OpenRaiser/Intern-Atlas huggingface dataset:
MinJointTracker: Real-time inertial kinematic chain tracking with joint position estimation and minimal state size
Venue: arXiv cs.RO 2025
Year: 2025

Inertial motion capture is a promising approach for capturing motion outside the laboratory. However, as one major drawback, most of the current methods require different quantities to be calibrated or computed offline as part of the setup process, such as segment lengths, relative orientations between inertial measurement units (IMUs) and segment coordinate frames (IMU-to-segment calibrations) or the joint positions in the IMU frames. This renders the setup process inconvenient. This work contributes to real-time capable calibration-free inertial tracking of a kinematic chain, i.e. simultaneous recursive Bayesian estimation of global IMU angular kinematics and joint positions in the IMU frames, with a minimal state size. Experimental results on simulated IMU data from a three-link kinematic chain (manipulator study) as well as re-simulated IMU data from healthy humans walking (lower body study) show that the calibration-free and lightweight algorithm provides not only drift-free relative but also drift-free absolute orientation estimates with a global heading reference for only one IMU as well as robust and fast convergence of joint position estimates in the different movement scenarios.
Scaffolding Flexible Ideation Workflows with AI in Creative Problem-Solving
Venue: arXiv cs.HC 2025
Year: 2025

Divergent thinking in the ideation stage of creative problem-solving demands that individuals explore a broad design space. Yet this exploration rarely follows a neat, linear sequence; problem-solvers constantly shift among searching, creating, and evaluating ideas. Existing interfaces either impose rigid, step-by-step workflows or permit unguided free-form exploration. To strike a balance between flexibility and guidance for augmenting people's efficiency and creativity, we introduce a human-AI collaborative workflow that supports a fluid ideation process. The system surfaces three opt-in aids: (1) high-level schemas to uncover alternative ideas, (2) risk analysis with mitigation suggestions, and (3) steering system-generated suggestions. Users can invoke these supports at any moment, allowing seamless back-and-forth movement among design actions to maintain creative momentum.
From Legacy Fortran to Portable Kokkos: An Autonomous Agentic AI Workflow
Venue: arXiv cs.SE 2025
Year: 2025

Scientific applications continue to rely on legacy Fortran codebases originally developed for homogeneous, CPU-based systems. As High-Performance Computing (HPC) shifts toward heterogeneous GPU-accelerated architectures, many accelerators lack native Fortran bindings, creating an urgent need to modernize legacy codes for portability. Frameworks like Kokkos provide performance portability and a single-source C++ abstraction, but manual Fortran-to-Kokkos porting demands significant expertise and time. Large language models (LLMs) have shown promise in source-to-source code generation, yet their use in fully autonomous workflows for translating and optimizing parallel code remains largely unexplored, especially for performance portability across diverse hardware. This paper presents an agentic AI workflow where specialized LLM "agents" collaborate to translate, validate, compile, run, test, debug, and optimize Fortran kernels into portable Kokkos C++ programs. Results show the pipeline modernizes a range of benchmark kernels, producing performance-portable Kokkos codes across hardware partitions. Paid OpenAI models such as GPT-5 and o4-mini-high executed the workflow for only a few U.S. dollars, generating optimized codes that surpassed Fortran baselines, whereas open-source models like Llama4-Maverick often failed to yield functional codes. This work demonstrates the feasibility of agentic AI for Fortran-to-Kokkos transformation and offers a pathway for autonomously modernizing legacy scientific applications to run portably and efficiently on diverse supercomputers. It further highlights the potential of LLM-driven agentic systems to perform structured, domain-specific reasoning tasks in scientific and systems-oriented applications.
PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization
Venue: arXiv cs.MA 2025
Year: 2025

