OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Language Agent Strategic Reasoning through Self-Play in Adversarial Games
Venue: arXiv cs.CL 2025
Year: 2025

Existing language agents often encounter difficulties in dynamic adversarial games due to poor strategic reasoning. To mitigate this limitation, a promising approach is to allow agents to learn from game interactions automatically, without relying on costly expert-labeled data. Unlike static environments where agents receive fixed feedback or rewards, selecting appropriate opponents in dynamic adversarial games can significantly impact learning performance. However, the discussion of opponents in adversarial environments remains an area under exploration. In this paper, we propose a Step-level poliCy Optimization method through Play-And-Learn, SCO-PAL. Leveraging SCO-PAL, we conduct a detailed analysis of opponent selection by setting opponents at different levels and find that self-play is the most effective way to improve strategic reasoning in such adversarial environments. Utilizing SCO-PAL with self-play, we increase the average win rate against four opponents by approximately 30% compared to baselines and achieve a 54.76% win rate against GPT-4 in six adversarial games.
EMRRG: Efficient Fine-Tuning Pre-trained X-ray Mamba Networks for Radiology Report Generation
Venue: arXiv cs.CV 2025
Year: 2025

X-ray image-based medical report generation (MRG) is a pivotal area in artificial intelligence that can significantly reduce diagnostic burdens for clinicians and patient wait times. Existing MRG models predominantly rely on Large Language Models (LLMs) to improve report generation, with limited exploration of pre-trained vision foundation models or advanced fine-tuning techniques. Mainstream frameworks either avoid fine-tuning or utilize simplistic methods like LoRA, often neglecting the potential of enhancing cross-attention mechanisms. Additionally, while Transformer-based models dominate vision-language tasks, non-Transformer architectures, such as the Mamba network, remain underexplored for medical report generation, presenting a promising avenue for future research. In this paper, we propose EMRRG, a novel X-ray report generation framework that fine-tunes pre-trained Mamba networks using parameter-efficient methods. Specifically, X-ray images are divided into patches, tokenized, and processed by an SSM-based vision backbone for feature extraction, with Partial LoRA yielding optimal performance. An LLM with a hybrid decoder generates the medical report, enabling end-to-end training and achieving strong results on benchmark datasets. Extensive experiments on three widely used benchmark datasets fully validated the effectiveness of our proposed strategies for the X-ray MRG. The source code of this paper will be released on https://github.com/Event-AHU/Medical_Image_Analysis.
GS2POSE: Marry Gaussian Splatting to 6D Object Pose Estimation
Venue: arXiv cs.CV 2025
Year: 2025

Accurate 6D pose estimation of 3D objects is a fundamental task in computer vision, and current research typically predicts the 6D pose by establishing correspondences between 2D image features and 3D model features. However, these methods often face difficulties with textureless objects and varying illumination conditions. To overcome these limitations, we propose GS2POSE, a novel approach for 6D object pose estimation. GS2POSE formulates a pose regression algorithm inspired by the principles of Bundle Adjustment (BA). By leveraging Lie algebra, we extend the capabilities of 3DGS to develop a pose-differentiable rendering pipeline, which iteratively optimizes the pose by comparing the input image to the rendered image. Additionally, GS2POSE updates color parameters within the 3DGS model, enhancing its adaptability to changes in illumination. Compared to previous models, GS2POSE demonstrates accuracy improvements of 1.4\%, 2.8\% and 2.5\% on the T-LESS, LineMod-Occlusion and LineMod datasets, respectively.
QuanBench: Benchmarking Quantum Code Generation with Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

Large language models (LLMs) have demonstrated good performance in general code generation; however, their capabilities in quantum code generation remain insufficiently studied. This paper presents QuanBench, a benchmark for evaluating LLMs on quantum code generation. QuanBench includes 44 programming tasks that cover quantum algorithms, state preparation, gate decomposition, and quantum machine learning. Each task has an executable canonical solution and is evaluated by functional correctness (Pass@K) and quantum semantic equivalence (Process Fidelity). We evaluate several recent LLMs, including general-purpose and code-specialized models. The results show that current LLMs have limited capability in generating the correct quantum code, with overall accuracy below 40% and frequent semantic errors. We also analyze common failure cases, such as outdated API usage, circuit construction errors, and incorrect algorithm logic. QuanBench provides a basis for future work on improving quantum code generation with LLMs.
From Mannequin to Human: A Pose-Aware and Identity-Preserving Video Generation Framework for Lifelike Clothing Display
Venue: arXiv cs.CV 2025
Year: 2025

