OpenRaiser/Intern-Atlas huggingface dataset:
EgoSurgery-HTS: A Dataset for Egocentric Hand-Tool Segmentation in Open Surgery Videos
Venue: arXiv cs.CV 2025
Year: 2025

Egocentric open-surgery videos capture rich, fine-grained details essential for accurately modeling surgical procedures and human behavior in the operating room. A detailed, pixel-level understanding of hands and surgical tools is crucial for interpreting a surgeon's actions and intentions. We introduce EgoSurgery-HTS, a new dataset with pixel-wise annotations and a benchmark suite for segmenting surgical tools, hands, and interacting tools in egocentric open-surgery videos. Specifically, we provide a labeled dataset for (1) tool instance segmentation of 14 distinct surgical tools, (2) hand instance segmentation, and (3) hand-tool segmentation to label hands and the tools they manipulate. Using EgoSurgery-HTS, we conduct extensive evaluations of state-of-the-art segmentation methods and demonstrate significant improvements in the accuracy of hand and hand-tool segmentation in egocentric open-surgery videos compared to existing datasets. The dataset will be released at https://github.com/Fujiry0/EgoSurgery.
Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm
Venue: arXiv cs.RO 2025
Year: 2025

In this work, we present a novel cooperative multi-agent reinforcement learning method called \textbf{Loc}ality based \textbf{Fac}torized \textbf{M}ulti-Agent \textbf{A}ctor-\textbf{C}ritic (Loc-FACMAC). Existing state-of-the-art algorithms, such as FACMAC, rely on global reward information, which may not accurately reflect the quality of individual robots' actions in decentralized systems. We integrate the concept of locality into critic learning, where strongly related robots form partitions during training. Robots within the same partition have a greater impact on each other, leading to more precise policy evaluation. Additionally, we construct a dependency graph to capture the relationships between robots, facilitating the partitioning process. This approach mitigates the curse of dimensionality and prevents robots from using irrelevant information. Our method improves existing algorithms by focusing on local rewards and leveraging partition-based learning to enhance training efficiency and performance. We evaluate the performance of Loc-FACMAC in three environments: Hallway, Multi-cartpole, and Bounded-Cooperative-Navigation. We explore the impact of partition sizes on the performance and compare the result with baseline MARL algorithms such as LOMAQ, FACMAC, and QMIX. The experiments reveal that, if the locality structure is defined properly, Loc-FACMAC outperforms these baseline algorithms up to 108\%, indicating that exploiting the locality structure in the actor-critic framework improves the MARL performance.
Coarse-graining Directed Networks with Ergodic Sets Preserving Diffusive Dynamics
Venue: arXiv cs.SI 2025
Year: 2025

In this paper, we introduce ergodic sets, subsets of nodes of the networks that are dynamically disjoint from the rest of the network (i.e. that can never be reached or left following to the network dynamics). We connect their definition to purely structural considerations of the network and study some of their basic properties. We study numerically the presence of such structures in a number of synthetic network models and in classes of networks from a variety of real-world applications, and we use them to present a compression algorithm that preserve the random walk diffusive dynamics of the original network.
MODIS: Multi-Omics Data Integration for Small and unpaired datasets
Venue: arXiv cs.LG 2025
Year: 2025

