OpenRaiser/Intern-Atlas huggingface dataset:
Brain Stroke Detection and Classification Using CT Imaging with Transformer Models and Explainable AI
Venue: arXiv cs.CV 2025
Year: 2025

Stroke is one of the leading causes of death globally, making early and accurate diagnosis essential for improving patient outcomes, particularly in emergency settings where timely intervention is critical. CT scans are the key imaging modality because of their speed, accessibility, and cost-effectiveness. This study proposed an artificial intelligence framework for multiclass stroke classification (ischemic, hemorrhagic, and no stroke) using CT scan images from a dataset provided by the Republic of Turkey's Ministry of Health. The proposed method adopted MaxViT, a state-of-the-art Vision Transformer, as the primary deep learning model for image-based stroke classification, with additional transformer variants (vision transformer, transformer-in-transformer, and ConvNext). To enhance model generalization and address class imbalance, we applied data augmentation techniques, including synthetic image generation. The MaxViT model trained with augmentation achieved the best performance, reaching an accuracy and F1-score of 98.00%, outperforming all other evaluated models and the baseline methods. The primary goal of this study was to distinguish between stroke types with high accuracy while addressing crucial issues of transparency and trust in artificial intelligence models. To achieve this, Explainable Artificial Intelligence (XAI) was integrated into the framework, particularly Grad-CAM++. It provides visual explanations of the model's decisions by highlighting relevant stroke regions in the CT scans and establishing an accurate, interpretable, and clinically applicable solution for early stroke detection. This research contributed to the development of a trustworthy AI-assisted diagnostic tool for stroke, facilitating its integration into clinical practice and enhancing access to timely and optimal stroke diagnosis in emergency departments, thereby saving more lives.
Minimum-Peak-Cost Flows Over Time
Venue: arXiv cs.DS 2025
Year: 2025

When planning transportation whose operation requires non-consumable resources, the peak demand for allocated resources is often of higher interest than the duration of resource usage. For instance, it is more cost-effective to deliver parcels with a single truck over eight hours than to use two trucks for four hours, as long as the time suffices. To model such scenarios, we introduce the novel minimum peak cost flow over time problem, whose objective is to minimise the maximum cost at all points in time rather than minimising the integral of costs. We focus on minimising peak costs of temporally repeated flows. These are desirable for practical applications due to their simple structure. This yields the minimum-peak-cost temporally repeated flow problem (MPC-TRF). We show that the simple structure of temporally repeated flows comes with the drawback of arbitrarily bad approximation ratios compared to general flows over time. Furthermore, our complexity analysis shows the integral version of MPC-TRF is strongly NP-hard, even under strong restrictions. On the positive side, we identify two benign special cases: unit-cost series-parallel networks and networks with time horizon at least twice as long as the longest path in the network (with respect to the transit time). In both cases, we show that integral optimal flows if the desired flow value equals the maximum flow value and fractional optimal flows for arbitrary flow values can be found in polynomial time. For each of these cases, we provide an explicit algorithm that constructs an optimal solution.
Frequency-aware Surrogate Modeling With SMT Kernels For Advanced Data Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

This paper introduces a comprehensive open-source framework for developing correlation kernels, with a particular focus on user-defined and composition of kernels for surrogate modeling. By advancing kernel-based modeling techniques, we incorporate frequency-aware elements that effectively capture complex mechanical behaviors and timefrequency dynamics intrinsic to aircraft systems. Traditional kernel functions, often limited to exponential-based methods, are extended to include a wider range of kernels such as exponential squared sine and rational quadratic kernels, along with their respective firstand second-order derivatives. The proposed methodologies are first validated on a sinus cardinal test case and then applied to forecasting Mauna-Loa Carbon Dioxide (CO 2 ) concentrations and airline passenger traffic. All these advancements are integrated into the open-source Surrogate Modeling Toolbox (SMT 2.0), providing a versatile platform for both standard and customizable kernel configurations. Furthermore, the framework enables the combination of various kernels to leverage their unique strengths into composite models tailored to specific problems. The resulting framework offers a flexible toolset for engineers and researchers, paving the way for numerous future applications in metamodeling for complex, frequency-sensitive domains.
A Study Of Sudoku Solving Algorithms: Backtracking and Heuristic
Venue: arXiv cs.LO 2025
Year: 2025