The rapid advancement of generative AI has democratized access to powerful tools such as Text-to-Image models. However, to generate high-quality images, users must still craft detailed prompts specifying scene, style, and context-often through multiple rounds of refinement. We propose PromptSculptor, a novel multi-agent framework that automates this iterative prompt optimization process. Our system decomposes the task into four specialized agents that work collaboratively to transform a short, vague user prompt into a comprehensive, refined prompt. By leveraging Chain-of-Thought reasoning, our framework effectively infers hidden context and enriches scene and background details. To iteratively refine the prompt, a self-evaluation agent aligns the modified prompt with the original input, while a feedback-tuning agent incorporates user feedback for further refinement. Experimental results demonstrate that PromptSculptor significantly enhances output quality and reduces the number of iterations needed for user satisfaction. Moreover, its model-agnostic design allows seamless integration with various T2I models, paving the way for industrial applications.
Empowering Clinical Trial Design through AI: A Randomized Evaluation of PowerGPT
Venue: arXiv cs.AI 2025
Year: 2025

Sample size calculations for power analysis are critical for clinical research and trial design, yet their complexity and reliance on statistical expertise create barriers for many researchers. We introduce PowerGPT, an AI-powered system integrating large language models (LLMs) with statistical engines to automate test selection and sample size estimation in trial design. In a randomized trial to evaluate its effectiveness, PowerGPT significantly improved task completion rates (99.3% vs. 88.9% for test selection, 99.3% vs. 77.8% for sample size calculation) and accuracy (94.1% vs. 55.4% in sample size estimation, p < 0.001), while reducing average completion time (4.0 vs. 9.3 minutes, p < 0.001). These gains were consistent across various statistical tests and benefited both statisticians and non-statisticians as well as bridging expertise gaps. Already under deployment across multiple institutions, PowerGPT represents a scalable AI-driven approach that enhances accessibility, efficiency, and accuracy in statistical power analysis for clinical research.
Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures
Venue: arXiv cs.LG 2025
Year: 2025

We propose a novel neural network architecture, called Non-Trainable Modification (NTM), for computing Nash equilibria in stochastic differential games (SDGs) on graphs. These games model a broad class of graph-structured multi-agent systems arising in finance, robotics, energy, and social dynamics, where agents interact locally under uncertainty. The NTM architecture imposes a graph-guided sparsification on feedforward neural networks, embedding fixed, non-trainable components aligned with the underlying graph topology. This design enhances interpretability and stability, while significantly reducing the number of trainable parameters in large-scale, sparse settings. We theoretically establish a universal approximation property for NTM in static games on graphs and numerically validate its expressivity and robustness through supervised learning tasks. Building on this foundation, we incorporate NTM into two state-of-the-art game solvers, Direct Parameterization and Deep BSDE, yielding their sparse variants (NTM-DP and NTM-DBSDE). Numerical experiments on three SDGs across various graph structures demonstrate that NTM-based methods achieve performance comparable to their fully trainable counterparts, while offering improved computational efficiency.
Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Coding
Venue: arXiv cs.SE 2025
Year: 2025

Vibe coding, a term coined by Andrej Karpathy in February 2025, has quickly become a compelling and controversial natural language programming paradigm in AI-assisted software development. Centered on iterative co-design with an AI assistant, vibe coding emphasizes flow and experimentation over strict upfront specification. While initial studies have begun to explore this paradigm, most focus on analyzing code artifacts or proposing theories with limited empirical backing. There remains a need for a grounded understanding of vibe coding as it is perceived and experienced by developers. We present the first systematic qualitative investigation of vibe coding perceptions and practice. Drawing on over 190,000 words from semi-structured interviews, Reddit threads, and LinkedIn posts, we characterize what vibe coding is, why and how developers use it, where it breaks down, and which emerging practices aim to support it. We propose a qualitatively grounded theory of vibe coding centered on conversational interaction with AI, co-creation, and developer flow and joy. We find that AI trust regulates movement along a continuum from delegation to co-creation and supports the developer experience by sustaining flow. We surface recurring pain points and risks in areas including specification, reliability, debugging, latency, code review burden, and collaboration. We also present best practices that have been discovered and shared to mitigate these challenges. We conclude with implications for the future of AI dev tools and directions for researchers investigating vibe coding.
Towards Closing the Performance Gap for Cryptographic Kernels Between CPUs and Specialized Hardware
Venue: arXiv cs.CR 2025
Year: 2025

