OpenRaiser/Intern-Atlas huggingface dataset:
SPHaptics: A Real-Time Bidirectional Haptic Interaction Framework for Coupled Rigid-Soft Body and Lagrangian Fluid Simulation in Virtual Environments
Venue: arXiv cs.GR 2025
Year: 2025

Haptic feedback enhances immersion in virtual environments by allowing users to physically interact with simulated objects. Supporting accurate force responses in multiphysics systems is challenging because physically based simulation of fluid, rigid, and deformable materials is computationally demanding, especially when interaction must occur in real time. We present a unified framework for real-time, bidirectional haptic interaction with rigid bodies, deformable objects, and Lagrangian fluids in virtual reality (VR). Our approach integrates Smoothed Particle Hydrodynamics (SPH) with two-way force coupling and feedback smoothing to maintain stability and produce physically meaningful tactile responses. This enables users to manipulate objects immersed in fluid and feel reaction forces consistent with fluid-structure behavior. We demonstrate the capabilities of our framework through interactive VR scenarios involving fluid stirring, soft tissue manipulation, and rigid-body interaction. The proposed system advances haptic-enabled multiphysics simulation by unifying fluid, soft-body, and rigid-body dynamics into a single platform suitable for immersive educational applications.
RB-FT: Rationale-Bootstrapped Fine-Tuning for Video Classification
Venue: arXiv cs.CV 2025
Year: 2025

Vision Language Models (VLMs) are becoming increasingly integral to multimedia understanding; however, they often struggle with domain-specific video classification tasks, particularly in cases with limited data. This stems from a critical \textit{rationale gap}, where sparse domain data is insufficient to bridge the semantic distance between complex spatio-temporal content and abstract classification labels. We propose a two-stage self-improvement paradigm to bridge this gap without new annotations. First, we prompt the VLMs to generate detailed textual rationales for each video, compelling them to articulate the domain-specific logic. The VLM is then fine-tuned on these self-generated rationales, utilizing this intermediate supervision to align its representations with the nuances of the target domain. Second, conventional supervised fine-tuning (SFT) is performed on the task labels, achieving markedly higher effectiveness as a result of the model's pre-acquired domain reasoning. Extensive experiments on diverse datasets demonstrate that our method significantly outperforms direct SFT, validating self-generated rationale as an effective, annotation-efficient paradigm for adapting VLMs to domain-specific video analysis.
Automated Interpretable 2D Video Extraction from 3D Echocardiography
Venue: arXiv cs.CV 2025
Year: 2025

Although the heart has complex three-dimensional (3D) anatomy, conventional medical imaging with cardiac ultrasound relies on a series of 2D videos showing individual cardiac structures. 3D echocardiography is a developing modality that now offers adequate image quality for clinical use, with potential to streamline acquisition and improve assessment of off-axis features. We propose an automated method to select standard 2D views from 3D cardiac ultrasound volumes, allowing physicians to interpret the data in their usual format while benefiting from the speed and usability of 3D scanning. Applying a deep learning view classifier and downstream heuristics based on anatomical landmarks together with heuristics provided by cardiologists, we reconstruct standard echocardiography views. This approach was validated by three cardiologists in blinded evaluation (96\% accuracy in 1,600 videos from 2 hospitals). The downstream 2D videos were also validated in their ability to detect cardiac abnormalities using AI echocardiography models (EchoPrime and PanEcho) as well as ability to generate clinical-grade measurements of cardiac anatomy (EchoNet-Measurement). We demonstrated that the extracted 2D videos preserve spatial calibration and diagnostic features, allowing clinicians to obtain accurate real-world interpretations from 3D volumes. We release the code and a dataset of 29 3D echocardiography videos https://github.com/echonet/3d-echo .
The Role of Consequential and Functional Sound in Human-Robot Interaction: Toward Audio Augmented Reality Interfaces
Venue: arXiv cs.RO 2025
Year: 2025