An important objective in computational biology is the efficient integration of multi-omics data. The task of integration comes with challenges: multi-omics data are most often unpaired (requiring diagonal integration), partially labeled with information about biological conditions, and in some situations such as rare diseases, only very small datasets are available. We present MODIS, a semi supervised framework designed to account for these particular challenges. To address the challenge of very small datasets, we propose to exploit information contained in larger multi-omics databases by training our model on a large reference database and a small target dataset simultaneously, effectively turning the problem of transfer learning into a problem of learning with class imbalance. MODIS performs diagonal integration on unpaired samples, leveraging class-labels to align modalities despite class imbalance and data scarcity. The architecture combines multiple variational auto-encoders, a class classifier and an adversarially trained modality classifier. To ensure training stability, we adapted a regularized relativistic GAN loss to this setting. We first validate MODIS on a synthetic dataset to assess the level of supervision needed for accurate alignment and to quantify the impact of class imbalance on predictive performance. We then apply our approach to the large public TCGA database, considering between 10 and 34 classes (cancer types and normal tissue). MODIS demonstrates high prediction accuracy, robust performance with limited supervision, and stability to class imbalance. These results position MODIS as a promising solution for challenging integration scenarios, particularly diagonal integration with a small number of samples, typical of rare diseases studies. The code is available at https://github.com/VILLOUTREIXLab/MODIS.
Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations
Venue: arXiv cs.AI 2025
Year: 2025

The emergence of large language models offers new possibilities for structured exploration of scientific knowledge. Rather than viewing scientific discovery as isolated ideas or content, we propose a structured approach that emphasizes the role of method combinations in shaping disruptive insights. Specifically, we investigate how knowledge unit--especially those tied to methodological design--can be modeled and recombined to yield research breakthroughs. Our proposed framework addresses two key challenges. First, we introduce a contrastive learning-based mechanism to identify distinguishing features of historically disruptive method combinations within problem-driven contexts. Second, we propose a reasoning-guided Monte Carlo search algorithm that leverages the chain-of-thought capability of LLMs to identify promising knowledge recombinations for new problem statements.Empirical studies across multiple domains show that the framework is capable of modeling the structural dynamics of innovation and successfully highlights combinations with high disruptive potential. This research provides a new path for computationally guided scientific ideation grounded in structured reasoning and historical data modeling.
A semantic communication-based workload-adjustable transceiver for wireless AI-generated content (AIGC) delivery
Venue: arXiv cs.LG 2025
Year: 2025

With the significant advances in generative AI (GAI) and the proliferation of mobile devices, providing high-quality AI-generated content (AIGC) services via wireless networks is becoming the future direction. However, the primary challenges of AIGC service delivery in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed computational resources. In this paper, we employ semantic communication (SemCom) in diffusion-based GAI models to propose a Resource-aware wOrkload-adjUstable TransceivEr (ROUTE) for AIGC delivery in dynamic wireless networks. Specifically, to relieve the communication resource bottleneck, SemCom is utilized to prioritize semantic information of the generated content. Then, to improve computational resource utilization in both edge and local and reduce AIGC semantic distortion in transmission, modified diffusion-based models are applied to adjust the computing workload and semantic density in cooperative content generation. Simulations verify the superiority of our proposed ROUTE in terms of latency and content quality compared to conventional AIGC approaches.
Online 3D Scene Reconstruction Using Neural Object Priors
Venue: arXiv cs.CV 2025
Year: 2025

This paper addresses the problem of reconstructing a scene online at the level of objects given an RGB-D video sequence. While current object-aware neural implicit representations hold promise, they are limited in online reconstruction efficiency and shape completion. Our main contributions to alleviate the above limitations are twofold. First, we propose a feature grid interpolation mechanism to continuously update grid-based object-centric neural implicit representations as new object parts are revealed. Second, we construct an object library with previously mapped objects in advance and leverage the corresponding shape priors to initialize geometric object models in new videos, subsequently completing them with novel views as well as synthesized past views to avoid losing original object details. Extensive experiments on synthetic environments from the Replica dataset, real-world ScanNet sequences and videos captured in our laboratory demonstrate that our approach outperforms state-of-the-art neural implicit models for this task in terms of reconstruction accuracy and completeness.
Autonomous Generation of Sub-goals for Lifelong Learning in Robots
Venue: arXiv cs.RO 2025
Year: 2025

