OpenRaiser/Intern-Atlas huggingface dataset:
Toward Better Generalisation in Uncertainty Estimators: Leveraging Data-Agnostic Features
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLMs) often generate responses that are factually incorrect yet expressed with high confidence, which can pose serious risks for end users. To address this, it is essential for LLMs not only to produce answers but also to provide accurate estimates of their correctness. Uncertainty quantification methods have been introduced to assess the quality of LLM outputs, with factual accuracy being a key aspect of that quality. Among these methods, those that leverage hidden states to train probes have shown particular promise, as these internal representations encode information relevant to the factuality of responses, making this approach the focus of this paper. However, the probe trained on the hidden states of one dataset often struggles to generalise to another dataset of a different task or domain. To address this limitation, we explore combining data-agnostic features with hidden-state features and assess whether this hybrid feature set enhances out-of-domain performance. We further examine whether selecting only the most informative hidden-state features, thereby discarding task-specific noise, enables the data-agnostic features to contribute more effectively. The experiment results indicate that although introducing data-agnostic features generally enhances generalisation performance in most cases, in certain scenarios their inclusion degrades performance. A similar pattern emerges when retaining only the most important hidden-state features - adding data-agnostic features does not consistently further enhance performance compared to using the full set of hidden-state features. A closer analysis reveals that, in some specific cases, the trained probe underweights the data-agnostic features relative to the hidden-state features, which we believe is the main reason why the results are inconclusive.
Gaussian-LIC2: LiDAR-Inertial-Camera Gaussian Splatting SLAM
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents the first photo-realistic LiDAR-Inertial-Camera Gaussian Splatting SLAM system that simultaneously addresses visual quality, geometric accuracy, and real-time performance. The proposed method performs robust and accurate pose estimation within a continuous-time trajectory optimization framework, while incrementally reconstructing a 3D Gaussian map using camera and LiDAR data, all in real time. The resulting map enables high-quality, real-time novel view rendering of both RGB images and depth maps. To effectively address under-reconstruction in regions not covered by the LiDAR, we employ a lightweight zero-shot depth model that synergistically combines RGB appearance cues with sparse LiDAR measurements to generate dense depth maps. The depth completion enables reliable Gaussian initialization in LiDAR-blind areas, significantly improving system applicability for sparse LiDAR sensors. To enhance geometric accuracy, we use sparse but precise LiDAR depths to supervise Gaussian map optimization and accelerate it with carefully designed CUDA-accelerated strategies. Furthermore, we explore how the incrementally reconstructed Gaussian map can improve the robustness of odometry. By tightly incorporating photometric constraints from the Gaussian map into the continuous-time factor graph optimization, we demonstrate improved pose estimation under LiDAR degradation scenarios. We also showcase downstream applications via extending our elaborate system, including video frame interpolation and fast 3D mesh extraction. To support rigorous evaluation, we construct a dedicated LiDAR-Inertial-Camera dataset featuring ground-truth poses, depth maps, and extrapolated trajectories for assessing out-of-sequence novel view synthesis. Both the dataset and code will be made publicly available on project page https://xingxingzuo.github.io/gaussian_lic2.
Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models
Venue: arXiv cs.LG 2025
Year: 2025

Urban socioeconomic modeling has predominantly concentrated on extensive location and neighborhood-based features, relying on the localized population footprint. However, networks in urban systems are common, and many urban modeling methods don't account for network-based effects. In this study, we propose using commute information records from the census as a reliable and comprehensive source to construct mobility networks across cities. Leveraging deep learning architectures, we employ these commute networks across U.S. metro areas for socioeconomic modeling. We show that mobility network structures provide significant predictive performance without considering any node features. Consequently, we use mobility networks to present a supervised learning framework to model a city's socioeconomic indicator directly, combining Graph Neural Network and Vanilla Neural Network models to learn all parameters in a single learning pipeline. Our experiments in 12 major U.S. cities show the proposed model outperforms previous conventional machine learning models. This work provides urban researchers methods to incorporate network effects in urban modeling and informs stakeholders of wider network-based effects in urban policymaking and planning.
CTR-Guided Generative Query Suggestion in Conversational Search
Venue: arXiv cs.IR 2025
Year: 2025

Generating effective query suggestions in conversational search requires aligning model outputs with user preferences, which is challenging due to sparse and noisy click signals. We propose GQS, a generative framework that integrates click modeling and preference optimization to enhance real-world user engagement. GQS consists of three key components: (1) a Multi-Source CTR Modeling module that captures diverse contextual signals to estimate fine-grained click-through rates; (2) a Diversity-Aware Preference Alignment strategy using CTR-weighted Direct Preference Optimization (DPO), which balances relevance and semantic diversity; and (3) a CTR-Calibrated Iterative Optimization process that jointly refines the CTR and generation models across training rounds. Experiments on two real-world tasks demonstrate that GQS outperforms strong baselines in CTR, relevance, and diversity.
Attention-Guided Multi-Scale Local Reconstruction for Point Clouds via Masked Autoencoder Self-Supervised Learning
Venue: arXiv cs.GR 2025
Year: 2025