This paper presents a comparative analysis of Sudoku-solving strategies, focusing on recursive backtracking and a heuristic-based constraint propagation method. Using a dataset of 500 puzzles across five difficulty levels (Beginner to Expert), we evaluated performance based on average solving time. The heuristic approach consistently outperformed backtracking, achieving speedup ratios ranging from 1.27x in Beginner puzzles to 2.91x in Expert puzzles. These findings underscore the effectiveness of heuristic strategies, particularly in tackling complex puzzles across varying difficulty levels.
Visual Homing in Outdoor Robots Using Mushroom Body Circuits and Learning Walks
Venue: arXiv cs.RO 2025
Year: 2025

Ants achieve robust visual homing with minimal sensory input and only a few learning walks, inspiring biomimetic solutions for autonomous navigation. While Mushroom Body (MB) models have been used in robotic route following, they have not yet been applied to visual homing. We present the first real-world implementation of a lateralized MB architecture for visual homing onboard a compact autonomous car-like robot. We test whether the sign of the angular path integration (PI) signal can categorize panoramic views, acquired during learning walks and encoded in the MB, into "goal on the left" and "goal on the right" memory banks, enabling robust homing in natural outdoor settings. We validate this approach through four incremental experiments: (1) simulation showing attractor-like nest dynamics; (2) real-world homing after decoupled learning walks, producing nest search behavior; (3) homing after random walks using noisy PI emulated with GPS-RTK; and (4) precise stopping-at-the-goal behavior enabled by a fifth MB Output Neuron (MBON) encoding goal-views to control velocity. This mimics the accurate homing behavior of ants and functionally resembles waypoint-based position control in robotics, despite relying solely on visual input. Operating at 8 Hz on a Raspberry Pi 4 with 32x32 pixel views and a memory footprint under 9 kB, our system offers a biologically grounded, resource-efficient solution for autonomous visual homing.
Universal Physics Simulation: A Foundational Diffusion Approach
Venue: arXiv cs.LG 2025
Year: 2025

We present the first foundational AI model for universal physics simulation that learns physical laws directly from boundary-condition data without requiring a priori equation encoding. Traditional physics-informed neural networks (PINNs) and finite-difference methods necessitate explicit mathematical formulation of governing equations, fundamentally limiting their generalizability and discovery potential. Our sketch-guided diffusion transformer approach reimagines computational physics by treating simulation as a conditional generation problem, where spatial boundary conditions guide the synthesis of physically accurate steady-state solutions. By leveraging enhanced diffusion transformer architectures with novel spatial relationship encoding, our model achieves direct boundary-to-equilibrium mapping and is generalizable to diverse physics domains. Unlike sequential time-stepping methods that accumulate errors over iterations, our approach bypasses temporal integration entirely, directly generating steady-state solutions with SSIM > 0.8 while maintaining sub-pixel boundary accuracy. Our data-informed approach enables physics discovery through learned representations analyzable via Layer-wise Relevance Propagation (LRP), revealing emergent physical relationships without predetermined mathematical constraints. This work represents a paradigm shift from AI-accelerated physics to AI-discovered physics, establishing the first truly universal physics simulation framework.
Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts
Venue: arXiv cs.LG 2025
Year: 2025