One of the challenges of open-ended learning in robots is the need to autonomously discover goals and learn skills to achieve them. However, when in lifelong learning settings, it is always desirable to generate sub-goals with their associated skills, without relying on explicit reward, as steppingstones to a goal. This allows sub-goals and skills to be reused to facilitate achieving other goals. This work proposes a two-pronged approach for sub-goal generation to address this challenge: a top-down approach, where sub-goals are hierarchically derived from general goals using intrinsic motivations to discover them, and a bottom-up approach, where sub-goal chains emerge from making latent relationships between goals and perceptual classes that were previously learned in different domains explicit. These methods help the robot to autonomously generate and chain sub-goals as a way to achieve more general goals. Additionally, they create more abstract representations of goals, helping to reduce sub-goal duplication and make the learning of skills more efficient. Implemented within an existing cognitive architecture for lifelong open-ended learning and tested with a real robot, our approach enhances the robot's ability to discover and achieve goals, generate sub-goals in an efficient manner, generalize learned skills, and operate in dynamic and unknown environments without explicit intermediate rewards.
Training-free Diffusion Acceleration with Bottleneck Sampling
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models have demonstrated remarkable capabilities in visual content generation but remain challenging to deploy due to their high computational cost during inference. This computational burden primarily arises from the quadratic complexity of self-attention with respect to image or video resolution. While existing acceleration methods often compromise output quality or necessitate costly retraining, we observe that most diffusion models are pre-trained at lower resolutions, presenting an opportunity to exploit these low-resolution priors for more efficient inference without degrading performance. In this work, we introduce Bottleneck Sampling, a training-free framework that leverages low-resolution priors to reduce computational overhead while preserving output fidelity. Bottleneck Sampling follows a high-low-high denoising workflow: it performs high-resolution denoising in the initial and final stages while operating at lower resolutions in intermediate steps. To mitigate aliasing and blurring artifacts, we further refine the resolution transition points and adaptively shift the denoising timesteps at each stage. We evaluate Bottleneck Sampling on both image and video generation tasks, where extensive experiments demonstrate that it accelerates inference by up to 3$\times$ for image generation and 2.5$\times$ for video generation, all while maintaining output quality comparable to the standard full-resolution sampling process across multiple evaluation metrics.
SlowFast-LLaVA-1.5: A Family of Token-Efficient Video Large Language Models for Long-Form Video Understanding
Venue: arXiv cs.CV 2025
Year: 2025

We introduce SlowFast-LLaVA-1.5 (abbreviated as SF-LLaVA-1.5), a family of video large language models (LLMs) offering a token-efficient solution for long-form video understanding. We incorporate the two-stream SlowFast mechanism into a streamlined training pipeline, and perform joint video-image training on a carefully curated data mixture of only publicly available datasets. Our primary focus is on highly efficient model scales (1B and 3B), demonstrating that even relatively small Video LLMs can achieve state-of-the-art performance on video understanding, meeting the demand for mobile-friendly models. Experimental results demonstrate that SF-LLaVA-1.5 achieves superior performance on a wide range of video and image tasks, with robust results at all model sizes (ranging from 1B to 7B). Notably, SF-LLaVA-1.5 achieves state-of-the-art results in long-form video understanding (e.g., LongVideoBench and MLVU) and excels at small scales across various video benchmarks.
HH4AI: A methodological Framework for AI Human Rights impact assessment under the EUAI ACT
Venue: arXiv cs.CY 2025
Year: 2025

