OpenRaiser/Intern-Atlas huggingface dataset:
Find First, Track Next: Decoupling Identification and Propagation in Referring Video Object Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Referring video object segmentation aims to segment and track a target object in a video using a natural language prompt. Existing methods typically fuse visual and textual features in a highly entangled manner, processing multi-modal information together to generate per-frame masks. However, this approach often struggles with ambiguous target identification, particularly in scenes with multiple similar objects, and fails to ensure consistent mask propagation across frames. To address these limitations, we introduce FindTrack, an efficient decoupled framework that separates target identification from mask propagation. FindTrack first adaptively selects a key frame by balancing segmentation confidence and vision-text alignment, establishing a robust reference for the target object. This reference is then utilized by a dedicated propagation module to track and segment the object across the entire video. By decoupling these processes, FindTrack effectively reduces ambiguities in target association and enhances segmentation consistency. FindTrack significantly outperforms all existing methods on public benchmarks, demonstrating its superiority.
Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositories
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) have shown promise in software vulnerability detection, particularly on function-level benchmarks like Devign and BigVul. However, real-world detection requires interprocedural analysis, as vulnerabilities often emerge through multi-hop function calls rather than isolated functions. While repository-level benchmarks like ReposVul and VulEval introduce interprocedural context, they remain computationally expensive, lack pairwise evaluation of vulnerability fixes, and explore limited context retrieval, limiting their practicality. We introduce JitVul, a JIT vulnerability detection benchmark linking each function to its vulnerability-introducing and fixing commits. Built from 879 CVEs spanning 91 vulnerability types, JitVul enables comprehensive evaluation of detection capabilities. Our results show that ReAct Agents, leveraging thought-action-observation and interprocedural context, perform better than LLMs in distinguishing vulnerable from benign code. While prompting strategies like Chain-of-Thought help LLMs, ReAct Agents require further refinement. Both methods show inconsistencies, either misidentifying vulnerabilities or over-analyzing security guards, indicating significant room for improvement.
Measuring a moving target -- Innovation studies in practice
Venue: arXiv cs.DL 2025
Year: 2025

This paper pays a tribute to Loet's work in a specific way. More than 20 years ago Loet Leydesdorff and myself designed a programme for future innovation studies 'Measuring the knowledge base - a programme of innovation studies'. Although, the funding programme we envisioned eventually did not materialise, the proposal text set out the main lines of our research collaboration over the coming decades. This paper revisits main statements of this programme and discusses their remaining validity in the light of more recent research. Core to the Leydesdorff and Scharnhorst text was a system-theoretical, evolutionary perspective on science dynamics, newly emerging structures and phenomena and addressed the question to which extent they could be meaningful studied using quantitative approaches. This paper looks into three cases - all examples of newly emerging institutional structures and related practices in science. They are all located at the interface between research and research infrastructures. While discussing how the programmatic ideas written up at the beginning of the 2000s still informs measurement attempts in those three cases, the paper also touches upon questions of epistemological foundations of quantitative studies in general. The main conclusion is that combining measurement experiments with philosophical reflection remains important.
Design and Implementation of an IoT Cluster with Raspberry Pi Powered by Solar Energy: A Theoretical Approach
Venue: arXiv cs.DC 2025
Year: 2025

This document presents the design and implementation of a low-power IoT server cluster, based on Raspberry Pi 3 Model B and powered by solar energy. The proposed architecture integrates Kubernetes (K3s) and Docker, providing an efficient, scalable, and high-performance computing environment. The cluster is designed to optimize energy consumption, leveraging a 200W solar panel system and a 100Ah lithium-ion battery to support continuous operation under favorable environmental conditions. Performance analysis was conducted based on theoretical inferences and data obtained from external sources, evaluating resource allocation, power consumption, and service availability. These analyses provide theoretical estimates of the system's operational feasibility under different scenarios. The results suggest that this system can serve as a viable and sustainable alternative for edge computing applications and cloud services, reducing dependence on traditional data centers. In addition to its positive impact on environmental sustainability by significantly reducing the carbon footprint, this solution also addresses economic concerns, as conventional data centers consume enormous amounts of energy, leading to increased demand on the power grid and higher operational costs.
DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms
Venue: arXiv cs.CV 2025
Year: 2025

Dongba pictographic is the only pictographic script still in use in the world. Its pictorial ideographic features carry rich cultural and contextual information. However, due to the lack of relevant datasets, research on semantic understanding of Dongba hieroglyphs has progressed slowly. To this end, we constructed \textbf{DongbaMIE} - the first dataset focusing on multimodal information extraction of Dongba pictographs. The dataset consists of images of Dongba hieroglyphic characters and their corresponding semantic annotations in Chinese. It contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs. The annotations cover four semantic dimensions: object, action, relation and attribute. Systematic evaluation of mainstream multimodal large language models shows that the models are difficult to perform information extraction of Dongba hieroglyphs efficiently under zero-shot and few-shot learning. Although supervised fine-tuning can improve the performance, accurate extraction of complex semantics is still a great challenge at present.
Efektywne energetycznie wielodostępowe przetwarzanie brzegowe w sieci 5G; Energy efficient Multi-access Edge Computing in 5G network
Venue: arXiv cs.NI 2025
Year: 2025