Transcription Factor Binding Site (TFBS) prediction is crucial for understanding gene regulation and various biological processes. This study introduces a novel Mixture of Experts (MoE) approach for TFBS prediction, integrating multiple pre-trained Convolutional Neural Network (CNN) models, each specializing in different TFBS patterns. We evaluate the performance of our MoE model against individual expert models on both in-distribution and out-of-distribution (OOD) datasets, using six randomly selected transcription factors (TFs) for OOD testing. Our results demonstrate that the MoE model achieves competitive or superior performance across diverse TF binding sites, particularly excelling in OOD scenarios. The Analysis of Variance (ANOVA) statistical test confirms the significance of these performance differences. Additionally, we introduce ShiftSmooth, a novel attribution mapping technique that provides more robust model interpretability by considering small shifts in input sequences. Through comprehensive explainability analysis, we show that ShiftSmooth offers superior attribution for motif discovery and localization compared to traditional Vanilla Gradient methods. Our work presents an efficient, generalizable, and interpretable solution for TFBS prediction, potentially enabling new discoveries in genome biology and advancing our understanding of transcriptional regulation.
Dynamic Pacing for Real-time Satellite Traffic
Venue: arXiv cs.NI 2025
Year: 2025

Google's congestion control (GCC) has become a cornerstone for real-time video and audio communication, yet its performance remains fragile in emerging Low Earth Orbit (LEO) networks. In this paper, we study the behavior of videoconferencing systems in LEO constellations. We observe that video quality degrades due to inherent delays and network instability introduced by the high altitude and rapid movement of LEO satellites, with these effects exacerbated by WebRTC's conventional "one-size-fits-all" sender-side pacing queue management. To address these challenges, we introduce a data-driven queue management mechanism that tunes the maximum pacing queue capacity based on predicted handover activity, minimizing latency during no-handover periods and prioritizing stability when entering periods of increased handover activity. Our method yields up to 3x improvements in video bitrate and reduces freeze rate by 62% in emulation, while delivering up to a 41% reduction in freeze rate and 40% decrease in mean packet loss on real Starlink constellations compared to WebRTC's default pacing queue policy.
Technical Requirements for Halting Dangerous AI Activities
Venue: arXiv cs.AI 2025
Year: 2025

The rapid development of AI systems poses unprecedented risks, including loss of control, misuse, geopolitical instability, and concentration of power. To navigate these risks and avoid worst-case outcomes, governments may proactively establish the capability for a coordinated halt on dangerous AI development and deployment. In this paper, we outline key technical interventions that could allow for a coordinated halt on dangerous AI activities. We discuss how these interventions may contribute to restricting various dangerous AI activities, and show how these interventions can form the technical foundation for potential AI governance plans.
Compressed Computation: Dense Circuits in a Toy Model of the Universal-AND Problem
Venue: arXiv cs.LG 2025
Year: 2025

Neural networks are capable of superposition -- representing more features than there are dimensions. Recent work considers the analogous concept for computation instead of storage, proposing theoretical constructions. But there has been little investigation into whether these circuits can be learned in practice. In this work, we investigate a toy model for the Universal-AND problem which computes the AND of all $m\choose 2$ pairs of $m$ sparse inputs. The hidden dimension that determines the number of non-linear activations is restricted to pressure the model to find a compute-efficient circuit, called compressed computation. We find that the training process finds a simple solution that does not correspond to theoretical constructions. It is fully dense -- every neuron contributes to every output. The solution circuit naturally scales with dimension, trading off error rates for neuron efficiency. It is similarly robust to changes in sparsity and other key parameters, and extends naturally to other boolean operations and boolean circuits. We explain the found solution in detail and compute why it is more efficient than the theoretical constructions at low sparsity. Our findings shed light on the types of circuits that models like to form and the flexibility of the superposition representation. This contributes to a broader understanding of network circuitry and interpretability.
Legendre Polynomials and Their Use for Karhunen-Loève Expansion
Venue: arXiv cs.CE 2025
Year: 2025

