OpenRaiser/Intern-Atlas huggingface dataset:
Accuracy and Political Bias of News Source Credibility Ratings by Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Search engines increasingly leverage large language models (LLMs) to generate direct answers, and AI chatbots now access the Internet for fresh data. As information curators for billions of users, LLMs must assess the accuracy and reliability of different sources. This paper audits nine widely used LLMs from three leading providers -- OpenAI, Google, and Meta -- to evaluate their ability to discern credible and high-quality information sources from low-credibility ones. We find that while LLMs can rate most tested news outlets, larger models more frequently refuse to provide ratings due to insufficient information, whereas smaller models are more prone to making errors in their ratings. For sources where ratings are provided, LLMs exhibit a high level of agreement among themselves (average Spearman's $ρ= 0.79$), but their ratings align only moderately with human expert evaluations (average $ρ= 0.50$). Analyzing news sources with different political leanings in the US, we observe a liberal bias in credibility ratings yielded by all LLMs in default configurations. Additionally, assigning partisan roles to LLMs consistently induces strong politically congruent bias in their ratings. These findings have important implications for the use of LLMs in curating news and political information.
Towards Goal-Oriented Semantic Communications: New Metrics, Framework, and Open Challenges
Venue: arXiv cs.IT 2025
Year: 2025

Since Shannon's pioneering masterpiece which established the prototype of modern information theory, the goal of communications has long been promising accurate message reconstruction at a refined bit-by-bit level, which deliberately neglects the semantics/effectiveness aspect of information. Nevertheless, the recent development of wireless technologies and the spurt of deep learning (DL) techniques allow us to reclaim the meaning/usefulness aspect in the design of future 6G communication paradigms, where goal-oriented communication is becoming a trend. Age of Information (AoI), a well-known metric that captures the importance of information by recording the time elapsed from the generation time slot, has been extended to various variants, such as Value of Information (VoI), Urgency of Information (UoI), Age of Incorrect Information (AoII), and etc. While each of them proposes novel ways to measure the meaning/usefulness aspect of information, there is not yet an integrated framework encompassing all of them. To this end, we propose a novel tensor-based approach, the Goal-oriented Tensor (GoT), to unify them, which also allows more flexible and fine-grained goal characterizations. Following the proposed GoT, we architect a holistic goal-oriented framework to enable goal-oriented semantic communications, in which information perception, dissemination, and control-plane decisions are implemented in accordance with specific goals. Finally, we outline several open challenges to fulfill the vision of the GoT framework.
A Machine Learning Approach to Forecasting Honey Production with Tree-Based Methods
Venue: arXiv cs.LG 2025
Year: 2025

The beekeeping sector has experienced significant production fluctuations in recent years, largely due to increasingly frequent adverse weather events linked to climate change. These events can severely affect the environment, reducing its suitability for bee activity. We conduct a forecasting analysis of honey production across Italy using a range of machine learning models, with a particular focus on weather-related variables as key predictors. Our analysis relies on a dataset collected in 2022, which combines hive-level observations with detailed weather data. We train and compare several linear and nonlinear models, evaluating both their predictive accuracy and interpretability. By examining model explanations, we identify the main drivers of honey production. We also ensemble models from different families to assess whether combining predictions improves forecast accuracy. These insights support beekeepers in managing production risks and may inform the development of insurance products against unexpected losses due to poor harvests.
Generating Continual Human Motion in Diverse 3D Scenes
Venue: arXiv cs.CV 2025
Year: 2025