Self-supervised learning has emerged as a prominent research direction in point cloud processing. While existing models predominantly concentrate on reconstruction tasks at higher encoder layers, they often neglect the effective utilization of low-level local features, which are typically employed solely for activation computations rather than directly contributing to reconstruction tasks. To overcome this limitation, we introduce PointAMaLR, a novel self-supervised learning framework that enhances feature representation and processing accuracy through attention-guided multi-scale local reconstruction. PointAMaLR implements hierarchical reconstruction across multiple local regions, with lower layers focusing on fine-scale feature restoration while upper layers address coarse-scale feature reconstruction, thereby enabling complex inter-patch interactions. Furthermore, to augment feature representation capabilities, we incorporate a Local Attention (LA) module in the embedding layer to enhance semantic feature understanding. Comprehensive experiments on benchmark datasets ModelNet and ShapeNet demonstrate PointAMaLR's superior accuracy and quality in both classification and reconstruction tasks. Moreover, when evaluated on the real-world dataset ScanObjectNN and the 3D large scene segmentation dataset S3DIS, our model achieves highly competitive performance metrics. These results not only validate PointAMaLR's effectiveness in multi-scale semantic understanding but also underscore its practical applicability in real-world scenarios.
Enhancing Robustness of LLM-Driven Multi-Agent Systems through Randomized Smoothing
Venue: arXiv cs.AI 2025
Year: 2025

This paper presents a defense framework for enhancing the safety of large language model (LLM) empowered multi-agent systems (MAS) in safety-critical domains such as aerospace. We apply randomized smoothing, a statistical robustness certification technique, to the MAS consensus context, enabling probabilistic guarantees on agent decisions under adversarial influence. Unlike traditional verification methods, our approach operates in black-box settings and employs a two-stage adaptive sampling mechanism to balance robustness and computational efficiency. Simulation results demonstrate that our method effectively prevents the propagation of adversarial behaviors and hallucinations while maintaining consensus performance. This work provides a practical and scalable path toward safe deployment of LLM-based MAS in real-world, high-stakes environments.
Relational inductive biases on attention mechanisms
Venue: arXiv cs.LG 2025
Year: 2025

Inductive learning aims to construct general models from specific examples, guided by biases that influence hypothesis selection and determine generalization capacity. In this work, we focus on characterizing the relational inductive biases present in attention mechanisms, understood as assumptions about the underlying relationships between data elements. From the perspective of geometric deep learning, we analyze the most common attention mechanisms in terms of their equivariance properties with respect to permutation subgroups, which allows us to propose a classification based on their relational biases. Under this perspective, we show that different attention layers are characterized by the underlying relationships they assume on the input data.
Graph Neural Networks as a Substitute for Transformers in Single-Cell Transcriptomics
Venue: arXiv cs.LG 2025
Year: 2025

Graph Neural Networks (GNNs) and Transformers share significant similarities in their encoding strategies for interacting with features from nodes of interest, where Transformers use query-key scores and GNNs use edges. Compared to GNNs, which are unable to encode relative positions, Transformers leverage dynamic attention capabilities to better represent relative relationships, thereby becoming the standard backbones in large-scale sequential pre-training. However, the subtle difference prompts us to consider: if positions are no longer crucial, could we substitute Transformers with Graph Neural Networks in some fields such as Single-Cell Transcriptomics? In this paper, we first explore the similarities and differences between GNNs and Transformers, specifically in terms of relative positions. Additionally, we design a synthetic example to illustrate their equivalence where there are no relative positions between tokens in the sample. Finally, we conduct extensive experiments on a large-scale position-agnostic dataset-single-cell transcriptomics-finding that GNNs achieve competitive performance compared to Transformers while consuming fewer computation resources. These findings provide novel insights for researchers in the field of single-cell transcriptomics, challenging the prevailing notion that the Transformer is always the optimum choice.
BlowPrint: Blow-Based Multi-Factor Biometrics for Smartphone User Authentication
Venue: arXiv cs.CR 2025
Year: 2025