This paper makes two main contributions. First, we present a pedagogical review of the derivation of the three-term recurrence relation for Legendre polynomials, without relying on the classical Legendre differential equation, Rodrigues' formula, or generating functions. This exposition is designed to be accessible to undergraduate students. Second, we develop a computational framework for Karhunen-Loève expansions of isotropic Gaussian random fields on hyper-rectangular domains. The framework leverages Legendre polynomials and their associated Gaussian quadrature, and it remains efficient even in higher spatial dimensions. A covariance kernel is first approximated by a non-negative mixture of squared-exponentials, obtained via a Newton-optimized fit with a theoretically informed initialization. The resulting separable kernel enables a Legendre-Galerkin discretization in the form of a Kronecker product over single dimensions, with submatrices that exhibit even/odd parity structure. For assembly, we introduce a Duffy-type transformation followed by quadrature. These structural properties significantly reduce both memory usage and arithmetic cost compared to naive approaches. All algorithms and numerical experiments are provided in an open-source repository that reproduces every figure and table in this work.
Incomplete Multiview Learning via Wyner Common Information
Venue: arXiv cs.IT 2025
Year: 2025

Incomplete multiview clustering is of high recent interest, fueled by the advancement of common information-based deep multiview learning. The practical scenarios where unpaired multiview data with missing values have wide applications in generative learning, cross-modal retrieval, and wireless device identification problems. Following the perspective that the shared information between the incomplete multiview data aligns with the cluster targets, recent works have generalized the well-known common information frameworks in information theory multiview learning problems, with improved performance reported. Different from previous works, we extend the frameworks to incomplete multiview clustering problems and propose an efficient solver: Wyner Incomplete MultiView Clustering (WyIMVC). Interestingly, the common randomness in WyIMVC allows for joint clustering and missing value inference in contrast to the compared methods in the literature. Moreover, leveraging the difference-of-convex structure of the formulated problems, we propose an efficient solver with a convergence guarantee independent of initialization. Empirically, our solver outperforms the state-of-the-art solvers in a range of incomplete multiview datasets with varying numbers of views and dimensions.
Customize Harmonic Potential Fields via Hybrid Optimization over Homotopic Paths
Venue: arXiv cs.RO 2025
Year: 2025

Safe navigation within a workspace is a fundamental skill for autonomous robots to accomplish more complex tasks. Harmonic potentials are artificial potential fields that are analytical, globally convergent and provably free of local minima. Thus, it has been widely used for generating safe and reliable robot navigation control policies. However, most existing methods do not allow customization of the harmonic potential fields nor the resulting paths, particularly regarding their topological properties. In this paper, we propose a novel method that automatically finds homotopy classes of paths that can be generated by valid harmonic potential fields. The considered complex workspaces can be as general as forest worlds consisting of numerous overlapping star-obstacles. The method is based on a hybrid optimization algorithm that searches over homotopy classes, selects the structure of each tree-of-stars within the forest, and optimizes over the continuous weight parameters for each purged tree via the projected gradient descent. The key insight is to transform the forest world to the unbounded point world via proper diffeomorphic transformations. It not only facilitates a simpler design of the multi-directional D-signature between non-homotopic paths, but also retain the safety and convergence properties. Extensive simulations and hardware experiments are conducted for non-trivial scenarios, where the navigation potentials are customized for desired homotopic properties. Project page: https://shuaikang-wang.github.io/CustFields.
VerifyBench: A Systematic Benchmark for Evaluating Reasoning Verifiers Across Domains
Venue: arXiv cs.AI 2025
Year: 2025