Specialized hardware like application-specific integrated circuits (ASICs) remains the primary accelerator type for cryptographic kernels based on large integer arithmetic. Prior work has shown that commodity and server-class GPUs can achieve near-ASIC performance for these workloads. However, achieving comparable performance on CPUs remains an open challenge. This work investigates the following question: How can we narrow the performance gap between CPUs and specialized hardware for key cryptographic kernels like basic linear algebra subprograms (BLAS) operations and the number theoretic transform (NTT)? To this end, we develop an optimized scalar implementation of these kernels for x86 CPUs at the per-core level. We utilize SIMD instructions (specifically AVX2 and AVX-512) to further improve performance, achieving an average speedup of 38 times and 62 times over state-of-the-art CPU baselines for NTTs and BLAS operations, respectively. To narrow the gap further, we propose a small AVX-512 extension, dubbed multi-word extension (MQX), which delivers substantial speedup with only three new instructions and minimal proposed hardware modifications. MQX cuts the slowdown relative to ASICs to as low as 35 times on a single CPU core. Finally, we perform a roofline analysis to evaluate the peak performance achievable with MQX when scaled across an entire multi-core CPU. Our results show that, with MQX, top-tier server-grade CPUs can approach the performance of state-of-the-art ASICs for cryptographic workloads.
Artist-Created Mesh Generation from Raw Observation
Venue: arXiv cs.CV 2025
Year: 2025

We present an end-to-end framework for generating artist-style meshes from noisy or incomplete point clouds, such as those captured by real-world sensors like LiDAR or mobile RGB-D cameras. Artist-created meshes are crucial for commercial graphics pipelines due to their compatibility with animation and texturing tools and their efficiency in rendering. However, existing approaches often assume clean, complete inputs or rely on complex multi-stage pipelines, limiting their applicability in real-world scenarios. To address this, we propose an end-to-end method that refines the input point cloud and directly produces high-quality, artist-style meshes. At the core of our approach is a novel reformulation of 3D point cloud refinement as a 2D inpainting task, enabling the use of powerful generative models. Preliminary results on the ShapeNet dataset demonstrate the promise of our framework in producing clean, complete meshes.
Axis-Aligned 3D Stalk Diameter Estimation from RGB-D Imagery
Venue: arXiv cs.CV 2025
Year: 2025

Accurate, high-throughput phenotyping is a critical component of modern crop breeding programs, especially for improving traits such as mechanical stability, biomass production, and disease resistance. Stalk diameter is a key structural trait, but traditional measurement methods are labor-intensive, error-prone, and unsuitable for scalable phenotyping. In this paper, we present a geometry-aware computer vision pipeline for estimating stalk diameter from RGB-D imagery. Our method integrates deep learning-based instance segmentation, 3D point cloud reconstruction, and axis-aligned slicing via Principal Component Analysis (PCA) to perform robust diameter estimation. By mitigating the effects of curvature, occlusion, and image noise, this approach offers a scalable and reliable solution to support high-throughput phenotyping in breeding and agronomic research.
A comparison of pipelines for the translation of a low resource language based on transformers
Venue: arXiv cs.CL 2025
Year: 2025

This work compares three pipelines for training transformer-based neural networks to produce machine translators for Bambara, a Mandè language spoken in Africa by about 14,188,850 people. The first pipeline trains a simple transformer to translate sentences from French into Bambara. The second fine-tunes LLaMA3 (3B-8B) instructor models using decoder-only architectures for French-to-Bambara translation. Models from the first two pipelines were trained with different hyperparameter combinations to improve BLEU and chrF scores, evaluated on both test sentences and official Bambara benchmarks. The third pipeline uses language distillation with a student-teacher dual neural network to integrate Bambara into a pre-trained LaBSE model, which provides language-agnostic embeddings. A BERT extension is then applied to LaBSE to generate translations. All pipelines were tested on Dokotoro (medical) and Bayelemagaba (mixed domains). Results show that the first pipeline, although simpler, achieves the best translation accuracy (10% BLEU, 21% chrF on Bayelemagaba), consistent with low-resource translation results. On the Yiri dataset, created for this work, it achieves 33.81% BLEU and 41% chrF. Instructor-based models perform better on single datasets than on aggregated collections, suggesting they capture dataset-specific patterns more effectively.
A Dimensionality-Reduced XAI Framework for Roundabout Crash Severity Insights
Venue: arXiv cs.AI 2025
Year: 2025

