiene (WASH) services remains a major public health concern in refugee camps. This study introduces a remote sensing-driven framework to quantify WASH accessibility-specifically to water pumps, latrines, and bathing cubicles-in the Rohingya camps of Cox's Bazar, one of the world's most densely populated displacement settings. Detecting refugee shelters in such emergent camps presents substantial challenges, primarily due to their dense spatial configuration and irregular geometric patterns. Using sub-meter satellite images, we develop a semi-supervised segmentation framework that achieves an F1-score of 76.4% in detecting individual refugee shelters. Applying the framework across multi-year data reveals declining WASH accessibility, driven by rapid refugee population growth and reduced facility availability, rising from 25 people per facility in 2022 to 29.4 in 2025. Gender-disaggregated analysis further shows that women and girls experience reduced accessibility, in scenarios with inadequate safety-related segregation in WASH facilities. These findings suggest the importance of demand-responsive allocation strategies that can identify areas with under-served populations-such as women and girls-and ensure that limited infrastructure serves the greatest number of people in settings with fixed or shrinking budgets. We also discuss the value of high-resolution remote sensing and machine learning to detect inequality and inform equitable resource planning in complex humanitarian environments.
4DSTR: Advancing Generative 4D Gaussians with Spatial-Temporal Rectification for High-Quality and Consistent 4D Generation
Venue: arXiv cs.CV 2025
Year: 2025

Remarkable advances in recent 2D image and 3D shape generation have induced a significant focus on dynamic 4D content generation. However, previous 4D generation methods commonly struggle to maintain spatial-temporal consistency and adapt poorly to rapid temporal variations, due to the lack of effective spatial-temporal modeling. To address these problems, we propose a novel 4D generation network called 4DSTR, which modulates generative 4D Gaussian Splatting with spatial-temporal rectification. Specifically, temporal correlation across generated 4D sequences is designed to rectify deformable scales and rotations and guarantee temporal consistency. Furthermore, an adaptive spatial densification and pruning strategy is proposed to address significant temporal variations by dynamically adding or deleting Gaussian points with the awareness of their pre-frame movements. Extensive experiments demonstrate that our 4DSTR achieves state-of-the-art performance in video-to-4D generation, excelling in reconstruction quality, spatial-temporal consistency, and adaptation to rapid temporal movements.
Robotic versus Human Teleoperation for Remote Ultrasound
Venue: arXiv cs.RO 2025
Year: 2025

Diagnostic medical ultrasound is widely used, safe, and relatively low cost but requires a high degree of expertise to acquire and interpret the images. Personnel with this expertise are often not available outside of larger cities, leading to difficult, costly travel and long wait times for rural populations. To address this issue, tele-ultrasound techniques are being developed, including robotic teleoperation and recently human teleoperation, in which a novice user is remotely guided in a hand-over-hand manner through mixed reality to perform an ultrasound exam. These methods have not been compared, and their relative strengths are unknown. Human teleoperation may be more practical than robotics for small communities due to its lower cost and complexity, but this is only relevant if the performance is comparable. This paper therefore evaluates the differences between human and robotic teleoperation, examining practical aspects such as setup time and flexibility and experimentally comparing performance metrics such as completion time, position tracking, and force consistency. It is found that human teleoperation does not lead to statistically significant differences in completion time or position accuracy, with mean differences of 1.8% and 0.5%, respectively, and provides more consistent force application despite being substantially more practical and accessible.
RobustA: Robust Anomaly Detection in Multimodal Data
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, multimodal anomaly detection methods have demonstrated remarkable performance improvements over video-only models. However, real-world multimodal data is often corrupted due to unforeseen environmental distortions. In this paper, we present the first-of-its-kind work that comprehensively investigates the adverse effects of corrupted modalities on multimodal anomaly detection task. To streamline this work, we propose RobustA, a carefully curated evaluation dataset to systematically observe the impacts of audio and visual corruptions on the overall effectiveness of anomaly detection systems. Furthermore, we propose a multimodal anomaly detection method, which shows notable resilience against corrupted modalities. The proposed method learns a shared representation space for different modalities and employs a dynamic weighting scheme during inference based on the estimated level of corruption. Our work represents a significant step forward in enabling the real-world application of multimodal anomaly detection, addressing situations where the likely events of modality corruptions occur. The proposed evaluation dataset with corrupted modalities and respective extracted features will be made publicly available.
Het 'right to be forgotten' en bijzondere persoonsgegevens: geen ruimte meer voor een belangenafweging? [The 'Right to Be Forgotten' and Sensitive Personal Data: No Room for Balancing?]
Venue: arXiv cs.CY 2025
Year: 2025

An attorney submitted a 'right to be forgotten' delisting request to Google, regarding a blog post about a criminal conviction of the attorney in another country. The Rotterdam District Court ruled that Google may no longer link to the blog post when people search for the attorney's name. The court granted the attorney's request because the blog post concerns a criminal conviction. Personal data regarding criminal convictions are, under Dutch law, special categories of data (sometimes called sensitive data). The reasoning of the court on special categories of data creates problems for freedom of expression. This paper, in Dutch, explores how these problems can be reduced. Google has appealed the decision; the judgment of the Court of Appeals is expected in March 2017.
Singling out people without knowing their names -- Behavioural targeting, pseudonymous data, and the New Data Protection Regulation
Venue: arXiv cs.CY 2025
Year: 2025