Multi-access edge computing is a technique that combines the use of communication networks and remote computing resources. It allows to perform complex computational tasks for devices with low computing power while maintaining low latencies. However, it is important to effectively allocate the computing tasks to individual nodes. The work will present how the multi-access edge computing system can be integrated into the 5G network, as well as how resources can be distributed between individual nodes to minimize energy consumption. Some new degrees of freedom will be presented, which enable a significant reduction in energy consumption compared to existing solutions for independent optimization of the computation and communication parts. -- Wielodostępowe przetwarzanie brzegowe jest techniką łączącą wykorzystanie sieci komunikacyjnych i oddalonych zasobów obliczeniowych. Pozwala wykonać złożone zadania obliczeniowe na potrzeby urządzeń o niewielkiej mocy obliczeniowej przy zachowaniu niewielkich opóźnień. Istotne jest jednak efektywne zarządzanie przydziałem zadań obliczeniowych do poszczególnych węzłów. W pracy przedstawiono jak system przetwarzania brzegowego może być zintegrowany z siecią 5G, a także jak można rozdzielić zasoby między poszczególne węzły, żeby zminimalizować zużycie energii. Przedstawiony zostanie szereg nowych stopni swobody, które umożliwiają znaczne obniżenie zużycia energii w stosunku do istniejących rozwiązań niezależnej optymalizacji części obliczeniowej i komunikacyjnej.
Chunking the Critic: A Transformer-based Soft Actor-Critic with N-Step Returns
Venue: arXiv cs.LG 2025
Year: 2025

We introduce a sequence-conditioned critic for Soft Actor--Critic (SAC) that models trajectory context with a lightweight Transformer and trains on aggregated $N$-step targets. Unlike prior approaches that (i) score state--action pairs in isolation or (ii) rely on actor-side action chunking to handle long horizons, our method strengthens the critic itself by conditioning on short trajectory segments and integrating multi-step returns -- without importance sampling (IS). The resulting sequence-aware value estimates capture the critical temporal structure for extended-horizon and sparse-reward problems. On local-motion benchmarks, we further show that freezing critic parameters for several steps makes our update compatible with CrossQ's core idea, enabling stable training \emph{without} a target network. Despite its simplicity -- a 2-layer Transformer with 128-256 hidden units and a maximum update-to-data ratio (UTD) of $1$ -- the approach consistently outperforms standard SAC and strong off-policy baselines, with particularly large gains on long-trajectory control. These results highlight the value of sequence modeling and $N$-step bootstrapping on the critic side for long-horizon reinforcement learning.
Towards Trustworthy Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

This paper develops a comprehensive framework to address three critical trustworthy challenges in federated learning (FL): robustness against Byzantine attacks, fairness, and privacy preservation. To improve the system's defense against Byzantine attacks that send malicious information to bias the system's performance, we develop a Two-sided Norm Based Screening (TNBS) mechanism, which allows the central server to crop the gradients that have the l lowest norms and h highest norms. TNBS functions as a screening tool to filter out potential malicious participants whose gradients are far from the honest ones. To promote egalitarian fairness, we adopt the q-fair federated learning (q-FFL). Furthermore, we adopt a differential privacy-based scheme to prevent raw data at local clients from being inferred by curious parties. Convergence guarantees are provided for the proposed framework under different scenarios. Experimental results on real datasets demonstrate that the proposed framework effectively improves robustness and fairness while managing the trade-off between privacy and accuracy. This work appears to be the first study that experimentally and theoretically addresses fairness, privacy, and robustness in trustworthy FL.
Towards Understanding Distilled Reasoning Models: A Representational Approach
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we investigate how model distillation impacts the development of reasoning features in large language models (LLMs). To explore this, we train a crosscoder on Qwen-series models and their fine-tuned variants. Our results suggest that the crosscoder learns features corresponding to various types of reasoning, including self-reflection and computation verification. Moreover, we observe that distilled models contain unique reasoning feature directions, which could be used to steer the model into over-thinking or incisive-thinking mode. In particular, we perform analysis on four specific reasoning categories: (a) self-reflection, (b) deductive reasoning, (c) alternative reasoning, and (d) contrastive reasoning. Finally, we examine the changes in feature geometry resulting from the distillation process and find indications that larger distilled models may develop more structured representations, which correlate with enhanced distillation performance. By providing insights into how distillation modifies the model, our study contributes to enhancing the transparency and reliability of AI systems.
Rethinking Deep Clustering Paradigms: Self-Supervision Is All You Need
Venue: arXiv cs.CV 2025
Year: 2025