Mannequin-based clothing displays offer a cost-effective alternative to real-model showcases for online fashion presentation, but lack realism and expressive detail. To overcome this limitation, we introduce a new task called mannequin-to-human (M2H) video generation, which aims to synthesize identity-controllable, photorealistic human videos from footage of mannequins. We propose M2HVideo, a pose-aware and identity-preserving video generation framework that addresses two key challenges: the misalignment between head and body motion, and identity drift caused by temporal modeling. In particular, M2HVideo incorporates a dynamic pose-aware head encoder that fuses facial semantics with body pose to produce consistent identity embeddings across frames. To address the loss of fine facial details due to latent space compression, we introduce a mirror loss applied in pixel space through a denoising diffusion implicit model (DDIM)-based one-step denoising. Additionally, we design a distribution-aware adapter that aligns statistical distributions of identity and clothing features to enhance temporal coherence. Extensive experiments on the UBC fashion dataset, our self-constructed ASOS dataset, and the newly collected MannequinVideos dataset captured on-site demonstrate that M2HVideo achieves superior performance in terms of clothing consistency, identity preservation, and video fidelity in comparison to state-of-the-art methods.
ThreatIntel-Andro: Expert-Verified Benchmarking for Robust Android Malware Research
Venue: arXiv cs.CR 2025
Year: 2025

The rapidly evolving Android malware ecosystem demands high-quality, real-time datasets as a foundation for effective detection and defense. With the widespread adoption of mobile devices across industrial systems, they have become a critical yet often overlooked attack surface in industrial cybersecurity. However, mainstream datasets widely used in academia and industry (e.g., Drebin) exhibit significant limitations: on one hand, their heavy reliance on VirusTotal's multi-engine aggregation results introduces substantial label noise; on the other hand, outdated samples reduce their temporal relevance. Moreover, automated labeling tools (e.g., AVClass2) suffer from suboptimal aggregation strategies, further compounding labeling errors and propagating inaccuracies throughout the research community.
DiRAC - Distributed Robot Awareness and Consensus
Venue: arXiv cs.MA 2025
Year: 2025

DiRAC is a scalable, distributed framework designed to enable efficient task assignment and path planning in very large robotic swarms. It introduces a novel zone-partitioned architecture with dynamically elected leaders and a tick-synchronized consensus protocol that yields strong consistency and deterministic outcomes. For path planning, DiRAC uses a novel algorithm, a force-based decentralized planner for real-time collision resolution. Validated within ROS 2 middleware through preliminary simulation, DiRAC demonstrates architectural scalability and modular efficiency in simulated warehouse environments, laying the groundwork for real-world deployment in large-scale industrial and logistics domains.
Uncovering Brain-Like Hierarchical Patterns in Vision-Language Models through fMRI-Based Neural Encoding
Venue: arXiv cs.CV 2025
Year: 2025

