OpenRaiser/Intern-Atlas huggingface dataset:
Data Skeleton Learning: Scalable Active Clustering with Sparse Graph Structures
Venue: arXiv cs.LG 2025
Year: 2025

In this work, we focus on the efficiency and scalability of pairwise constraint-based active clustering, crucial for processing large-scale data in applications such as data mining, knowledge annotation, and AI model pre-training. Our goals are threefold: (1) to reduce computational costs for iterative clustering updates; (2) to enhance the impact of user-provided constraints to minimize annotation requirements for precise clustering; and (3) to cut down memory usage in practical deployments. To achieve these aims, we propose a graph-based active clustering algorithm that utilizes two sparse graphs: one for representing relationships between data (our proposed data skeleton) and another for updating this data skeleton. These two graphs work in concert, enabling the refinement of connected subgraphs within the data skeleton to create nested clusters. Our empirical analysis confirms that the proposed algorithm consistently facilitates more accurate clustering with dramatically less input of user-provided constraints, and outperforms its counterparts in terms of computational performance and scalability, while maintaining robustness across various distance metrics.
MESH -- Understanding Videos Like Human: Measuring Hallucinations in Large Video Models
Venue: arXiv cs.CV 2025
Year: 2025

Large Video Models (LVMs) build on the semantic capabilities of Large Language Models (LLMs) and vision modules by integrating temporal information to better understand dynamic video content. Despite their progress, LVMs are prone to hallucinations-producing inaccurate or irrelevant descriptions. Current benchmarks for video hallucination depend heavily on manual categorization of video content, neglecting the perception-based processes through which humans naturally interpret videos. We introduce MESH, a benchmark designed to evaluate hallucinations in LVMs systematically. MESH uses a Question-Answering framework with binary and multi-choice formats incorporating target and trap instances. It follows a bottom-up approach, evaluating basic objects, coarse-to-fine subject features, and subject-action pairs, aligning with human video understanding. We demonstrate that MESH offers an effective and comprehensive approach for identifying hallucinations in videos. Our evaluations show that while LVMs excel at recognizing basic objects and features, their susceptibility to hallucinations increases markedly when handling fine details or aligning multiple actions involving various subjects in longer videos.
Embedding Empathy into Visual Analytics: A Framework for Person-Centred Dementia Care
Venue: arXiv cs.HC 2025
Year: 2025

Dementia care requires healthcare professionals to balance a patient's medical needs with a deep understanding of their personal needs, preferences, and emotional cues. However, current digital tools prioritise quantitative metrics over empathetic engagement,limiting caregivers ability to develop a deeper personal understanding of their patients. This paper presents an empathy centred visualisation framework, developed through a design study, to address this gap. The framework integrates established principles of person centred care with empathy mapping methodologies to encourage deeper engagement. Our methodology provides a structured approach to designing for indirect end users, patients whose experience is shaped by a tool they may not directly interact with. To validate the framework, we conducted evaluations with healthcare professinals, including usability testing of a working prototype and a User Experience Questionnaire study. Results suggest the feasibility of the framework, with participants highlighting its potential to support a more personal and empathetic relationship between medical staff and patients. The work starts to explore how empathy could be systematically embedded into visualisation design, as we contribute to ongoing efforts in the data visualisation community to support human centred, interpretable, and ethically aligned clinical care, addressing the urgent need to improve dementia patients experiences in hospital settings.
Acceptability of AI Assistants for Privacy: Perceptions of Experts and Users on Personalized Privacy Assistants
Venue: arXiv cs.HC 2025
Year: 2025