The recent advances in deep clustering have been made possible by significant progress in self-supervised and pseudo-supervised learning. However, the trade-off between self-supervision and pseudo-supervision can give rise to three primary issues. The joint training causes Feature Randomness and Feature Drift, whereas the independent training causes Feature Randomness and Feature Twist. In essence, using pseudo-labels generates random and unreliable features. The combination of pseudo-supervision and self-supervision drifts the reliable clustering-oriented features. Moreover, moving from self-supervision to pseudo-supervision can twist the curved latent manifolds. This paper addresses the limitations of existing deep clustering paradigms concerning Feature Randomness, Feature Drift, and Feature Twist. We propose a new paradigm with a new strategy that replaces pseudo-supervision with a second round of self-supervision training. The new strategy makes the transition between instance-level self-supervision and neighborhood-level self-supervision smoother and less abrupt. Moreover, it prevents the drifting effect that is caused by the strong competition between instance-level self-supervision and clustering-level pseudo-supervision. Moreover, the absence of the pseudo-supervision prevents the risk of generating random features. With this novel approach, our paper introduces a Rethinking of the Deep Clustering Paradigms, denoted by R-DC. Our model is specifically designed to address three primary challenges encountered in Deep Clustering: Feature Randomness, Feature Drift, and Feature Twist. Experimental results conducted on six datasets have shown that the two-level self-supervision training yields substantial improvements.
Constrained Gaussian Wasserstein Optimal Transport with Commutative Covariance Matrices
Venue: arXiv cs.IT 2025
Year: 2025

Optimal transport has found widespread applications in signal processing and machine learning. Among its many equivalent formulations, optimal transport seeks to reconstruct a random variable/vector with a prescribed distribution at the destination while minimizing the expected distortion relative to a given random variable/vector at the source. However, in practice, certain constraints may render the optimal transport plan infeasible. In this work, we consider three types of constraints: rate constraints, dimension constraints, and channel constraints, motivated by perception-aware lossy compression, generative principal component analysis, and deep joint source-channel coding, respectively. Special attenion is given to the setting termed Gaussian Wasserstein optimal transport, where both the source and reconstruction variables are multivariate Gaussian, and the end-to-end distortion is measured by the mean squared error. We derive explicit results for the minimum achievable mean squared error under the three aforementioned constraints when the covariance matrices of the source and reconstruction variables commute.
On Phi-entropic Dependence Measures and Non-local Correlations
Venue: arXiv cs.IT 2025
Year: 2025

We say that a measure of dependence between two random variables $X$ and $Y$, denoted as $ρ(X;Y)$, satisfies the data processing property if $ρ(X;Y)\geq ρ(X';Y')$ for every $X'\rightarrow X\rightarrow Y\rightarrow Y'$, and satisfies the tensorization property if $ρ(X_1X_2;Y_1Y_2)=\max\{ρ(X_1;Y_1),ρ(X_2;Y_2)\}$ when $(X_1,Y_1)$ is independent of $(X_2,Y_2)$. It is known that measures of dependence defined based on $Φ$-entropy satisfy these properties. These measures are important because they generalize R{é}nyi's maximal correlation and the hypercontractivity ribbon. The data processing and tensorization properties are special cases of monotonicity under wirings of non-local boxes. We show that ribbons defined using $Φ$-entropic measures of dependence are monotone under wiring of non-local no-signaling boxes, generalizing an earlier result. In addition, we also discuss the evaluation of $Φ$-strong data processing inequality constant for joint distributions obtained from a $Z$-channel.
A Survey on Semantic Communications in Internet of Vehicles
Venue: arXiv cs.NI 2025
Year: 2025

Internet of Vehicles (IoV), as the core of intelligent transportation system, enables comprehensive interconnection between vehicles and their surroundings through multiple communication modes, which is significant for autonomous driving and intelligent traffic management. However, with the emergence of new applications, traditional communication technologies face the problems of scarce spectrum resources and high latency. Semantic communication, which focuses on extracting, transmitting, and recovering some useful semantic information from messages, can reduce redundant data transmission, improve spectrum utilization, and provide innovative solutions to communication challenges in the IoV. This paper systematically reviews state of art of semantic communications in the IoV, elaborates the technical background of IoV and semantic communications, and deeply discusses key technologies of semantic communications in IoV, including semantic information extraction, semantic communication architecture, resource allocation and management, and so on. Through specific case studies, it demonstrates that semantic communications can be effectively employed in the scenarios of traffic environment perception and understanding, intelligent driving decision support, IoV service optimization, and intelligent traffic management. Additionally, it analyzes the current challenges and future research directions. This survey reveals that semantic communications has broad application prospects in IoV, but it is necessary to solve the real existing problems by combining advanced technologies to promote its wide application in IoV and contributing to the development of intelligent transportation system.
Synthetic Data Augmentation for Enhancing Harmful Algal Bloom Detection with Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

