OpenRaiser/Intern-Atlas huggingface dataset:
Prostate Cancer Classification Using Multimodal Feature Fusion and Explainable AI
Venue: arXiv cs.LG 2025
Year: 2025

Prostate cancer, the second most prevalent male malignancy, requires advanced diagnostic tools. We propose an explainable AI system combining BERT (for textual clinical notes) and Random Forest (for numerical lab data) through a novel multimodal fusion strategy, achieving superior classification performance on PLCO-NIH dataset (98% accuracy, 99% AUC). While multimodal fusion is established, our work demonstrates that a simple yet interpretable BERT+RF pipeline delivers clinically significant improvements - particularly for intermediate cancer stages (Class 2/3 recall: 0.900 combined vs 0.824 numerical/0.725 textual). SHAP analysis provides transparent feature importance rankings, while ablation studies prove textual features' complementary value. This accessible approach offers hospitals a balance of high performance (F1=89%), computational efficiency, and clinical interpretability - addressing critical needs in prostate cancer diagnostics.
Exascale Implicit Kinetic Plasma Simulations on El~Capitan for Solving the Micro-Macro Coupling in Magnetospheric Physics
Venue: arXiv cs.CE 2025
Year: 2025

Our fully kinetic, implicit Particle-in-Cell (PIC) simulations of global magnetospheres on up to 32,768 of El Capitan's AMD Instinct MI300A Accelerated Processing Units (APUs) represent an unprecedented computational capability that addresses a fundamental challenge in space physics: resolving the multi-scale coupling between microscopic (electron-scale) and macroscopic (global-scale) dynamics in planetary magnetospheres. The implicit scheme of iPIC3D supports time steps and grid spacing that are up to 10 times larger than those of explicit methods, without sacrificing physical accuracy. This enables the simulation of magnetospheres while preserving fine-scale electron physics, which is critical for key processes such as magnetic reconnection and plasma turbulence. Our algorithmic and technological innovations include GPU-optimized kernels, particle control, and physics-aware data compression using Gaussian Mixture Models. With simulation domains spanning 100-1,000 ion skin depths, we reach the global scale of small-to-medium planetary magnetospheres, such as those of Mercury and Ganymede, which supports fully kinetic treatment of global-scale dynamics in systems previously out of reach for fully kinetic PIC codes.
Beyond Text: Probing K-12 Educators' Perspectives and Ideas for Learning Opportunities Leveraging Multimodal Large Language Models
Venue: arXiv cs.HC 2025
Year: 2025

Multimodal Large Language Models (MLLMs) are beginning to empower new user experiences that can flexibly generate content from a range of inputs, including images, text, speech, and video. These capabilities have the potential to enrich learning by enabling users to capture and interact with information using a variety of modalities, but little is known about how educators envision how MLLMs might shape the future of learning experiences, what challenges diverse teachers encounter when interpreting how these models work, and what practical needs should be considered for successful implementation in educational contexts. We investigated educator perspectives through formative workshops with 12 K-12 educators, where participants brainstormed learning opportunities, discussed practical concerns for effective use, and prototyped their own MLLM-powered learning applications using Claude 3.5 and its Artifacts feature for previewing code-based output. We use case studies to illustrate two contrasting end-user approaches (teacher-and student-driven), and share insights about opportunities and concerns expressed by our participants, ending with implications for leveraging MLLMs for future learning experiences.
Vocalize: Lead Acquisition and User Engagement through Gamified Voice Competitions
Venue: arXiv cs.HC 2025
Year: 2025

This paper explores the prospect of creating engaging user experiences and collecting leads through an interactive and gamified platform. We introduce Vocalize, an end-to-end system for increasing user engagement and lead acquisition through gamified voice competitions. Using audio processing techniques and LLMs, we create engaging and interactive experiences that have the potential to reach a wide audience, foster brand recognition, and increase customer loyalty. We describe the system from a technical standpoint and report results from launching Vocalize at 4 different live events. Our user study shows that Vocalize is capable of generating significant user engagement, which shows potential for gamified audio campaigns in marketing and similar verticals.
How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation
Venue: arXiv cs.AI 2025
Year: 2025

Chain-of-Thought (CoT) prompting significantly enhances model reasoning, yet its internal mechanisms remain poorly understood. We analyze CoT's operational principles by reversely tracing information flow across decoding, projection, and activation phases. Our quantitative analysis suggests that CoT may serve as a decoding space pruner, leveraging answer templates to guide output generation, with higher template adherence strongly correlating with improved performance. Furthermore, we surprisingly find that CoT modulates neuron engagement in a task-dependent manner: reducing neuron activation in open-domain tasks, yet increasing it in closed-domain scenarios. These findings offer a novel mechanistic interpretability framework and critical insights for enabling targeted CoT interventions to design more efficient and robust prompts. We released our code and data at https://anonymous.4open.science/r/cot-D247.
KASportsFormer: Kinematic Anatomy Enhanced Transformer for 3D Human Pose Estimation on Short Sports Scene Video
Venue: arXiv cs.CV 2025
Year: 2025