Individuals increasingly face an overwhelming number of tasks and decisions. To cope with the new reality, there is growing research interest in developing intelligent agents that can effectively assist people across various aspects of daily life in a tailored manner, with privacy emerging as a particular area of application. Artificial intelligence (AI) assistants for privacy, such as personalized privacy assistants (PPAs), have the potential to automatically execute privacy decisions based on users' pre-defined privacy preferences, sparing them the mental effort and time usually spent on each privacy decision. This helps ensure that, even when users feel overwhelmed or resigned about privacy, the decisions made by PPAs still align with their true preferences and best interests. While research has explored possible designs of such agents, user and expert perspectives on the acceptability of such AI-driven solutions remain largely unexplored. In this study, we conducted five focus groups with domain experts (n = 11) and potential users (n = 26) to uncover key themes shaping the acceptance of PPAs. Factors influencing the acceptability of AI assistants for privacy include design elements (such as information sources used by the agent), external conditions (such as regulation and literacy education), and systemic conditions (e.g., public or market providers and the need to avoid monopoly) to PPAs. These findings provide theoretical extensions to technology acceptance models measuring PPAs, insights on design, and policy implications for PPAs, as well as broader implications for the design of AI assistants.
Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal models have achieved remarkable success in natural image segmentation, yet they often underperform when applied to the medical domain. Through extensive study, we attribute this performance gap to the challenges of multimodal fusion, primarily the significant semantic gap between abstract textual prompts and fine-grained medical visual features, as well as the resulting feature dispersion. To address these issues, we revisit the problem from the perspective of semantic aggregation. Specifically, we propose an Expectation-Maximization (EM) Aggregation mechanism and a Text-Guided Pixel Decoder. The former mitigates feature dispersion by dynamically clustering features into compact semantic centers to enhance cross-modal correspondence. The latter is designed to bridge the semantic gap by leveraging domain-invariant textual knowledge to effectively guide deep visual representations. The synergy between these two mechanisms significantly improves the model's generalization ability. Extensive experiments on public cardiac and fundus datasets demonstrate that our method consistently outperforms existing SOTA approaches across multiple domain generalization benchmarks.
EfficientIML: Efficient High-Resolution Image Manipulation Localization
Venue: arXiv cs.CV 2025
Year: 2025

With imaging devices delivering ever-higher resolutions and the emerging diffusion-based forgery methods, current detectors trained only on traditional datasets (with splicing, copy-moving and object removal forgeries) lack exposure to this new manipulation type. To address this, we propose a novel high-resolution SIF dataset of 1200+ diffusion-generated manipulations with semantically extracted masks. However, this also imposes a challenge on existing methods, as they face significant computational resource constraints due to their prohibitive computational complexities. Therefore, we propose a novel EfficientIML model with a lightweight, three-stage EfficientRWKV backbone. EfficientRWKV's hybrid state-space and attention network captures global context and local details in parallel, while a multi-scale supervision strategy enforces consistency across hierarchical predictions. Extensive evaluations on our dataset and standard benchmarks demonstrate that our approach outperforms ViT-based and other SOTA lightweight baselines in localization performance, FLOPs and inference speed, underscoring its suitability for real-time forensic applications.
Interpretability as Alignment: Making Internal Understanding a Design Principle
Venue: arXiv cs.LG 2025
Year: 2025

Frontier AI systems require governance mechanisms that can verify internal alignment, not just behavioral compliance. Private governance mechanisms audits, certification, insurance, and procurement are emerging to complement public regulation, but they require technical substrates that generate verifiable causal evidence about model behavior. This paper argues that mechanistic interpretability provides this substrate. We frame interpretability not as post-hoc explanation but as a design constraint embedding auditability, provenance, and bounded transparency within model architectures. Integrating causal abstraction theory and empirical benchmarks such as MIB and LoBOX, we outline how interpretability-first models can underpin private assurance pipelines and role-calibrated transparency frameworks. This reframing situates interpretability as infrastructure for private AI governance bridging the gap between technical reliability and institutional accountability.
No-Knowledge Alarms for Misaligned LLMs-as-Judges
Venue: arXiv cs.AI 2025
Year: 2025