While brain-inspired artificial intelligence(AI) has demonstrated promising results, current understanding of the parallels between artificial neural networks (ANNs) and human brain processing remains limited: (1) unimodal ANN studies fail to capture the brain's inherent multimodal processing capabilities, and (2) multimodal ANN research primarily focuses on high-level model outputs, neglecting the crucial role of individual neurons. To address these limitations, we propose a novel neuron-level analysis framework that investigates the multimodal information processing mechanisms in vision-language models (VLMs) through the lens of human brain activity. Our approach uniquely combines fine-grained artificial neuron (AN) analysis with fMRI-based voxel encoding to examine two architecturally distinct VLMs: CLIP and METER. Our analysis reveals four key findings: (1) ANs successfully predict biological neurons (BNs) activities across multiple functional networks (including language, vision, attention, and default mode), demonstrating shared representational mechanisms; (2) Both ANs and BNs demonstrate functional redundancy through overlapping neural representations, mirroring the brain's fault-tolerant and collaborative information processing mechanisms; (3) ANs exhibit polarity patterns that parallel the BNs, with oppositely activated BNs showing mirrored activation trends across VLM layers, reflecting the complexity and bidirectional nature of neural information processing; (4) The architectures of CLIP and METER drive distinct BNs: CLIP's independent branches show modality-specific specialization, whereas METER's cross-modal design yields unified cross-modal activation, highlighting the architecture's influence on ANN brain-like properties. These results provide compelling evidence for brain-like hierarchical processing in VLMs at the neuronal level.
Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosis
Venue: arXiv cs.CV 2025
Year: 2025

Generative models, especially Diffusion Models, have demonstrated remarkable capability in generating high-quality synthetic data, including medical images. However, traditional class-conditioned generative models often struggle to generate images that accurately represent specific medical categories, limiting their usefulness for applications such as skin cancer diagnosis. To address this problem, we propose a classification-induced diffusion model, namely, Class-N-Diff, to simultaneously generate and classify dermoscopic images. Our Class-N-Diff model integrates a classifier within a diffusion model to guide image generation based on its class conditions. Thus, the model has better control over class-conditioned image synthesis, resulting in more realistic and diverse images. Additionally, the classifier demonstrates improved performance, highlighting its effectiveness for downstream diagnostic tasks. This unique integration in our Class-N-Diff makes it a robust tool for enhancing the quality and utility of diffusion model-based synthetic dermoscopic image generation. Our code is available at https://github.com/Munia03/Class-N-Diff.
C-Free-Uniform: A Map-Conditioned Trajectory Sampler for Model Predictive Path Integral Control
Venue: arXiv cs.RO 2025
Year: 2025

Trajectory sampling is a key component of sampling-based control mechanisms. Trajectory samplers rely on control input samplers, which generate control inputs u from a distribution p(u | x) where x is the current state. We introduce the notion of Free Configuration Space Uniformity (C-Free-Uniform for short) which has two key features: (i) it generates a control input distribution so as to uniformly sample the free configuration space, and (ii) in contrast to previously introduced trajectory sampling mechanisms where the distribution p(u | x) is independent of the environment, C-Free-Uniform is explicitly conditioned on the current local map. Next, we integrate this sampler into a new Model Predictive Path Integral (MPPI) Controller, CFU-MPPI. Experiments show that CFU-MPPI outperforms existing methods in terms of success rate in challenging navigation tasks in cluttered polygonal environments while requiring a much smaller sampling budget.
Does Visual Grounding Enhance the Understanding of Embodied Knowledge in Large Language Models?
Venue: arXiv cs.CL 2025
Year: 2025

Despite significant progress in multimodal language models (LMs), it remains unclear whether visual grounding enhances their understanding of embodied knowledge compared to text-only models. To address this question, we propose a novel embodied knowledge understanding benchmark based on the perceptual theory from psychology, encompassing visual, auditory, tactile, gustatory, olfactory external senses, and interoception. The benchmark assesses the models' perceptual abilities across different sensory modalities through vector comparison and question-answering tasks with over 1,700 questions. By comparing 30 state-of-the-art LMs, we surprisingly find that vision-language models (VLMs) do not outperform text-only models in either task. Moreover, the models perform significantly worse in the visual dimension compared to other sensory dimensions. Further analysis reveals that the vector representations are easily influenced by word form and frequency, and the models struggle to answer questions involving spatial perception and reasoning. Our findings underscore the need for more effective integration of embodied knowledge in LMs to enhance their understanding of the physical world.
Prompt-MII: Meta-Learning Instruction Induction for LLMs
Venue: arXiv cs.CL 2025
Year: 2025