Recent transformer based approaches have demonstrated impressive performance in solving real-world 3D human pose estimation problems. Albeit these approaches achieve fruitful results on benchmark datasets, they tend to fall short of sports scenarios where human movements are more complicated than daily life actions, as being hindered by motion blur, occlusions, and domain shifts. Moreover, due to the fact that critical motions in a sports game often finish in moments of time (e.g., shooting), the ability to focus on momentary actions is becoming a crucial factor in sports analysis, where current methods appear to struggle with instantaneous scenarios. To overcome these limitations, we introduce KASportsFormer, a novel transformer based 3D pose estimation framework for sports that incorporates a kinematic anatomy-informed feature representation and integration module. In which the inherent kinematic motion information is extracted with the Bone Extractor (BoneExt) and Limb Fuser (LimbFus) modules and encoded in a multimodal manner. This improved the capability of comprehending sports poses in short videos. We evaluate our method through two representative sports scene datasets: SportsPose and WorldPose. Experimental results show that our proposed method achieves state-of-the-art results with MPJPE errors of 58.0mm and 34.3mm, respectively. Our code and models are available at: https://github.com/jw0r1n/KASportsFormer
Understanding Bias in Perceiving Dimensionality Reduction Projections
Venue: arXiv cs.HC 2025
Year: 2025

Selecting the dimensionality reduction technique that faithfully represents the structure is essential for reliable visual communication and analytics. In reality, however, practitioners favor projections for other attractions, such as aesthetics and visual saliency, over the projection's structural faithfulness, a bias we define as visual interestingness. In this research, we conduct a user study that (1) verifies the existence of such bias and (2) explains why the bias exists. Our study suggests that visual interestingness biases practitioners' preferences when selecting projections for analysis, and this bias intensifies with color-encoded labels and shorter exposure time. Based on our findings, we discuss strategies to mitigate bias in perceiving and interpreting DR projections.
Client--Library Compatibility Testing with API Interaction Snapshots
Venue: arXiv cs.SE 2025
Year: 2025

Modern software development heavily relies on third-party libraries to speed up development and enhance quality. As libraries evolve, they may break the tacit contract established with their clients by introducing behavioral breaking changes (BBCs) that alter run-time behavior and silently break client applications without being detected at compile time. Traditional regression tests on the client side often fail to detect such BBCs, either due to limited library coverage or weak assertions that do not sufficiently exercise the library's expected behavior. To address this issue, we propose a novel approach to client--library compatibility testing that leverages existing client tests in a novel way. Instead of relying on developer-written assertions, we propose recording the actual interactions at the API boundary during the execution of client tests (protocol, input and output values, exceptions, etc.). These sequences of API interactions are stored as snapshots which capture the exact contract expected by a client at a specific point in time. As the library evolves, we compare the original and new snapshots to identify perturbations in the contract, flag potential BBCs, and notify clients. We implement this technique in our prototype tool Gilesi, a Java framework that automatically instruments library APIs, records snapshots, and compares them. Through a preliminary case study on several client--library pairs with artificially seeded BBCs, we show that Gilesi reliably detects BBCs missed by client test suites.
DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking
Venue: arXiv cs.CV 2025
Year: 2025

The advent of Vision-Language Models (VLMs) has significantly advanced end-to-end autonomous driving, demonstrating powerful reasoning abilities for high-level behavior planning tasks. However, existing methods are often constrained by a passive perception paradigm, relying solely on text-based reasoning. This passivity restricts the model's capacity to actively seek crucial visual evidence when faced with uncertainty. To address this, we introduce DriveAgent-R1, the first autonomous driving agent capable of active perception for planning. In complex scenarios, DriveAgent-R1 proactively invokes tools to perform visual reasoning, firmly grounding its decisions in visual evidence, thereby enhancing both interpretability and reliability. Furthermore, we propose a hybrid thinking framework, inspired by human driver cognitive patterns, allowing the agent to adaptively switch between efficient text-only reasoning and robust tool-augmented visual reasoning based on scene complexity. This capability is cultivated through a three-stage progressive training strategy, featuring a core Cascaded Reinforcement Learning (Cascaded RL) phase. Extensive experiments on the Drive-Internal dataset, which is rich in long-tail scenarios, and the public nuScenes dataset show that, with only 3B parameters, DriveAgent-R1 achieves competitive performance comparable to top closed model systems such as GPT-5 and to human driving proficiency while remaining deployment-friendly, offering a proven path toward building more intelligent autonomous driving systems.
JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment
Venue: arXiv cs.SD 2025
Year: 2025