If we use LLMs as judges to evaluate the complex decisions of other LLMs, who or what monitors the judges? Infinite monitoring chains are inevitable whenever we do not know the ground truth of the decisions by experts and we do not want to trust them. One way to ameliorate our evaluation uncertainty is to exploit the use of logical consistency between disagreeing experts. By observing how LLM judges agree and disagree while grading other LLMs, we can compute the only possible evaluations of their grading ability. For example, if two LLM judges disagree on which tasks a third one completed correctly, they cannot both be 100\% correct in their judgments. This logic can be formalized as a Linear Programming problem in the space of integer response counts for any finite test. We use it here to develop no-knowledge alarms for misaligned LLM judges. The alarms can detect, with no false positives, that at least one member or more of an ensemble of judges are violating a user specified grading ability requirement.
Trace Repair for Temporal Behavior Trees
Venue: arXiv cs.LO 2025
Year: 2025

We present methods for repairing traces against specifications given as temporal behavior trees (TBT). TBT are a specification formalism for action sequences in robotics and cyber-physical systems, where specifications of sub-behaviors, given in signal temporal logic, are composed using operators for sequential and parallel composition, fallbacks, and repetition. Trace repairs are useful to explain failures and as training examples that avoid the observed problems. In principle, repairs can be obtained via mixed-integer linear programming (MILP), but this is far too expensive for practical applications. We present two practical repair strategies: (1) incremental repair, which reduces the MILP by splitting the trace into segments, and (2) landmark-based repair, which solves the repair problem iteratively using TBT's robust semantics as a heuristic that approximates MILP with more efficient linear programming. In our experiments, we were able to repair traces with more than 25,000 entries in under ten minutes, while MILP runs out of memory.
X-Part: high fidelity and structure coherent shape decomposition
Venue: arXiv cs.GR 2025
Year: 2025

Generating 3D shapes at part level is pivotal for downstream applications such as mesh retopology, UV mapping, and 3D printing. However, existing part-based generation methods often lack sufficient controllability and suffer from poor semantically meaningful decomposition. To this end, we introduce X-Part, a controllable generative model designed to decompose a holistic 3D object into semantically meaningful and structurally coherent parts with high geometric fidelity. X-Part exploits the bounding box as prompts for the part generation and injects point-wise semantic features for meaningful decomposition. Furthermore, we design an editable pipeline for interactive part generation. Extensive experimental results show that X-Part achieves state-of-the-art performance in part-level shape generation. This work establishes a new paradigm for creating production-ready, editable, and structurally sound 3D assets. Codes will be released for public research.
Rotatable Array-Aided Hybrid Beamforming for Integrated Sensing and Communication
Venue: arXiv cs.ET 2025
Year: 2025

Six-dimensional movable antenna (6DMA) technology has been proposed to enhance the performance of Integrated Sensing and Communication (ISAC) systems. However, within 6DMA-related research, studies on the ISAC system based on rotatable array (RA) remains relatively limited. Given the significant advantages of hybrid beamforming technology in balancing system performance and hardware complexity, this paper focuses on a channel model that accounts for the efficiency of the antenna radiation pattern and studies the sub-connected hybrid beamforming design for multi-user RA-aided ISAC systems. Aiming at the non-convex nature with coupled variables in this problem, this paper transforms the complex fractional objective function using the Fractional Programming (FP) method, and then proposes an algorithm based on the Alternating Optimization (AO) framework, which achieves optimization by alternately solving five subproblems. For the analog beamforming optimization subproblem, we adopt Singular Value Decomposition (SVD) method to transform the objective function, thereby deriving the closed-form update expression for the analog beamforming matrix. For the antenna rotation optimization subproblem, we derive the closed-form derivative expression of the array rotation angle and propose a two-stage Gradient Ascent (GA) based method to optimize the antenna rotation angle. Extensive simulation results demonstrate the effectiveness of the proposed RA-aided hybrid beamforming design method. It not only significantly improves the overall system performance while reducing hardware costs, but also achieves performance comparable to that of the fully-digital beamforming design with fixed-position antennas (FPA) under specific parameter configurations.
Replicable Reinforcement Learning with Linear Function Approximation
Venue: arXiv cs.LG 2025
Year: 2025