We introduce a method to synthesize animator guided human motion across 3D scenes. Given a set of sparse (3 or 4) joint locations (such as the location of a person's hand and two feet) and a seed motion sequence in a 3D scene, our method generates a plausible motion sequence starting from the seed motion while satisfying the constraints imposed by the provided keypoints. We decompose the continual motion synthesis problem into walking along paths and transitioning in and out of the actions specified by the keypoints, which enables long generation of motions that satisfy scene constraints without explicitly incorporating scene information. Our method is trained only using scene agnostic mocap data. As a result, our approach is deployable across 3D scenes with various geometries. For achieving plausible continual motion synthesis without drift, our key contribution is to generate motion in a goal-centric canonical coordinate frame where the next immediate target is situated at the origin. Our model can generate long sequences of diverse actions such as grabbing, sitting and leaning chained together in arbitrary order, demonstrated on scenes of varying geometry: HPS, Replica, Matterport, ScanNet and scenes represented using NeRFs. Several experiments demonstrate that our method outperforms existing methods that navigate paths in 3D scenes. For more results we urge the reader to watch our supplementary video available at: https://www.youtube.com/watch?v=0wZgsdyCT4A&t=1s
Determinantal Sieving
Venue: arXiv cs.DS 2025
Year: 2025

We introduce determinantal sieving, a new, remarkably powerful tool in the toolbox of algebraic FPT algorithms. Given a polynomial $P(X)$ on a set of variables $X=\{x_1,\ldots,x_n\}$ and a linear matroid $M=(X,\mathcal{I})$ of rank $k$, both over a field $\mathbb{F}$ of characteristic 2, in $2^k$ evaluations we can sieve for those terms in the monomial expansion of $P$ which are multilinear and whose support is a basis for $M$. Alternatively, using $2^k$ evaluations of $P$ we can sieve for those monomials whose odd support spans $M$. Applying this framework, we improve on a range of algebraic FPT algorithms, such as: 1. Solving $q$-Matroid Intersection in time $O^*(2^{(q-2)k})$ and $q$-Matroid Parity in time $O^*(2^{qk})$, improving on $O^*(4^{qk})$ over general fields (Brand and Pratt, ICALP 2021) 2. Long $(s,t)$-Path in $O^*(1.66^k)$ time, improving on $O^*(2^k)$, and Rank $k$ $(S,T)$-Linkage in so-called frameworks in $O^*(2^k)$ time, improving on $O^*(2^{|S|+O(k^2 \log(k+|\mathbb{F}|))})$ over general fields (Fomin et al., SODA 2023). 3. Many instances of the Diverse X paradigm, finding a collection of $r$ solutions to a problem with a minimum mutual distance of $d$ in time $O^*(2^{r(r-1)d/2})$, improving solutions for $k$-Distinct Branchings from time $2^{O(k \log k)}$ to $O^*(2^k)$ (Bang-Jensen et al., ESA 2021), and for Diverse Perfect Matchings from $O^*(2^{2^{O(rd)}})$ to $O^*(2^{r^2d/2})$ (Fomin et al., STACS 2021). Here, all matroids are assumed to be represented over fields of characteristic 2. Over general fields, we achieve similar results at the cost of using exponential space by working over the exterior algebra. For a class of arithmetic circuits we call strongly monotone, this is even achieved without any loss of running time. However, the odd support sieving result appears to be specific to working over characteristic 2.
SCB-Dataset: A Dataset for Detecting Student and Teacher Classroom Behavior
Venue: arXiv cs.CV 2025
Year: 2025

Using deep learning methods to detect the classroom behaviors of both students and teachers is an effective way to automatically analyze classroom performance and enhance teaching effectiveness. Then, there is still a scarcity of publicly available high-quality datasets on student-teacher behaviors. We constructed SCB-Dataset a comprehensive dataset of student and teacher classroom behaviors covering 19 classes. SCB-Dataset is divided into two types: Object Detection and Image Classification. The Object Detection part includes 13,330 images and 122,977 labels, and the Image Classification part includes 21,019 images. We conducted benchmark tests on SCB-Dataset using YOLO series algorithms and Large vision-language model. We believe that SCB-Dataset can provide a solid foundation for future applications of artificial intelligence in education. Code:https://github.com/Whiffe/SCB-dataset
Representer Theorems for Metric and Preference Learning: Geometric Insights and Algorithms
Venue: arXiv cs.LG 2025
Year: 2025