A popular method to adapt large language models (LLMs) to new tasks is in-context learning (ICL), which is effective but incurs high inference costs as context length grows. In this paper we propose a method to perform instruction induction, where we take training examples and reduce them to a compact but descriptive prompt that can achieve performance comparable to ICL over the full training set. Specifically, we propose PROMPT-MII, a reinforcement learning (RL) based framework to meta-learn an instruction induction model that can generate compact instructions on the fly for an arbitrary new dataset. We train on over 3,000 diverse classification datasets from the HuggingFace hub, and evaluate on 90 unseen tasks. PROMPT-MII improves downstream model quality by 4-9 F1 points (10-20% relative), matching ICL performance while requiring 3-13x fewer tokens.
A Scalable In Transit Solution for Comprehensive Exploration of Simulation Data
Venue: arXiv cs.GR 2025
Year: 2025

As simulations produce more data than available disk space on supercomputers, many simulations are employing in situ analysis and visualization to reduce the amount of data that needs to be stored. While in situ visualization offers potential for substantial data reduction, its efficacy is hindered by the need for a priori knowledge. First, we need to know what visualization parameters to use to highlight features of interest. Second, we do not know ahead of time how much resources will be needed to run the in situ workflows, e.g. how many compute nodes will be needed for in situ work. In this work, we present SeerX, a lightweight, scalable in-transit in situ service that supports dynamic resource allocation and lossy compression of 3D simulation data. SeerX enables multiple simulations to offload analysis to a shared, elastic service infrastructure without MPI synchronization.
A Multi-echelon Demand-driven Supply Chain Model for Proactive Optimal Control of Epidemics: Insights from a COVID-19 Study
Venue: arXiv cs.SI 2025
Year: 2025

Timely and effective decision-making is critical during epidemics to reduce preventable infections and deaths. This demands integrated models that jointly capture disease dynamics, vaccine distribution, regional disparities, and behavioral responses. However, most existing approaches decouple epidemic forecasting from logistics planning, hindering adaptive and regionally responsive interventions. We propose a novel epidemiological-optimization framework that jointly models epidemic progression and a multiscale vaccine supply chain. The model incorporates spatio-temporally varying effective infection rates to reflect regional policy and behavioral dynamics. It supports coordinated, data-driven decision-making across spatial scales through two formulations: a multi-objective Gini-based model and a knapsack-based model that leverages regional vulnerability indicators for tractability and improved mitigation. To address computational complexity, we design two scalable heuristic decomposition algorithms inspired by the Benders decomposition. The model is validated using COVID-19 data in the U.S.. We introduce SARIMA-based forecasting as a novel approach for validating epidemic-optimization models under data limitations. The results show that our approach can prevent more than 2 million infections and 30,000 deaths in just six months while significantly improving the accessibility of vaccines in underserved regions. Our framework demonstrates that integrating fairness and epidemic dynamics with vaccine logistics leads to superior outcomes compared to traditional myopic policies. Fairness improves overall efficiency in the long term by prioritizing the most vulnerable populations, leading to better long-term public health outcomes. The model offers policymakers a scalable and operationally relevant tool to strengthen preparedness and ensure a more effective and equitable response to epidemics.
Foundation Models in Medical Image Analysis: A Systematic Review and Meta-Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in artificial intelligence (AI), particularly foundation models (FMs), have revolutionized medical image analysis, demonstrating strong zero- and few-shot performance across diverse medical imaging tasks, from segmentation to report generation. Unlike traditional task-specific AI models, FMs leverage large corpora of labeled and unlabeled multimodal datasets to learn generalized representations that can be adapted to various downstream clinical applications with minimal fine-tuning. However, despite the rapid proliferation of FM research in medical imaging, the field remains fragmented, lacking a unified synthesis that systematically maps the evolution of architectures, training paradigms, and clinical applications across modalities. To address this gap, this review article provides a comprehensive and structured analysis of FMs in medical image analysis. We systematically categorize studies into vision-only and vision-language FMs based on their architectural foundations, training strategies, and downstream clinical tasks. Additionally, a quantitative meta-analysis of the studies was conducted to characterize temporal trends in dataset utilization and application domains. We also critically discuss persistent challenges, including domain adaptation, efficient fine-tuning, computational constraints, and interpretability along with emerging solutions such as federated learning, knowledge distillation, and advanced prompting. Finally, we identify key future research directions aimed at enhancing the robustness, explainability, and clinical integration of FMs, thereby accelerating their translation into real-world medical practice.
ReclAIm: A multi-agent framework for degradation-aware performance tuning of medical imaging AI
Venue: arXiv cs.MA 2025
Year: 2025