As robots become increasingly integrated into everyday environments, understanding how they communicate with humans is critical. Sound offers a powerful channel for interaction, encompassing both operational noises and intentionally designed auditory cues. In this study, we examined the effects of consequential and functional sounds on human perception and behavior, including a novel exploration of spatial sound through localization and handover tasks. Results show that consequential sounds of the Kinova Gen3 manipulator did not negatively affect perceptions, spatial localization is highly accurate for lateral cues but declines for frontal cues, and spatial sounds can simultaneously convey task-relevant information while promoting warmth and reducing discomfort. These findings highlight the potential of functional and transformative auditory design to enhance human-robot collaboration and inform future sound-based interaction strategies.
An Enhanced Whale Optimization Algorithm with Log-Normal Distribution for Optimizing Coverage of Wireless Sensor Networks
Venue: arXiv cs.CE 2025
Year: 2025

Wireless Sensor Networks (WSNs) are essential for monitoring and communication in complex environments, where coverage optimization directly affects performance and energy efficiency. However, traditional algorithms such as the Whale Optimization Algorithm (WOA) often suffer from limited exploration and premature convergence. To overcome these issues, this paper proposes an enhanced WOA which is called GLNWOA. GLNWOA integrates a log-normal distribution model into WOA to improve convergence dynamics and search diversity. GLNWOA employs a Good Nodes Set initialization for uniform population distribution, a Leader Cognitive Guidance Mechanism for efficient information sharing, and an Enhanced Spiral Updating Strategy to balance global exploration and local exploitation. Tests on benchmark functions verify its superior convergence accuracy and robustness. In WSN coverage optimization, deploying 25 nodes in a 60 m $\times$ 60 m area achieved a 99.0013\% coverage rate, outperforming AROA, WOA, HHO, ROA, and WOABAT by up to 15.5\%. These results demonstrate that GLNWOA offers fast convergence, high stability, and excellent optimization capability for intelligent network deployment.
KRAL: Knowledge and Reasoning Augmented Learning for LLM-assisted Clinical Antimicrobial Therapy
Venue: arXiv cs.AI 2025
Year: 2025

Clinical antimicrobial therapy requires the dynamic integration of pathogen profiles,host factors, pharmacological properties of antimicrobials,and the severity of infection. This complexity imposes fundamental limitations on the applicability of Large Language Models (LLMs) in high-stakes clinical decision-making including knowledge gaps, data privacy concerns, high deployment costs, and limited reasoning capabilities. To address these challenges, we propose KRAL (Knowledge and Reasoning Augmented Learning), a low-cost, scalable, privacy-preserving paradigm that leverages teacher-model reasoning to automatically distill knowledge and reasoning trajectories via answer-to-question reverse generation, employs heuristic learning for semi-supervised data augmentation (reducing manual annotation requirements by approximately 80%), and utilizes agentic reinforcement learning to jointly enhance medical knowledge and reasoning while optimizing computational and memory efficiency. A hierarchical evaluation employing diverse teacher-model proxies reduces assessment costs, while modular interface design facilitates seamless system updates. Experimental results demonstrate that KRAL significantly outperforms traditional Retrieval-Augmented Generation (RAG) and Supervised Fine-Tuning (SFT) methods. It improves knowledge question-answering capability (Accuracy@1 on the external open-source benchmark MEDQA increased by 1.8% vs. SFT and 3.6% vs. RAG) and reasoning capability (Pass@1 on the external benchmark PUMCH Antimicrobial increased by 27% vs. SFT and 27.2% vs. RAG), achieved at about 20% of SFT's long-term training costs. This establishes KRAL as an effective solution for enhancing local LLMs' clinical diagnostic capabilities, enabling low-cost, high-safety deployment in complex medical decision support.
PushingBots: Collaborative Pushing via Neural Accelerated Combinatorial Hybrid Optimization
Venue: arXiv cs.RO 2025
Year: 2025