Harmful Algal Blooms (HABs) pose severe threats to aquatic ecosystems and public health, resulting in substantial economic losses globally. Early detection is crucial but often hindered by the scarcity of high-quality datasets necessary for training reliable machine learning (ML) models. This study investigates the use of synthetic data augmentation using Gaussian Copulas to enhance ML-based HAB detection systems. Synthetic datasets of varying sizes (100-1,000 samples) were generated using relevant environmental features$\unicode{x2015}$water temperature, salinity, and UVB radiation$\unicode{x2015}$with corrected Chlorophyll-a concentration as the target variable. Experimental results demonstrate that moderate synthetic augmentation significantly improves model performance (RMSE reduced from 0.4706 to 0.1850; $p < 0.001$). However, excessive synthetic data introduces noise and reduces predictive accuracy, emphasizing the need for a balanced approach to data augmentation. These findings highlight the potential of synthetic data to enhance HAB monitoring systems, offering a scalable and cost-effective method for early detection and mitigation of ecological and public health risks.
DeepGrav: Anomalous Gravitational-Wave Detection Through Deep Latent Features
Venue: arXiv cs.LG 2025
Year: 2025

This work introduces a novel deep learning-based approach for gravitational wave anomaly detection, aiming to overcome the limitations of traditional matched filtering techniques in identifying unknown waveform gravitational wave signals. We introduce a modified convolutional neural network architecture inspired by ResNet that leverages residual blocks to extract high-dimensional features, effectively capturing subtle differences between background noise and gravitational wave signals. This network architecture learns a high-dimensional projection while preserving discrepancies with the original input, facilitating precise identification of gravitational wave signals. In our experiments, we implement an innovative data augmentation strategy that generates new data by computing the arithmetic mean of multiple signal samples while retaining the key features of the original signals. In the NSF HDR A3D3: Detecting Anomalous Gravitational Wave Signals competition, it is honorable for us (group name: easonyan123) to get to the first place at the end with our model achieving a true negative rate (TNR) of 0.9708 during development/validation phase and 0.9832 on an unseen challenge dataset during final/testing phase, the highest among all competitors. These results demonstrate that our method not only achieves excellent generalization performance but also maintains robust adaptability in addressing the complex uncertainties inherent in gravitational wave anomaly detection.
Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence
Venue: arXiv cs.MA 2025
Year: 2025

This work examines the integration of large language models (LLMs) into multi-agent simulations by replacing the hard-coded programs of agents with LLM-driven prompts. The proposed approach is showcased in the context of two examples of complex systems from the field of swarm intelligence: ant colony foraging and bird flocking. Central to this study is a toolchain that integrates LLMs with the NetLogo simulation platform, leveraging its Python extension to enable communication with GPT-4o via the OpenAI API. This toolchain facilitates prompt-driven behavior generation, allowing agents to respond adaptively to environmental data. For both example applications mentioned above, we employ both structured, rule-based prompts and autonomous, knowledge-driven prompts. Our work demonstrates how this toolchain enables LLMs to study self-organizing processes and induce emergent behaviors within multi-agent environments, paving the way for new approaches to exploring intelligent systems and modeling swarm intelligence inspired by natural phenomena. We provide the code, including simulation files and data at https://github.com/crjimene/swarm_gpt.
Vision-Language Models Struggle to Align Entities across Modalities
Venue: arXiv cs.CL 2025
Year: 2025

Cross-modal entity linking refers to the ability to align entities and their attributes across different modalities. While cross-modal entity linking is a fundamental skill needed for real-world applications such as multimodal code generation, fake news detection, or scene understanding, it has not been thoroughly studied in the literature. In this paper, we introduce a new task and benchmark to address this gap. Our benchmark, MATE, consists of 5.5k evaluation instances featuring visual scenes aligned with their textual representations. To evaluate cross-modal entity linking performance, we design a question-answering task that involves retrieving one attribute of an object in one modality based on a unique attribute of that object in another modality. We evaluate state-of-the-art Vision-Language Models (VLMs) and humans on this task, and find that VLMs struggle significantly compared to humans, particularly as the number of objects in the scene increases. Our analysis also shows that, while chain-of-thought prompting can improve VLM performance, models remain far from achieving human-level proficiency. These findings highlight the need for further research in cross-modal entity linking and show that MATE is a strong benchmark to support that progress.