Roundabouts reduce severe crashes, yet risk patterns vary by conditions. This study analyzes 2017-2021 Ohio roundabout crashes using a two-step, explainable workflow. Cluster Correspondence Analysis (CCA) identifies co-occurring factors and yields four crash patterns. A tree-based severity model is then interpreted with SHAP to quantify drivers of injury within and across patterns. Results show higher severity when darkness, wet surfaces, and higher posted speeds coincide with fixed-object or angle events, and lower severity in clear, low-speed settings. Pattern-specific explanations highlight mechanisms at entries (fail-to-yield, gap acceptance), within multi-lane circulation (improper maneuvers), and during slow-downs (rear-end). The workflow links pattern discovery with case-level explanations, supporting site screening, countermeasure selection, and audit-ready reporting. The contribution to Information Systems is a practical template for usable XAI in public safety analytics.
iCD: A Implicit Clustering Distillation Mathod for Structural Information Mining
Venue: arXiv cs.LG 2025
Year: 2025

Logit Knowledge Distillation has gained substantial research interest in recent years due to its simplicity and lack of requirement for intermediate feature alignment; however, it suffers from limited interpretability in its decision-making process. To address this, we propose implicit Clustering Distillation (iCD): a simple and effective method that mines and transfers interpretable structural knowledge from logits, without requiring ground-truth labels or feature-space alignment. iCD leverages Gram matrices over decoupled local logit representations to enable student models to learn latent semantic structural patterns. Extensive experiments on benchmark datasets demonstrate the effectiveness of iCD across diverse teacher-student architectures, with particularly strong performance in fine-grained classification tasks -- achieving a peak improvement of +5.08% over the baseline. The code is available at: https://github.com/maomaochongaa/iCD.
Explicit Multimodal Graph Modeling for Human-Object Interaction Detection
Venue: arXiv cs.CV 2025
Year: 2025

Transformer-based methods have recently become the prevailing approach for Human-Object Interaction (HOI) detection. However, the Transformer architecture does not explicitly model the relational structures inherent in HOI detection, which impedes the recognition of interactions. In contrast, Graph Neural Networks (GNNs) are inherently better suited for this task, as they explicitly model the relationships between human-object pairs. Therefore, in this paper, we propose \textbf{M}ultimodal \textbf{G}raph \textbf{N}etwork \textbf{M}odeling (MGNM) that leverages GNN-based relational structures to enhance HOI detection. Specifically, we design a multimodal graph network framework that explicitly models the HOI task in a four-stage graph structure. Furthermore, we introduce a multi-level feature interaction mechanism within our graph network. This mechanism leverages multi-level visual and language features to enhance information propagation across human-object pairs. Consequently, our proposed MGNM achieves state-of-the-art (SOTA) performance on two widely used benchmarks: HICO-DET and V-COCO. Moreover, when integrated with a more advanced object detector, our method demonstrates a significant performance gain and maintains an effective balance between rare and non-rare classes.
Redefining CX with Agentic AI: Minerva CQ Case Study
Venue: arXiv cs.AI 2025
Year: 2025

Despite advances in AI for contact centers, customer experience (CX) continues to suffer from high average handling time (AHT), low first-call resolution, and poor customer satisfaction (CSAT). A key driver is the cognitive load on agents, who must navigate fragmented systems, troubleshoot manually, and frequently place customers on hold. Existing AI-powered agent-assist tools are often reactive driven by static rules, simple prompting, or retrieval-augmented generation (RAG) without deeper contextual reasoning. We introduce Agentic AI goal-driven, autonomous, tool-using systems that proactively support agents in real time. Unlike conventional approaches, Agentic AI identifies customer intent, triggers modular workflows, maintains evolving context, and adapts dynamically to conversation state. This paper presents a case study of Minerva CQ, a real-time Agent Assist product deployed in voice-based customer support. Minerva CQ integrates real-time transcription, intent and sentiment detection, entity recognition, contextual retrieval, dynamic customer profiling, and partial conversational summaries enabling proactive workflows and continuous context-building. Deployed in live production, Minerva CQ acts as an AI co-pilot, delivering measurable improvements in agent efficiency and customer experience across multiple deployments.
MAGIC-Enhanced Keyword Prompting for Zero-Shot Audio Captioning with CLIP Models
Venue: arXiv cs.CL 2025
Year: 2025