Replication of experimental results has been a challenge faced by many scientific disciplines, including the field of machine learning. Recent work on the theory of machine learning has formalized replicability as the demand that an algorithm produce identical outcomes when executed twice on different samples from the same distribution. Provably replicable algorithms are especially interesting for reinforcement learning (RL), where algorithms are known to be unstable in practice. While replicable algorithms exist for tabular RL settings, extending these guarantees to more practical function approximation settings has remained an open problem. In this work, we make progress by developing replicable methods for linear function approximation in RL. We first introduce two efficient algorithms for replicable random design regression and uncentered covariance estimation, each of independent interest. We then leverage these tools to provide the first provably efficient replicable RL algorithms for linear Markov decision processes in both the generative model and episodic settings. Finally, we evaluate our algorithms experimentally and show how they can inspire more consistent neural policies.
FractalPINN-Flow: A Fractal-Inspired Network for Unsupervised Optical Flow Estimation with Total Variation Regularization
Venue: arXiv cs.CV 2025
Year: 2025

We present FractalPINN-Flow, an unsupervised deep learning framework for dense optical flow estimation that learns directly from consecutive grayscale frames without requiring ground truth. The architecture centers on the Fractal Deformation Network (FDN) - a recursive encoder-decoder inspired by fractal geometry and self-similarity. Unlike traditional CNNs with sequential downsampling, FDN uses repeated encoder-decoder nesting with skip connections to capture both fine-grained details and long-range motion patterns. The training objective is based on a classical variational formulation using total variation (TV) regularization. Specifically, we minimize an energy functional that combines $L^1$ and $L^2$ data fidelity terms to enforce brightness constancy, along with a TV term that promotes spatial smoothness and coherent flow fields. Experiments on synthetic and benchmark datasets show that FractalPINN-Flow produces accurate, smooth, and edge-preserving optical flow fields. The model is especially effective for high-resolution data and scenarios with limited annotations.
Reshaping the Forward-Forward Algorithm with a Similarity-Based Objective
Venue: arXiv cs.LG 2025
Year: 2025

Backpropagation is the pivotal algorithm underpinning the success of artificial neural networks, yet it has critical limitations such as biologically implausible backward locking and global error propagation. To circumvent these constraints, the Forward-Forward algorithm was proposed as a more biologically plausible method that replaces the backward pass with an additional forward pass. Despite this advantage, the Forward-Forward algorithm significantly trails backpropagation in accuracy, and its optimal form exhibits low inference efficiency due to multiple forward passes required. In this work, the Forward-Forward algorithm is reshaped through its integration with similarity learning frameworks, eliminating the need for multiple forward passes during inference. This proposed algorithm is named Forward-Forward Algorithm Unified with Similarity-based Tuplet loss (FAUST). Empirical evaluations on MNIST, Fashion-MNIST, and CIFAR-10 datasets indicate that FAUST substantially improves accuracy, narrowing the gap with backpropagation. On CIFAR-10, FAUST achieves 56.22\% accuracy with a simple multi-layer perceptron architecture, approaching the backpropagation benchmark of 57.63\% accuracy.
One Model, Two Minds: A Context-Gated Graph Learner that Recreates Human Biases
Venue: arXiv cs.AI 2025
Year: 2025