Many robots are not equipped with a manipulator and many objects are not suitable for prehensile manipulation (such as large boxes and cylinders). In these cases, pushing is a simple yet effective non-prehensile skill for robots to interact with and further change the environment. Existing work often assumes a set of predefined pushing modes and fixed-shape objects. This work tackles the general problem of controlling a robotic fleet to push collaboratively numerous arbitrary objects to respective destinations, within complex environments of cluttered and movable obstacles. It incorporates several characteristic challenges for multi-robot systems such as online task coordination under large uncertainties of cost and duration, and for contact-rich tasks such as hybrid switching among different contact modes, and under-actuation due to constrained contact forces. The proposed method is based on combinatorial hybrid optimization over dynamic task assignments and hybrid execution via sequences of pushing modes and associated forces. It consists of three main components: (I) the decomposition, ordering and rolling assignment of pushing subtasks to robot subgroups; (II) the keyframe guided hybrid search to optimize the sequence of parameterized pushing modes for each subtask; (III) the hybrid control to execute these modes and transit among them. Last but not least, a diffusion-based accelerator is adopted to predict the keyframes and pushing modes that should be prioritized during hybrid search; and further improve planning efficiency. The framework is complete under mild assumptions. Its efficiency and effectiveness under different numbers of robots and general-shaped objects are validated extensively in simulations and hardware experiments, as well as generalizations to heterogeneous robots, planar assembly and 6D pushing.
CARE: Turning LLMs Into Causal Reasoning Expert
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) have recently demonstrated impressive capabilities across a range of reasoning and generation tasks. However, research studies have shown that LLMs lack the ability to identify causal relationships, a fundamental cornerstone of human intelligence. We first conduct an exploratory investigation of LLMs' behavior when asked to perform a causal-discovery task and find that they mostly rely on the semantic meaning of variable names, ignoring the observation data. This is unsurprising, given that LLMs were never trained to process structural datasets. To first tackle this challenge, we prompt the LLMs with the outputs of established causal discovery algorithms designed for observational datasets. These algorithm outputs effectively serve as the sufficient statistics of the observation data. However, quite surprisingly, we find that prompting the LLMs with these sufficient statistics decreases the LLMs' performance in causal discovery. To address this current limitation, we propose CARE, a framework that enhances LLMs' causal-reasoning ability by teaching them to effectively utilize the outputs of established causal-discovery algorithms through supervised fine-tuning. Experimental results show that a finetuned Qwen2.5-1.5B model produced by CARE significantly outperforms both traditional causal-discovery algorithms and state-of-the-art LLMs with over a thousand times more parameters, demonstrating effective utilization of its own knowledge and the external algorithmic clues.
SpellForger: Prompting Custom Spell Properties In-Game using BERT supervised-trained model
Venue: arXiv cs.AI 2025
Year: 2025

Introduction: The application of Artificial Intelligence in games has evolved significantly, allowing for dynamic content generation. However, its use as a core gameplay co-creation tool remains underexplored. Objective: This paper proposes SpellForger, a game where players create custom spells by writing natural language prompts, aiming to provide a unique experience of personalization and creativity. Methodology: The system uses a supervisedtrained BERT model to interpret player prompts. This model maps textual descriptions to one of many spell prefabs and balances their parameters (damage, cost, effects) to ensure competitive integrity. The game is developed in the Unity Game Engine, and the AI backend is in Python. Expected Results: We expect to deliver a functional prototype that demonstrates the generation of spells in real time, applied to an engaging gameplay loop, where player creativity is central to the experience, validating the use of AI as a direct gameplay mechanic.
Can Online GenAI Discussion Serve as Bellwether for Labor Market Shifts?
Venue: arXiv cs.CY 2025
Year: 2025

The rapid advancement of Large Language Models (LLMs) has generated considerable speculation regarding their transformative potential for labor markets. However, existing approaches to measuring AI exposure in the workforce predominantly rely on concurrent market conditions, offering limited predictive capacity for anticipating future disruptions. This paper presents a predictive study examining whether online discussions about LLMs can function as early indicators of labor market shifts. We employ four distinct analytical approaches to identify the domains and timeframes in which public discourse serves as a leading signal for employment changes, thereby demonstrating its predictive validity for labor market dynamics. Drawing on a comprehensive dataset that integrates the REALM corpus of LLM discussions, LinkedIn job postings, Indeed employment indices, and over 4 million LinkedIn user profiles, we analyze the relationship between discussion intensity across news media and Reddit forums and subsequent variations in job posting volumes, occupational net change ratios, job tenure patterns, unemployment duration, and transitions to GenAI-related roles across thirteen occupational categories. Our findings reveal that discussion intensity predicts employment changes 1-7 months in advance across multiple indicators, including job postings, net hiring rates, tenure patterns, and unemployment duration. These findings suggest that monitoring online discourse can provide actionable intelligence for workers making reskilling decisions and organizations anticipating skill requirements, offering a real-time complement to traditional labor statistics in navigating technological disruption.
A Quantum-Secure and Blockchain-Integrated E-Voting Framework with Identity Validation
Venue: arXiv cs.CR 2025
Year: 2025