This paper introduces the HH4AI Methodology, a structured approach to assessing the impact of AI systems on human rights, focusing on compliance with the EU AI Act and addressing technical, ethical, and regulatory challenges. The paper highlights AIs transformative nature, driven by autonomy, data, and goal-oriented design, and how the EU AI Act promotes transparency, accountability, and safety. A key challenge is defining and assessing "high-risk" AI systems across industries, complicated by the lack of universally accepted standards and AIs rapid evolution. To address these challenges, the paper explores the relevance of ISO/IEC and IEEE standards, focusing on risk management, data quality, bias mitigation, and governance. It proposes a Fundamental Rights Impact Assessment (FRIA) methodology, a gate-based framework designed to isolate and assess risks through phases including an AI system overview, a human rights checklist, an impact assessment, and a final output phase. A filtering mechanism tailors the assessment to the system's characteristics, targeting areas like accountability, AI literacy, data governance, and transparency. The paper illustrates the FRIA methodology through a fictional case study of an automated healthcare triage service. The structured approach enables systematic filtering, comprehensive risk assessment, and mitigation planning, effectively prioritizing critical risks and providing clear remediation strategies. This promotes better alignment with human rights principles and enhances regulatory compliance.
strideSEA: A STRIDE-centric Security Evaluation Approach
Venue: arXiv cs.CR 2025
Year: 2025

Microsoft's STRIDE methodology is at the forefront of threat modeling, supporting the increasingly critical quality attribute of security in software-intensive systems. However, in a comprehensive security evaluation process, the general consensus is that the STRIDE classification is only useful for threat elicitation, isolating threat modeling from the other security evaluation activities involved in a secure software development life cycle (SDLC). We present strideSEA, a STRIDE-centric Security Evaluation Approach that integrates STRIDE as the central classification scheme into the security activities of threat modeling, attack scenario analysis, risk analysis, and countermeasure recommendation that are conducted alongside software engineering activities in secure SDLCs. The application of strideSEA is demonstrated in a real-world online immunization system case study. Using STRIDE as a single unifying thread, we bind existing security evaluation approaches in the four security activities of strideSEA to analyze (1) threats using Microsoft threat modeling tool, (2) attack scenarios using attack trees, (3) systemic risk using NASA's defect detection and prevention (DDP) technique, and (4) recommend countermeasures based on their effectiveness in reducing the most critical risks using DDP. The results include a detailed quantitative assessment of the security of the online immunization system with a clear definition of the role and advantages of integrating STRIDE in the evaluation process. Overall, the unified approach in strideSEA enables a more structured security evaluation process, allowing easier identification and recommendation of countermeasures, thus supporting the security requirements and eliciting design considerations, informing the software development life cycle of future software-based information systems.
When Distances Lie: Euclidean Embeddings in the Presence of Outliers and Distance Violations
Venue: arXiv cs.CG 2025
Year: 2025

Distance geometry explores the properties of distance spaces that can be exactly represented as the pairwise Euclidean distances between points in $\mathbb{R}^d$ ($d \geq 1$), or equivalently, distance spaces that can be isometrically embedded in $\mathbb{R}^d$. In this work, we investigate whether a distance space can be isometrically embedded in $\mathbb{R}^d$ after applying a limited number of modifications. Specifically, we focus on two types of modifications: outlier deletion (removing points) and distance modification (adjusting distances between points). The central problem, Euclidean Embedding Editing (EEE), asks whether an input distance space on $n$ points can be transformed, using at most $k$ modifications, into a space that is isometrically embeddable in $\mathbb{R}^d$. We present several fixed-parameter tractable (FPT) and approximation algorithms for this problem. Our first result is an algorithm that solves EEE in time $(dk)^{\mathcal{O}(d+k)} + n^{\mathcal{O}(1)}$. The core subroutine of this algorithm, which is of independent interest, is a polynomial-time method for compressing the input distance space into an equivalent instance of EEE with $\mathcal{O}((dk)^2)$ points. For the special but important case of EEE where only outlier deletions are allowed, we improve the parameter dependence of the FPT algorithm and obtain a running time of $\min\{(d+3)^k, 2^{d+k}\} \cdot n^{\mathcal{O}(1)}$. Additionally, we provide an FPT-approximation algorithm for this problem, which outputs a set of at most $2 \cdot {\rm OPT}$ outliers in time $2^d \cdot n^{\mathcal{O}(1)}$. This 2-approximation algorithm improves upon the previous $(3+\varepsilon)$-approximation algorithm by Sidiropoulos, Wang, and Wang [SODA '17]. Furthermore, we complement our algorithms with hardness results motivating our choice of parameterizations.
Uncertainty-Aware Decomposed Hybrid Networks
Venue: arXiv cs.CV 2025
Year: 2025