We introduce a novel Theory of Mind (ToM) framework inspired by dual-process theories from cognitive science, integrating a fast, habitual graph-based reasoning system (System 1), implemented via graph convolutional networks (GCNs), and a slower, context-sensitive meta-adaptive learning system (System 2), driven by meta-learning techniques. Our model dynamically balances intuitive and deliberative reasoning through a learned context gate mechanism. We validate our architecture on canonical false-belief tasks and systematically explore its capacity to replicate hallmark cognitive biases associated with dual-process theory, including anchoring, cognitive-load fatigue, framing effects, and priming effects. Experimental results demonstrate that our dual-process approach closely mirrors human adaptive behavior, achieves robust generalization to unseen contexts, and elucidates cognitive mechanisms underlying reasoning biases. This work bridges artificial intelligence and cognitive theory, paving the way for AI systems exhibiting nuanced, human-like social cognition and adaptive decision-making capabilities.
Computational Imaging for Enhanced Computer Vision
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a comprehensive survey of computational imaging (CI) techniques and their transformative impact on computer vision (CV) applications. Conventional imaging methods often fail to deliver high-fidelity visual data in challenging conditions, such as low light, motion blur, or high dynamic range scenes, thereby limiting the performance of state-of-the-art CV systems. Computational imaging techniques, including light field imaging, high dynamic range (HDR) imaging, deblurring, high-speed imaging, and glare mitigation, address these limitations by enhancing image acquisition and reconstruction processes. This survey systematically explores the synergies between CI techniques and core CV tasks, including object detection, depth estimation, optical flow, face recognition, and keypoint detection. By analyzing the relationships between CI methods and their practical contributions to CV applications, this work highlights emerging opportunities, challenges, and future research directions. We emphasize the potential for task-specific, adaptive imaging pipelines that improve robustness, accuracy, and efficiency in real-world scenarios, such as autonomous navigation, surveillance, augmented reality, and robotics.
The More You Automate, the Less You See: Hidden Pitfalls of AI Scientist Systems
Venue: arXiv cs.AI 2025
Year: 2025

AI scientist systems, capable of autonomously executing the full research workflow from hypothesis generation and experimentation to paper writing, hold significant potential for accelerating scientific discovery. However, the internal workflow of these systems have not been closely examined. This lack of scrutiny poses a risk of introducing flaws that could undermine the integrity, reliability, and trustworthiness of their research outputs. In this paper, we identify four potential failure modes in contemporary AI scientist systems: inappropriate benchmark selection, data leakage, metric misuse, and post-hoc selection bias. To examine these risks, we design controlled experiments that isolate each failure mode while addressing challenges unique to evaluating AI scientist systems. Our assessment of two prominent open-source AI scientist systems reveals the presence of several failures, across a spectrum of severity, which can be easily overlooked in practice. Finally, we demonstrate that access to trace logs and code from the full automated workflow enables far more effective detection of such failures than examining the final paper alone. We thus recommend journals and conferences evaluating AI-generated research to mandate submission of these artifacts alongside the paper to ensure transparency, accountability, and reproducibility.
PAnDA: Rethinking Metric Differential Privacy Optimization at Scale with Anchor-Based Approximation
Venue: arXiv cs.CR 2025
Year: 2025

Metric Differential Privacy (mDP) extends the local differential privacy (LDP) framework to metric spaces, enabling more nuanced privacy protection for data such as geo-locations. However, existing mDP optimization methods, particularly those based on linear programming (LP), face scalability challenges due to the quadratic growth in decision variables. In this paper, we propose Perturbation via Anchor-based Distributed Approximation (PAnDA), a scalable two-phase framework for optimizing metric differential privacy (mDP). To reduce computational overhead, PAnDA allows each user to select a small set of anchor records, enabling the server to solve a compact linear program over a reduced domain. We introduce three anchor selection strategies, exponential decay (PAnDA-e), power-law decay (PAnDA-p), and logistic decay (PAnDA-l), and establish theoretical guarantees under a relaxed privacy notion called probabilistic mDP (PmDP). Experiments on real-world geo-location datasets demonstrate that PAnDA scales to secret domains with up to 5,000 records, two times larger than prior LP-based methods, while providing theoretical guarantees for both privacy and utility.