Information about millions of people is collected for behavioural targeting, a type of marketing that involves tracking people's online behaviour for targeted advertising. It is hotly debated whether data protection law applies to behavioural targeting. Many behavioural targeting companies say that, as long as they do not tie names to data they hold about individuals, they do not process any personal data, and that, therefore, data protection law does not apply to them. European Data Protection Authorities, however, take the view that a company processes personal data if it uses data to single out a person, even if it cannot tie a name to these data. This paper argues that data protection law should indeed apply to behavioural targeting. Companies can often tie a name to nameless data about individuals. Furthermore, behavioural targeting relies on collecting information about individuals, singling out individuals, and targeting ads to individuals. Many privacy risks remain, regardless of whether companies tie a name to the information they hold about a person. A name is merely one of the identifiers that can be tied to data about a person, and it is not even the most practical identifier for behavioural targeting. Seeing data used to single out a person as personal data fits the rationale for data protection law: protecting fairness and privacy.
Frequency Diverse (FD)-RIS-Enhanced Covert Communications: Defense Against Wiretapping via Joint Distance-Angle Beamforming
Venue: arXiv cs.IT 2025
Year: 2025

In response to the security blind zone challenges faced by traditional reconfigurable intelligent surface (RIS)-aided covert communication (CC) systems, the joint distance-angle beamforming capability of frequency diverse RIS (FD-RIS) shows significant potential for addressing these limitations. Therefore, this paper initially incorporates the FD-RIS into the CC systems and proposes the corresponding CC transmission scheme. Specifically, we first develop the signal processing model of the FD-RIS, which considers effective control of harmonic signals by leveraging the time-delay techniques. The joint distance-angle beamforming capability is then validated through its normalized beampattern. Based on this model, we then construct an FD-RIS-assisted CC system under a multi-warden scenario and derive an approximate closed-form expression for the covert constraints by considering the worst-case eavesdropping conditions and utilizing the logarithmic moment-generating function. An optimization problem is formulated which aims at maximizing the covert user's achievable rate under covert constrains by jointly designing the time delays and modulation frequencies. To tackle this non-convex problem, an iterative algorithm with assured convergence is proposed to effectively solve the time-delay and modulation frequency variables. To evaluate the performance of the proposed scheme, we consider three communication scenarios with varying spatial correlations between the covert user and wardens. Simulation results demonstrate that FD-RIS can significantly improve covert performance, particularly in angular-overlap scenarios where traditional RIS experiences severe degradation. These findings further highlight the effectiveness of FD-RIS in enhancing CC robustness under challenging spatial environments.
When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Despite substantial advances, large language models (LLMs) continue to exhibit hallucinations, generating plausible yet incorrect responses. In this paper, we highlight a critical yet previously underexplored class of hallucinations driven by spurious correlations -- superficial but statistically prominent associations between features (e.g., surnames) and attributes (e.g., nationality) present in the training data. We demonstrate that these spurious correlations induce hallucinations that are confidently generated, immune to model scaling, evade current detection methods, and persist even after refusal fine-tuning. Through systematically controlled synthetic experiments and empirical evaluations on state-of-the-art open-source and proprietary LLMs (including GPT-5), we show that existing hallucination detection methods, such as confidence-based filtering and inner-state probing, fundamentally fail in the presence of spurious correlations. Our theoretical analysis further elucidates why these statistical biases intrinsically undermine confidence-based detection techniques. Our findings thus emphasize the urgent need for new approaches explicitly designed to address hallucinations caused by spurious correlations.
Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training
Venue: arXiv cs.LG 2025
Year: 2025

Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and inference cost. However, most existing RAG methods focus on single-step retrieval, which is often insufficient for answering complex questions that require multi-step search. Recently, multi-step retrieval approaches have emerged, typically involving the fine-tuning of small LLMs to perform multi-step retrieval. This type of fine-tuning is highly resource-intensive and does not enable the use of larger LLMs. In this work, we propose Q-RAG, a novel approach that fine-tunes the Embedder model for multi-step retrieval using reinforcement learning (RL). Q-RAG offers a competitive, resource-efficient alternative to existing multi-step retrieval methods for open-domain question answering and achieves state-of-the-art results on the popular long-context benchmarks Babilong and RULER for contexts up to 10M tokens.
Inference-Time Scaling of Diffusion Models for Infrared Data Generation
Venue: arXiv cs.CV 2025
Year: 2025

Infrared imagery enables temperature-based scene understanding using passive sensors, particularly under conditions of low visibility where traditional RGB imaging fails. Yet, developing downstream vision models for infrared applications is hindered by the scarcity of high-quality annotated data, due to the specialized expertise required for infrared annotation. While synthetic infrared image generation has the potential to accelerate model development by providing large-scale, diverse training data, training foundation-level generative diffusion models in the infrared domain has remained elusive due to limited datasets. In light of such data constraints, we explore an inference-time scaling approach using a domain-adapted CLIP-based verifier for enhanced infrared image generation quality. We adapt FLUX.1-dev, a state-of-the-art text-to-image diffusion model, to the infrared domain by finetuning it on a small sample of infrared images using parameter-efficient techniques. The trained verifier is then employed during inference to guide the diffusion sampling process toward higher quality infrared generations that better align with input text prompts. Empirically, we find that our approach leads to consistent improvements in generation quality, reducing FID scores on the KAIST Multispectral Pedestrian Detection Benchmark dataset by 10% compared to unguided baseline samples. Our results suggest that inference-time guidance offers a promising direction for bridging the domain gap in low-data infrared settings.