Biometric authentication is a widely used security mechanism that leverages unique physiological or behavioral characteristics to authenticate users. In multi-factor biometrics (MFB), multiple biometric modalities, e.g., physiological and behavioral, are integrated to mitigate the limitations inherent in single-factor biometrics. The main challenge in MFB lies in identifying novel behavioral techniques capable of meeting critical criteria, including high accuracy, high usability, non-invasiveness, resilience against spoofing attacks, and low use of computational resources. Despite ongoing advancements, current behavioral biometric techniques often fall short of fulfilling one or more of these requirements. In this work, we propose BlowPrint, a novel behavioral biometric technique that allows us to authenticate users based on their phone blowing behaviors. In brief, we assume that the way users blow on a phone screen can produce distinctive acoustic patterns, which can serve as a unique biometric identifier for effective user authentication. It can also be seamlessly integrated with physiological techniques, such as facial recognition, to enhance its robustness and security. To assess BlowPrint's effectiveness, we conduct an empirical study involving 50 participants from whom we collect blow-acoustic and facial feature data. Subsequently, we compute the similarity scores of the two modalities using various similarity algorithms and combine them through score-level fusion. Finally, we compute the accuracy using a machine learning-based classifier. As a result, the proposed method demonstrates an accuracy of 99.35% for blow acoustics, 99.96% for facial recognition, and 99.82% for the combined approach. The experimental results demonstrate BlowPrint's high effectiveness in terms of authentication accuracy, spoofing attack resilience, usability, non-invasiveness, and other aspects.
Pedestrian Intention Prediction via Vision-Language Foundation Models
Venue: arXiv cs.CV 2025
Year: 2025

Prediction of pedestrian crossing intention is a critical function in autonomous vehicles. Conventional vision-based methods of crossing intention prediction often struggle with generalizability, context understanding, and causal reasoning. This study explores the potential of vision-language foundation models (VLFMs) for predicting pedestrian crossing intentions by integrating multimodal data through hierarchical prompt templates. The methodology incorporates contextual information, including visual frames, physical cues observations, and ego-vehicle dynamics, into systematically refined prompts to guide VLFMs effectively in intention prediction. Experiments were conducted on three common datasets-JAAD, PIE, and FU-PIP. Results demonstrate that incorporating vehicle speed, its variations over time, and time-conscious prompts significantly enhances the prediction accuracy up to 19.8%. Additionally, optimised prompts generated via an automatic prompt engineering framework yielded 12.5% further accuracy gains. These findings highlight the superior performance of VLFMs compared to conventional vision-based models, offering enhanced generalisation and contextual understanding for autonomous driving applications.
iBreath: Usage Of Breathing Gestures as Means of Interactions
Venue: arXiv cs.HC 2025
Year: 2025

Breathing is a spontaneous but controllable body function that can be used for hands-free interaction. Our work introduces "iBreath", a novel system to detect breathing gestures similar to clicks using bio-impedance. We evaluated iBreath's accuracy and user experience using two lab studies (n=34). Our results show high detection accuracy (F1-scores > 95.2%). Furthermore, the users found the gestures easy to use and comfortable. Thus, we developed eight practical guidelines for the future development of breathing gestures. For example, designers can train users on new gestures within just 50 seconds (five trials), and achieve robust performance with both user-dependent and user-independent models trained on data from 21 participants, each yielding accuracies above 90%. Users preferred single clicks and disliked triple clicks. The median gesture duration is 3.5-5.3 seconds. Our work provides solid ground for researchers to experiment with creating breathing gestures and interactions.
Hijacking JARVIS: Benchmarking Mobile GUI Agents against Unprivileged Third Parties
Venue: arXiv cs.CR 2025
Year: 2025

Mobile GUI agents are designed to autonomously execute diverse device-control tasks by interpreting and interacting with mobile screens. Despite notable advancements, their resilience in real-world scenarios where screen content may be partially manipulated by untrustworthy third parties remains largely unexplored. Owing to their black-box and autonomous nature, these agents are vulnerable to manipulations that could compromise user devices. In this work, we present the first systematic investigation into the vulnerabilities of mobile GUI agents. We introduce a scalable attack simulation framework AgentHazard, which enables flexible and targeted modifications of screen content within existing applications. Leveraging this framework, we develop a comprehensive benchmark suite comprising both a dynamic task execution environment and a static dataset of vision-language-action tuples, totaling over 3,000 attack scenarios. The dynamic environment encompasses 58 reproducible tasks in an emulator with various types of hazardous UI content, while the static dataset is constructed from 210 screenshots collected from 14 popular commercial apps. Importantly, our content modifications are designed to be feasible for unprivileged third parties. We evaluate 7 widely-used mobile GUI agents and 5 common backbone models using our benchmark. Our findings reveal that all examined agents are significantly influenced by misleading third-party content (with an average misleading rate of 28.8% in human-crafted attack scenarios) and that their vulnerabilities are closely linked to the employed perception modalities and backbone LLMs. Furthermore, we assess training-based mitigation strategies, highlighting both the challenges and opportunities for enhancing the robustness of mobile GUI agents. Our code and data will be released at https://agenthazard.github.io.
Ethics by Design: A Lifecycle Framework for Trustworthy AI in Medical Imaging From Transparent Data Governance to Clinically Validated Deployment
Venue: arXiv cs.CY 2025
Year: 2025