Automated Audio Captioning (AAC) generates captions for audio clips but faces challenges due to limited datasets compared to image captioning. To overcome this, we propose the zero-shot AAC system that leverages pre-trained models, eliminating the need for extensive training. Our approach uses a pre-trained audio CLIP model to extract auditory features and generate a structured prompt, which guides a Large Language Model (LLM) in caption generation. Unlike traditional greedy decoding, our method refines token selection through the audio CLIP model, ensuring alignment with the audio content. Experimental results demonstrate a 35% improvement in NLG mean score (from 4.7 to 7.3) using MAGIC search with the WavCaps model. The performance is heavily influenced by the audio-text matching model and keyword selection, with optimal results achieved using a single keyword prompt, and a 50% performance drop when no keyword list is used.
Match Chat: Real Time Generative AI and Generative Computing for Tennis
Venue: arXiv cs.AI 2025
Year: 2025

We present Match Chat, a real-time, agent-driven assistant designed to enhance the tennis fan experience by delivering instant, accurate responses to match-related queries. Match Chat integrates Generative Artificial Intelligence (GenAI) with Generative Computing (GenComp) techniques to synthesize key insights during live tennis singles matches. The system debuted at the 2025 Wimbledon Championships and the 2025 US Open, where it provided about 1 million users with seamless access to streaming and static data through natural language queries. The architecture is grounded in an Agent-Oriented Architecture (AOA) combining rule engines, predictive models, and agents to pre-process and optimize user queries before passing them to GenAI components. The Match Chat system had an answer accuracy of 92.83% with an average response time of 6.25 seconds under loads of up to 120 requests per second (RPS). Over 96.08% of all queries were guided using interactive prompt design, contributing to a user experience that prioritized clarity, responsiveness, and minimal effort. The system was designed to mask architectural complexity, offering a frictionless and intuitive interface that required no onboarding or technical familiarity. Across both Grand Slam deployments, Match Chat maintained 100% uptime and supported nearly 1 million unique users, underscoring the scalability and reliability of the platform. This work introduces key design patterns for real-time, consumer-facing AI systems that emphasize speed, precision, and usability that highlights a practical path for deploying performant agentic systems in dynamic environments.
A Multimodal Foundation Model to Enhance Generalizability and Data Efficiency for Pan-cancer Prognosis Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Multimodal data provides heterogeneous information for a holistic understanding of the tumor microenvironment. However, existing AI models often struggle to harness the rich information within multimodal data and extract poorly generalizable representations. Here we present MICE (Multimodal data Integration via Collaborative Experts), a multimodal foundation model that effectively integrates pathology images, clinical reports, and genomics data for precise pan-cancer prognosis prediction. Instead of conventional multi-expert modules, MICE employs multiple functionally diverse experts to comprehensively capture both cross-cancer and cancer-specific insights. Leveraging data from 11,799 patients across 30 cancer types, we enhanced MICE's generalizability by coupling contrastive and supervised learning. MICE outperformed both unimodal and state-of-the-art multi-expert-based multimodal models, demonstrating substantial improvements in C-index ranging from 3.8% to 11.2% on internal cohorts and 5.8% to 8.8% on independent cohorts, respectively. Moreover, it exhibited remarkable data efficiency across diverse clinical scenarios. With its enhanced generalizability and data efficiency, MICE establishes an effective and scalable foundation for pan-cancer prognosis prediction, holding strong potential to personalize tailored therapies and improve treatment outcomes.