Large language models (LLMs) increasingly rely on reinforcement learning (RL) to enhance their reasoning capabilities through feedback. A critical challenge is verifying the consistency of model-generated responses and reference answers, since these responses are often lengthy, diverse, and nuanced. Rule-based verifiers struggle with complexity, prompting the use of model-based verifiers. However, specialized verifiers lack flexibility, while general LLM judges can be inconsistent. Existing research primarily focuses on building better verifiers, yet a systematic evaluation of different types of verifiers' performance across domains remains lacking, severely constraining the reliable development of Reinforcement Learning with Verifiable Reward (RLVR). To address this, we propose VerifyBench--a cross-domain comprehensive benchmark for systematically evaluating verifiers. We construct 4,000 expert-level questions covering mathematics, physics, chemistry, and biology. Each question is equipped with reference answers and diverse responses. The reliability of the evaluation is ensured through a rigorous annotation process conducted by a multidisciplinary expert team. We design a four-dimensional experimental framework to comprehensively compare the performance boundaries of specialized verifiers and general LLMs under combined conditions of extracted answers vs. complete responses, and short vs. long outputs. Our evaluation uncovers fundamental trade-offs in verifiers: while specialized verifiers achieve leading accuracy, they exhibit deficiencies in recall; general models show stronger inclusivity but unstable precision. More importantly, we discover verifiers' high sensitivity to input structure and inherent limitations in cross-domain generalization, providing critical insights into the bottlenecks of current verifier technology.
NeuTSFlow: Modeling Continuous Functions Behind Time Series Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Time series forecasting is a fundamental task with broad applications, yet conventional methods often treat data as discrete sequences, overlooking their origin as noisy samples of continuous processes. Crucially, discrete noisy observations cannot uniquely determine a continuous function; instead, they correspond to a family of plausible functions. Mathematically, time series can be viewed as noisy observations of a continuous function family governed by a shared probability measure. Thus, the forecasting task can be framed as learning the transition from the historical function family to the future function family. This reframing introduces two key challenges: (1) How can we leverage discrete historical and future observations to learn the relationships between their underlying continuous functions? (2) How can we model the transition path in function space from the historical function family to the future function family? To address these challenges, we propose NeuTSFlow, a novel framework that leverages Neural Operators to facilitate flow matching for learning path of measure between historical and future function families. By parameterizing the velocity field of the flow in infinite-dimensional function spaces, NeuTSFlow moves beyond traditional methods that focus on dependencies at discrete points, directly modeling function-level features instead. Experiments on diverse forecasting tasks demonstrate NeuTSFlow's superior accuracy and robustness, validating the effectiveness of the function-family perspective.
Large Population Models
Venue: arXiv cs.MA 2025
Year: 2025

Many of society's most pressing challenges, from pandemic response to supply chain disruptions to climate adaptation, emerge from the collective behavior of millions of autonomous agents making decisions over time. Large Population Models (LPMs) offer an approach to understand these complex systems by simulating entire populations with realistic behaviors and interactions at unprecedented scale. LPMs extend traditional modeling approaches through three key innovations: computational methods that efficiently simulate millions of agents simultaneously, mathematical frameworks that learn from diverse real-world data streams, and privacy-preserving communication protocols that bridge virtual and physical environments. This allows researchers to observe how agent behavior aggregates into system-level outcomes and test interventions before real-world implementation. While current AI advances primarily focus on creating "digital humans" with sophisticated individual capabilities, LPMs develop "digital societies" where the richness of interactions reveals emergent phenomena. By bridging individual agent behavior and population-scale dynamics, LPMs offer a complementary path in AI research illuminating collective intelligence and providing testing grounds for policies and social innovations before real-world deployment. We discuss the technical foundations and some open problems here. LPMs are implemented by the AgentTorch framework (github.com/AgentTorch/AgentTorch)
Tie-breaking Agnostic Lower Bound for Fictitious Play
Venue: arXiv cs.GT 2025
Year: 2025

Fictitious play (FP) is a natural learning dynamic in two-player zero-sum games. Samuel Karlin conjectured in 1959 that FP converges at a rate of $O(t^{-1/2})$ to Nash equilibrium, where $t$ is the number of steps played. However, Daskalakis and Pan disproved the stronger form of this conjecture in 2014, where \emph{adversarial} tie-breaking is allowed. This paper disproves Karlin's conjecture in its weaker form. In particular, there exists a 10-by-10 zero-sum matrix game, in which FP converges at a rate of $Ω(t^{-1/3})$, and no ties occur except for the first step.