The integration of artificial intelligence (AI) in medical imaging raises crucial ethical concerns at every stage of its development, from data collection to deployment. Addressing these concerns is essential for ensuring that AI systems are developed and implemented in a manner that respects patient rights and promotes fairness. This study aims to explore the ethical implications of AI in medical imaging, focusing on five key stages: data collection, data processing, model training, model evaluation, and deployment. The goal is to evaluate how these stages adhere to fundamental ethical principles, including data privacy, fairness, transparency, accountability, and autonomy. An analytical approach was employed to examine the ethical challenges associated with each stage of AI development. We reviewed existing literature, guidelines, and regulations concerning AI ethics in healthcare and identified critical ethical issues at each stage. The study outlines specific inquiries and principles for each phase of AI development. The findings highlight key ethical issues: ensuring patient consent and anonymization during data collection, addressing biases in model training, ensuring transparency and fairness during model evaluation, and the importance of continuous ethical assessments during deployment. The analysis also emphasizes the impact of accessibility issues on different stakeholders, including private, public, and third-party entities. The study concludes that ethical considerations must be systematically integrated into each stage of AI development in medical imaging. By adhering to these ethical principles, AI systems can be made more robust, transparent, and aligned with patient care and data control. We propose tailored ethical inquiries and strategies to support the creation of ethically sound AI systems in medical imaging.
MoReMouse: Monocular Reconstruction of Laboratory Mouse
Venue: arXiv cs.CV 2025
Year: 2025

Laboratory mice, particularly the C57BL/6 strain, are essential animal models in biomedical research. However, accurate 3D surface motion reconstruction of mice remains a significant challenge due to their complex non-rigid deformations, textureless fur-covered surfaces, and the lack of realistic 3D mesh models. Moreover, existing visual datasets for mice reconstruction only contain sparse viewpoints without 3D geometries. To fill the gap, we introduce MoReMouse, the first monocular dense 3D reconstruction network specifically designed for C57BL/6 mice. To achieve high-fidelity 3D reconstructions, we present three key innovations. First, we create the first high-fidelity, dense-view synthetic dataset for C57BL/6 mice by rendering a realistic, anatomically accurate Gaussian mouse avatar. Second, MoReMouse leverages a transformer-based feedforward architecture combined with triplane representation, enabling high-quality 3D surface generation from a single image, optimized for the intricacies of small animal morphology. Third, we propose geodesic-based continuous correspondence embeddings on the mouse surface, which serve as strong semantic priors, improving surface consistency and reconstruction stability, especially in highly dynamic regions like limbs and tail. Through extensive quantitative and qualitative evaluations, we demonstrate that MoReMouse significantly outperforms existing open-source methods in both accuracy and robustness.
M$^3$-Med: A Benchmark for Multi-lingual, Multi-modal, and Multi-hop Reasoning in Medical Instructional Video Understanding
Venue: arXiv cs.CV 2025
Year: 2025

With the rapid progress of artificial intelligence (AI) in multi-modal understanding, there is increasing potential for video comprehension technologies to support professional domains such as medical education. However, existing benchmarks suffer from two primary limitations: (1) Linguistic Singularity: they are largely confined to English, neglecting the need for multilingual resources; and (2) Shallow Reasoning: their questions are often designed for surface-level information retrieval, failing to properly assess deep multi-modal integration. To address these limitations, we present M3-Med, the first benchmark for Multi-lingual, Multi-modal, and Multi-hop reasoning in Medical instructional video understanding. M3-Med consists of medical questions paired with corresponding video segments, annotated by a team of medical experts. A key innovation of M3-Med is its multi-hop reasoning task, which requires a model to first locate a key entity in the text, then find corresponding visual evidence in the video, and finally synthesize information across both modalities to derive the answer. This design moves beyond simple text matching and poses a substantial challenge to a model's deep cross-modal understanding capabilities. We define two tasks: Temporal Answer Grounding in Single Video (TAGSV) and Temporal Answer Grounding in Video Corpus (TAGVC). We evaluated several state-of-the-art models and Large Language Models (LLMs) on M3-Med. The results reveal a significant performance gap between all models and human experts, especially on the complex multi-hop questions where model performance drops sharply. M3-Med effectively highlights the current limitations of AI models in deep cross-modal reasoning within specialized domains and provides a new direction for future research.