Diffusion and flow-matching models have revolutionized automatic text-to-audio generation in recent times. These models are increasingly capable of generating high quality and faithful audio outputs capturing to speech and acoustic events. However, there is still much room for improvement in creative audio generation that primarily involves music and songs. Recent open lyrics-to-song models, such as, DiffRhythm, ACE-Step, and LeVo, have set an acceptable standard in automatic song generation for recreational use. However, these models lack fine-grained word-level controllability often desired by musicians in their workflows. To the best of our knowledge, our flow-matching-based JAM is the first effort toward endowing word-level timing and duration control in song generation, allowing fine-grained vocal control. To enhance the quality of generated songs to better align with human preferences, we implement aesthetic alignment through Direct Preference Optimization, which iteratively refines the model using a synthetic dataset, eliminating the need or manual data annotations. Furthermore, we aim to standardize the evaluation of such lyrics-to-song models through our public evaluation dataset JAME. We show that JAM outperforms the existing models in terms of the music-specific attributes.
$A^2R^2$: Advancing Img2LaTeX Conversion via Visual Reasoning with Attention-Guided Refinement
Venue: arXiv cs.CV 2025
Year: 2025

Img2LaTeX is a practically important task that involves translating mathematical expressions and structured visual content from images into LaTeX code. In recent years, vision-language models (VLMs) have achieved remarkable progress across a range of visual understanding tasks, largely due to their strong generalization capabilities. However, despite initial efforts to apply VLMs to the Img2LaTeX task, their performance remains suboptimal. Empirical evidence shows that VLMs can be challenged by fine-grained visual elements, such as subscripts and superscripts in mathematical expressions, which results in inaccurate LaTeX generation. To address this challenge, we propose $A^2R^2$: Advancing Img2LaTeX Conversion via Visual Reasoning with Attention-Guided Refinement, a framework that effectively integrates attention localization and iterative refinement within a visual reasoning framework, enabling VLMs to perform self-correction and progressively improve LaTeX generation quality. For effective evaluation, we introduce a new dataset, Img2LaTex-Hard-1K, consisting of 1,100 carefully curated and challenging examples designed to rigorously evaluate the capabilities of VLMs within this task domain. Extensive experimental results demonstrate that: (1) $A^2R^2$ significantly improves model performance across various evaluation metrics spanning both textual and visual levels; (2) Increasing the number of inference rounds yields notable performance gains, underscoring the potential of $A^2R^2$ in test-time scaling scenarios; (3) Ablation studies and further evaluations confirm the effectiveness of our approach and the synergy of its core components during inference.
Modeling User Behavior from Adaptive Surveys with Supplemental Context
Venue: arXiv cs.LG 2025
Year: 2025