We develop a mathematical framework to address a broad class of metric and preference learning problems within a Hilbert space. We obtain a novel representer theorem for the simultaneous task of metric and preference learning. Our key observation is that the representer theorem for this task can be derived by regularizing the problem with respect to the norm inherent in the task structure. For the general task of metric learning, our framework leads to a simple and self-contained representer theorem and offers new geometric insights into the derivation of representer theorems for this task. In the case of Reproducing Kernel Hilbert Spaces (RKHSs), we illustrate how our representer theorem can be used to express the solution of the learning problems in terms of finite kernel terms similar to classical representer theorems. Lastly, our representer theorem leads to a novel nonlinear algorithm for metric and preference learning. We compare our algorithm against challenging baseline methods on real-world rank inference benchmarks, where it achieves competitive performance. Notably, our approach significantly outperforms vanilla ideal point methods and surpasses strong baselines across multiple datasets. Code available at: https://github.com/PeymanMorteza/Metric-Preference-Learning-RKHS
Enabling immersive experiences in challenging network conditions
Venue: arXiv cs.NI 2025
Year: 2025

Immersive experiences, such as remote collaboration and augmented and virtual reality, require delivery of large volumes of data with consistent ultra-low latency across wireless networks in fluctuating network conditions. We describe the high-level design behind a data delivery solution that meets these requirements and provide synthetic simulations and test results running in network conditions based on real-world measurements demonstrating the efficacy of the solution.
A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty
Venue: arXiv cs.SI 2025
Year: 2025

In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often uncertain, thereby rendering established community detection approaches ineffective without costly network topology acquisition. To tackle this challenge, we present META-CODE, a unified framework for detecting overlapping communities via exploratory learning aided by easy-to-collect node metadata when networks are topologically unknown (or only partially known). Specifically, META-CODE consists of three iterative steps in addition to the initial network inference step: 1) node-level community-affiliation embeddings based on graph neural networks (GNNs) trained by our new reconstruction loss, 2) network exploration via community-affiliation-based node queries, and 3) network inference using an edge connectivity-based Siamese neural network model from the explored network. Through extensive experiments on three real-world datasets including two large networks, we demonstrate: (a) the superiority of META-CODE over benchmark community detection methods, achieving remarkable gains up to 65.55% on the Facebook dataset over the best competitor among our selected competitive methods in terms of normalized mutual information (NMI), (b) the impact of each module in META-CODE, (c) the effectiveness of node queries in META-CODE based on empirical evaluations and theoretical findings, and (d) the convergence of the inferred network.
Bayes correlated equilibria, no-regret dynamics in Bayesian games, and the price of anarchy
Venue: arXiv cs.GT 2025
Year: 2025

This paper investigates equilibrium computation and the price of anarchy for Bayesian games, which are the fundamental models of games with incomplete information. In normal-form games with complete information, it is known that efficiently computable no-regret dynamics converge to correlated equilibria, and the price of anarchy for correlated equilibria can be bounded for a broad class of games called smooth games. However, in Bayesian games, as surveyed by Forges (1993), several non-equivalent extensions of correlated equilibria exist, and it remains unclear whether they can be efficiently computed or whether their price of anarchy can be bounded. In this paper, we identify a natural extension of correlated equilibria that can be computed efficiently and is guaranteed to have bounds on the price of anarchy in various games. First, we propose a variant of regret called untruthful swap regret. If each player minimizes it in repeated play of Bayesian games, the empirical distribution of these dynamics is guaranteed to converge to communication equilibria, which is one of the extensions of correlated equilibria proposed by Myerson (1982). We present an efficient algorithm for minimizing untruthful swap regret with a sublinear upper bound, which we prove to be tight in terms of the number of types. As a result, by simulating the dynamics with our algorithm, we can approximately compute a communication equilibrium in polynomial time. Furthermore, we extend existing lower bounds on the price of anarchy based on the smoothness arguments from Bayes--Nash equilibria to equilibria obtained by the proposed dynamics.
Traffic Modeling with SUMO: a Tutorial
Venue: arXiv cs.NI 2025
Year: 2025