The robustness of image recognition algorithms remains a critical challenge, as current models often depend on large quantities of labeled data. In this paper, we propose a hybrid approach that combines the adaptability of neural networks with the interpretability, transparency, and robustness of domain-specific quasi-invariant operators. Our method decomposes the recognition into multiple task-specific operators that focus on different characteristics, supported by a novel confidence measurement tailored to these operators. This measurement enables the network to prioritize reliable features and accounts for noise. We argue that our design enhances transparency and robustness, leading to improved performance, particularly in low-data regimes. Experimental results in traffic sign detection highlight the effectiveness of the proposed method, especially in semi-supervised and unsupervised scenarios, underscoring its potential for data-constrained applications.
Masks and Mimicry: Strategic Obfuscation and Impersonation Attacks on Authorship Verification
Venue: arXiv cs.CL 2025
Year: 2025

The increasing use of Artificial Intelligence (AI) technologies, such as Large Language Models (LLMs) has led to nontrivial improvements in various tasks, including accurate authorship identification of documents. However, while LLMs improve such defense techniques, they also simultaneously provide a vehicle for malicious actors to launch new attack vectors. To combat this security risk, we evaluate the adversarial robustness of authorship models (specifically an authorship verification model) to potent LLM-based attacks. These attacks include untargeted methods - \textit{authorship obfuscation} and targeted methods - \textit{authorship impersonation}. For both attacks, the objective is to mask or mimic the writing style of an author while preserving the original texts' semantics, respectively. Thus, we perturb an accurate authorship verification model, and achieve maximum attack success rates of 92\% and 78\% for both obfuscation and impersonation attacks, respectively.
Angular-Based Hybrid Beamforming for Wideband THz Massive MIMO Systems: Mitigating Beam Split by Leveraging Angular Spread
Venue: arXiv cs.IT 2025
Year: 2025

Beam split is a critical challenge in wideband THz massive MIMO systems, arising from frequency-dependent beam misalignment that degrades communication performance, particularly in scenarios with narrow beamwidths and large arrays. This work proposes an angular-based hybrid beamforming framework that leverages angular spread to mitigate the beam split effect. Instead of relying on precise angular spread modeling, we utilize coarse angular information to guide the design of subcarrier-specific beams, effectively reducing misalignment across subcarriers. By broadening the effective beamwidth through angular spread, the proposed method enhances user coverage and alleviates beam split without requiring complex time-delay units or hardware-intensive solutions. Simulation results demonstrate that the proposed approach achieves significant improvements in spectral efficiency and beamforming accuracy while maintaining low computational and hardware complexity. This work provides a practical and efficient solution for addressing beam split in next-generation wideband THz communication systems.
Dom, cars don't fly! -- Or do they? In-Air Vehicle Maneuver for High-Speed Off-Road Navigation
Venue: arXiv cs.RO 2025
Year: 2025

When pushing the speed limit for aggressive off-road navigation on uneven terrain, it is inevitable that vehicles may become airborne from time to time. During time-sensitive tasks, being able to fly over challenging terrain can also save time, instead of cautiously circumventing or slowly negotiating through. However, most off-road autonomy systems operate under the assumption that the vehicles are always on the ground and therefore limit operational speed. In this paper, we present a novel approach for in-air vehicle maneuver during high-speed off-road navigation. Based on a hybrid forward kinodynamic model using both physics principles and machine learning, our fixed-horizon, sampling-based motion planner ensures accurate vehicle landing poses and their derivatives within a short airborne time window using vehicle throttle and steering commands. We test our approach in extensive in-air experiments both indoors and outdoors, compare it against an error-driven control method, and demonstrate that precise and timely in-air vehicle maneuver is possible through existing ground vehicle controls.