Modeling user behavior is critical across many industries where understanding preferences, intent, or decisions informs personalization, targeting, and strategic outcomes. Surveys have long served as a classical mechanism for collecting such behavioral data due to their interpretability, structure, and ease of deployment. However, surveys alone are inherently limited by user fatigue, incomplete responses, and practical constraints on their length making them insufficient for capturing user behavior. In this work, we present LANTERN (Late-Attentive Network for Enriched Response Modeling), a modular architecture for modeling user behavior by fusing adaptive survey responses with supplemental contextual signals. We demonstrate the architectural value of maintaining survey primacy through selective gating, residual connections and late fusion via cross-attention, treating survey data as the primary signal while incorporating external modalities only when relevant. LANTERN outperforms strong survey-only baselines in multi-label prediction of survey responses. We further investigate threshold sensitivity and the benefits of selective modality reliance through ablation and rare/frequent attribute analysis. LANTERN's modularity supports scalable integration of new encoders and evolving datasets. This work provides a practical and extensible blueprint for behavior modeling in survey-centric applications.
RIS-LAD: A Benchmark and Model for Referring Low-Altitude Drone Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Referring Image Segmentation (RIS), which aims to segment specific objects based on natural language descriptions, plays an essential role in vision-language understanding. Despite its progress in remote sensing applications, RIS in Low-Altitude Drone (LAD) scenarios remains underexplored. Existing datasets and methods are typically designed for high-altitude and static-view imagery. They struggle to handle the unique characteristics of LAD views, such as diverse viewpoints and high object density. To fill this gap, we present RIS-LAD, the first fine-grained RIS benchmark tailored for LAD scenarios. This dataset comprises 13,871 carefully annotated image-text-mask triplets collected from realistic drone footage, with a focus on small, cluttered, and multi-viewpoint scenes. It highlights new challenges absent in previous benchmarks, such as category drift caused by tiny objects and object drift under crowded same-class objects. To tackle these issues, we propose the Semantic-Aware Adaptive Reasoning Network (SAARN). Rather than uniformly injecting all linguistic features, SAARN decomposes and routes semantic information to different stages of the network. Specifically, the Category-Dominated Linguistic Enhancement (CDLE) aligns visual features with object categories during early encoding, while the Adaptive Reasoning Fusion Module (ARFM) dynamically selects semantic cues across scales to improve reasoning in complex scenes. The experimental evaluation reveals that RIS-LAD presents substantial challenges to state-of-the-art RIS algorithms, and also demonstrates the effectiveness of our proposed model in addressing these challenges. The dataset and code will be publicly released soon at: https://github.com/AHideoKuzeA/RIS-LAD/.
GPT-IMAGE-EDIT-1.5M: A Million-Scale, GPT-Generated Image Dataset
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in large multimodal models like GPT-4o have set a new standard for high-fidelity, instruction-guided image editing. However, the proprietary nature of these models and their training data creates a significant barrier for open-source research. To bridge this gap, we introduce GPT-IMAGE-EDIT-1.5M, a publicly available, large-scale image-editing corpus containing more than 1.5 million high-quality triplets (instruction, source image, edited image). We systematically construct this dataset by leveraging the versatile capabilities of GPT-4o to unify and refine three popular image-editing datasets: OmniEdit, HQ-Edit, and UltraEdit. Specifically, our methodology involves 1) regenerating output images to enhance visual quality and instruction alignment, and 2) selectively rewriting prompts to improve semantic clarity. To validate the efficacy of our dataset, we fine-tune advanced open-source models on GPT-IMAGE-EDIT-1.5M. The empirical results are exciting, e.g., the fine-tuned FluxKontext achieves highly competitive performance across a comprehensive suite of benchmarks, including 7.24 on GEdit-EN, 3.80 on ImgEdit-Full, and 8.78 on Complex-Edit, showing stronger instruction following and higher perceptual quality while maintaining identity. These scores markedly exceed all previously published open-source methods and substantially narrow the gap to leading proprietary models. We hope the full release of GPT-IMAGE-EDIT-1.5M can help to catalyze further open research in instruction-guided image editing.
Development and analysis of a secured VoIP system for surveillance activities
Venue: arXiv cs.CR 2025
Year: 2025

Since the 1990s, the telephone has been the primary mode of communication. However, Voice over Internet Protocol (VoIP), which is a highly straightforward and affordable form of data transfer, is now becoming an important part of daily communication. VoIP is the technology that makes it possible to send speech and multimedia data packets across either a public or private IP network. However, a cyberattack known as a man-in-the-middle attack poses a serious concern in transferring data through any network. Therefore, the authors have designed a system that sends voice over the internet within the range of a router using encrypted data transfer. An embedded system comprising an electret microphone, Embedded C, Particle Photon microcontroller, and Internet of Things (IoT) technology is developed. Due to its compact size, this type of device may be incorporated into automobiles, surveillance systems, or covert listening tools. The VoIP system gathers sound signals using the MAX9814 microphone, while the Particle Photon microcontroller securely transmits the data. Devices with access can download data from the VoIP systems Transmission Control Protocol (TCP) server. The accessed device stores the audio locally and uploads the corresponding data to Google Drive. This VoIP system provides a secure method of communication while conserving the integrity of the original signal.
Flow Matching Policy Gradients
Venue: arXiv cs.LG 2025
Year: 2025

Flow-based generative models, including diffusion models, excel at modeling continuous distributions in high-dimensional spaces. In this work, we introduce Flow Policy Optimization (FPO), a simple on-policy reinforcement learning algorithm that brings flow matching into the policy gradient framework. FPO casts policy optimization as maximizing an advantage-weighted ratio computed from the conditional flow matching loss, in a manner compatible with the popular PPO-clip framework. It sidesteps the need for exact likelihood computation while preserving the generative capabilities of flow-based models. Unlike prior approaches for diffusion-based reinforcement learning that bind training to a specific sampling method, FPO is agnostic to the choice of diffusion or flow integration at both training and inference time. We show that FPO can train diffusion-style policies from scratch in a variety of continuous control tasks. We find that flow-based models can capture multimodal action distributions and achieve higher performance than Gaussian policies, particularly in under-conditioned settings.
Can LLMs Reason About Trust?: A Pilot Study
Venue: arXiv cs.HC 2025
Year: 2025

In human society, trust is an essential component of social attitude that helps build and maintain long-term, healthy relationships which creates a strong foundation for cooperation, enabling individuals to work together effectively and achieve shared goals. As many human interactions occur through electronic means such as using mobile apps, the potential arises for AI systems to assist users in understanding the social state of their relationships. In this paper we investigate the ability of Large Language Models (LLMs) to reason about trust between two individuals in an environment which requires fostering trust relationships. We also assess whether LLMs are capable of inducing trust by role-playing one party in a trust based interaction and planning actions which can instil trust.