This paper presents a step-by-step guide to generating and simulating a traffic scenario using the open-source simulation tool SUMO. It introduces the common pipeline used to generate a synthetic traffic model for SUMO, how to import existing traffic data into a model to achieve accuracy in traffic simulation (that is, producing a traffic model which dynamics is similar to the real one). It also describes how SUMO outputs information from simulation that can be used for data analysis purposes.
VidStyleODE: Disentangled Video Editing via StyleGAN and NeuralODEs
Venue: arXiv cs.CV 2025
Year: 2025

We propose $\textbf{VidStyleODE}$, a spatiotemporally continuous disentangled $\textbf{Vid}$eo representation based upon $\textbf{Style}$GAN and Neural-$\textbf{ODE}$s. Effective traversal of the latent space learned by Generative Adversarial Networks (GANs) has been the basis for recent breakthroughs in image editing. However, the applicability of such advancements to the video domain has been hindered by the difficulty of representing and controlling videos in the latent space of GANs. In particular, videos are composed of content (i.e., appearance) and complex motion components that require a special mechanism to disentangle and control. To achieve this, VidStyleODE encodes the video content in a pre-trained StyleGAN $\mathcal{W}_+$ space and benefits from a latent ODE component to summarize the spatiotemporal dynamics of the input video. Our novel continuous video generation process then combines the two to generate high-quality and temporally consistent videos with varying frame rates. We show that our proposed method enables a variety of applications on real videos: text-guided appearance manipulation, motion manipulation, image animation, and video interpolation and extrapolation. Project website: https://cyberiada.github.io/VidStyleODE
Majority is not Needed: A Counterstrategy to Selfish Mining
Venue: arXiv cs.CR 2025
Year: 2025

In the last few years several papers investigated selfish mine attacks, most of which assumed that every miner that is not part of the selfish mine pool will continue to mine honestly. However, in reality, remaining honest is not always incentivized, particularly when another pool is employing selfish mining or other deviant strategies. In this work we explore the scenario in which a large enough pool capitalises on another selfish pool to gain 100\% of the profit and commit double spending attacks. We show that this counterstrategy can effectively counter any deviant strategy, and that even the possibility of it discourages other pools from implementing deviant strategies.
Submerse: Visualizing Storm Surge Flooding Simulations in Immersive Display Ecologies
Venue: arXiv cs.HC 2025
Year: 2025

We present Submerse, an end-to-end framework for visualizing flooding scenarios on large and immersive display ecologies. Specifically, we reconstruct a surface mesh from input flood simulation data and generate a to-scale 3D virtual scene by incorporating geographical data such as terrain, textures, buildings, and additional scene objects. To optimize computation and memory performance for large simulation datasets, we discretize the data on an adaptive grid using dynamic quadtrees and support level-of-detail based rendering. Moreover, to provide a perception of flooding direction for a time instance, we animate the surface mesh by synthesizing water waves. As interaction is key for effective decision-making and analysis, we introduce two novel techniques for flood visualization in immersive systems: (1) an automatic scene-navigation method using optimal camera viewpoints generated for marked points-of-interest based on the display layout, and (2) an AR-based focus+context technique using an auxiliary display system. Submerse is developed in collaboration between computer scientists and atmospheric scientists. We evaluate the effectiveness of our system and application by conducting workshops with emergency managers, domain experts, and concerned stakeholders in the Stony Brook Reality Deck, an immersive gigapixel facility, to visualize a superstorm flooding scenario in New York City.
On Approximate Reconfigurability of Label Cover
Venue: arXiv cs.DM 2025
Year: 2025