Ensuring the long-term reliability of AI models in clinical practice requires continuous performance monitoring and corrective actions when degradation occurs. Addressing this need, this manuscript presents ReclAIm, a multi-agent framework capable of autonomously monitoring, evaluating, and fine-tuning medical image classification models. The system, built on a large language model core, operates entirely through natural language interaction, eliminating the need for programming expertise. ReclAIm successfully trains, evaluates, and maintains consistent performance of models across MRI, CT, and X-ray datasets. Once ReclAIm detects significant performance degradation, it autonomously executes state-of-the-art fine-tuning procedures that substantially reduce the performance gap. In cases with performance drops of up to -41.1% (MRI InceptionV3), ReclAIm managed to readjust performance metrics within 1.5% of the initial model results. ReclAIm enables automated, continuous maintenance of medical imaging AI models in a user-friendly and adaptable manner that facilitates broader adoption in both research and clinical environments.
Bombardier Beetle Optimizer: A Novel Bio-Inspired Algorithm for Global Optimization
Venue: arXiv cs.NE 2025
Year: 2025

In this paper, a novel bio-inspired optimization algorithm is proposed, called Bombardier Beetle Optimizer (BBO). This type of species is very intelligent, which has an ability to defense and escape from predators. The principles of the former one is inspired by the defense mechanism of Bombardier Beetle against the predators, which the Bombardier Beetle triggers a toxic chemical spray when it feels threatened. This reaction occurs in a specialized reaction chamber inside its abdomen and includes a well regulated enzymatic mechanism, which comprises hot water vapor, oxygen, and irritating substances like p-benzoquinones. In addition, the proposed BBO simulates also the escape mechanism of Bombardier Beetle from predator, which it has the ability to calculate its distance from predator and it can fly away. The BBO is tested with optimizing Congress on Evolutionary Computation (CEC 2017) test bed suites. Moreover, it is compared against well-known metaheuristic optimization algorithms includes Chernobyl Disaster Optimizer (CDO), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Bermuda Triangle Optimizer (BTO), Sperm Swarm Optimization (SSO) and Gravitational Search Algorithm (GSA). The outcomes of this paper prove the BBO's efficiency in which outperforms the other algorithms in terms of convergence rate and quality of results.
Traffic Prioritization Mechanisms for Mission and Time Critical Applications in Industrial Internet of Things
Venue: arXiv cs.NI 2025
Year: 2025

Industrial Internet of Things (IIoT) promises to revolutionize industrial operations and productions through utilizing Machine-to-Machine (M2M) communications. Since each node in such environments generates various types of data with diverse service requirements, MAC protocol holds crucial importance to ensure efficient delivery. In this context, simple to complex MAC schemes are found in literature. This paper focuses on evaluating the performance of two major techniques "slot stealing" and "packet fragmentation" for the IIoT; representative protocols SS-MAC and FROG-MAC have been chosen from each category respectively. We conducted realistic simulations for the two protocols using Contiki. Delay and packet loss comparison for SS-MAC and FROG-MAC indicates the superiority of FROG-MAC due to reduction in the waiting time for urgent traffic. Thus, a simple fragmentation scheme could be deployed for efficient scheduling of heterogenous traffic in the industrial environments.
Curiosity-driven RL for symbolic equation solving
Venue: arXiv cs.LG 2025
Year: 2025

We explore if RL can be useful for symbolic mathematics. Previous work showed contrastive learning can solve linear equations in one variable. We show model-free PPO \cite{schulman2017proximal} augmented with curiosity-based exploration and graph-based actions can solve nonlinear equations such as those involving radicals, exponentials, and trig functions. Our work suggests curiosity-based exploration may be useful for general symbolic reasoning tasks.