Given a two-prover game $G$ and its two satisfying labelings $ψ_\mathsf{ini}$ and $ψ_\mathsf{tar}$, the Label Cover Reconfiguration problem asks whether $ψ_\mathsf{ini}$ can be transformed into $ψ_\mathsf{tar}$ by repeatedly changing the label of a single vertex while preserving any intermediate labeling satisfying $G$. We consider its optimization version by relaxing the feasibility of labelings, referred to as Maxmin Label Cover Reconfiguration: We are allowed to pass through any non-satisfying labelings, but required to maximize the ``soundness error,'' which is defined as the minimum fraction of satisfied edges during transformation from $ψ_\mathsf{ini}$ to $ψ_\mathsf{tar}$. Since the parallel repetition theorem of Raz (SIAM J. Comput., 1998), which implies $\mathbf{NP}$-hardness of approximating Label Cover within any constant factor, gives strong inapproximability results for many $\mathbf{NP}$-hard problems, one may think of using Maxmin Label Cover Reconfiguration to derive inapproximability results for reconfiguration problems. We prove the following results on Maxmin Label Cover Reconfiguration, which display different trends from those of Label Cover and the parallel repetition theorem: $\bullet$ Maxmin Label Cover Reconfiguration can be approximated within a factor of $\frac{1}{4} - o(1)$ for some restricted graph classes, including biregular graphs, balanced bipartite graphs with no isolated vertices, and superconstant average degree graphs. $\bullet$ A ``naive'' parallel repetition of Maxmin Label Cover Reconfiguration does not decrease the soundness error for every two-prover game. $\bullet$ Label Cover Reconfiguration on projection games can be decided in polynomial time. Our results suggest that a reconfiguration analogue of the parallel repetition theorem is unlikely.
Multi-robot Motion Planning based on Nets-within-Nets Modeling and Simulation
Venue: arXiv cs.RO 2025
Year: 2025

This paper focuses on designing motion plans for a heterogeneous team of robots that must cooperate to fulfill a global mission. Robots move in an environment that contains some regions of interest, while the specification for the entire team can include avoidance, visits, or sequencing of these regions of interest. The mission is expressed in terms of a Petri net corresponding to an automaton, while each robot is also modeled by a state machine Petri net. The current work brings about the following contributions with respect to existing solutions for related problems. First, we propose a novel model, denoted High-Level robot team Petri Net (HLrtPN) system, to incorporate the specification and robot models into the Nets-within-Nets paradigm. A guard function, named Global Enabling Function, is designed to synchronize the firing of transitions so that robot motions do not violate the specification. Then, the solution is found by simulating the HLrtPN system in a specific software tool that accommodates Nets-within-Nets. Illustrative examples based on Linear Temporal Logic missions support the computational feasibility of the proposed framework.
Autonomous RISs and Oblivious Base Stations: The Observer Effect and its Mitigation
Venue: arXiv cs.IT 2025
Year: 2025

Autonomous reconfigurable intelligent surfaces (RISs) offer the potential to simplify deployment by reducing the need for real-time remote control between a base station (BS) and an RIS. However, we highlight two major challenges posed by autonomy. The first is implementation complexity, as autonomy requires hybrid RISs (HRISs) equipped with additional onboard hardware to monitor the propagation environment and perform local channel estimation (CHEST), a process known as probing. The second challenge, termed probe distortion, reflects a form of the observer effect: during probing, an HRIS can inadvertently alter the propagation environment, potentially disrupting the operations of other communicating devices sharing the environment. Although implementation complexity has been extensively studied, probe distortion remains largely unexplored. To further assess the potential of autonomous RIS, this paper comprehensively and pragmatically studies the fundamental trade-offs posed by these challenges collectively. In particular, we examine the robustness of an HRIS-assisted massive multiple-input multiple-output (mMIMO) system by considering its critical components and stringent conditions. The latter include: (a) two extremes of implementation complexity, represented by minimalist operation designs of two distinct HRIS hardware architectures, and (b) an oblivious BS that fully embraces probe distortion. To make our analysis possible, we propose a physical-layer orchestration framework that aligns HRIS and mMIMO operations. We present empirical evidence that autonomous RISs remain promising under stringent conditions and outline research directions to deepen probe distortion understanding.