The rapid growth of quantum computing poses a threat to the cryptographic foundations of digital systems, requiring the development of secure and scalable electronic voting (evoting) frameworks. We introduce a post-quantum-secure evoting architecture that integrates Falcon lattice-based digital signatures, biometric authentication via MobileNetV3 and AdaFace, and a permissioned blockchain for tamper-proof vote storage. Voter registration involves capturing facial embeddings, which are digitally signed using Falcon and stored on-chain to ensure integrity and non-repudiation. During voting, real-time biometric verification is performed using anti-spoofing techniques and cosine-similarity matching. The system demonstrates low latency and robust spoof detection, monitored through Prometheus and Grafana for real-time auditing. The average classification error rates (ACER) are below 3.5% on the CelebA Spoof dataset and under 8.2% on the Wild Face Anti-Spoofing (WFAS) dataset. Blockchain anchoring incurs minimal gas overhead, approximately 3.3% for registration and 0.15% for voting, supporting system efficiency, auditability, and transparency. The experimental results confirm the system's scalability, efficiency, and resilience under concurrent loads. This approach offers a unified solution to address key challenges in voter authentication, data integrity, and quantum-resilient security for digital systems.
Artificial Intelligence and Accounting Research: A Framework and Agenda
Venue: arXiv cs.AI 2025
Year: 2025

Recent advances in artificial intelligence, particularly generative AI (GenAI) and large language models (LLMs), are fundamentally transforming accounting research, creating both opportunities and competitive threats for scholars. This paper proposes a framework that classifies AI-accounting research along two dimensions: research focus (accounting-centric versus AI-centric) and methodological approach (AI-based versus traditional methods). We apply this framework to papers from the IJAIS special issue and recent AI-accounting research published in leading accounting journals to map existing studies and identify research opportunities. Using this same framework, we analyze how accounting researchers can leverage their expertise through strategic positioning and collaboration, revealing where accounting scholars' strengths create the most value. We further examine how GenAI and LLMs transform the research process itself, comparing the capabilities of human researchers and AI agents across the entire research workflow. This analysis reveals that while GenAI democratizes certain research capabilities, it simultaneously intensifies competition by raising expectations for higher-order contributions where human judgment, creativity, and theoretical depth remain valuable. These shifts call for reforming doctoral education to cultivate comparative advantages while building AI fluency.
Neuromorphic Astronomy: An End-to-End SNN Pipeline for RFI Detection Hardware
Venue: arXiv cs.NE 2025
Year: 2025

Imminent radio telescope observatories provide massive data rates making deep learning based processing appealing while simultaneously demanding real-time performance at low-energy; prohibiting the use of many artificial neural network based approaches. We begin tackling the scientifically existential challenge of Radio Frequency Interference (RFI) detection by deploying deep Spiking Neural Networks (SNNs) on resource-constrained neuromorphic hardware. Our approach partitions large, pre-trained networks onto SynSense Xylo hardware using maximal splitting, a novel greedy algorithm. We validate this pipeline with on-chip power measurements, achieving instrument-scaled inference at 100mW. While our full-scale SNN achieves state-of-the-art accuracy among SNN baselines, our experiments reveal a more important insight that a smaller un-partitioned model significantly outperforms larger, split models. This finding highlights that hardware co-design is paramount for optimal performance. Our work thus provides a practical deployment blueprint, a key insight into the challenges of model scaling, and reinforces radio astronomy as a demanding yet ideal domain for advancing applied neuromorphic computing.
Change-of-Basis Pruning via Rotational Invariance
Venue: arXiv cs.LG 2025
Year: 2025

Structured pruning removes entire neurons or channels, but its effectiveness depends on how importance is distributed across the representation space. Change-of-basis (CoB) pruning addresses this challenge by applying orthogonal linear transformations that concentrate importance within certain dimensions. However, many standard deep learning architectures are not inherently invariant to such transformations. To enable compatibility, we introduce two-subspace radial activations (TSRAs): an activation family that is invariant to orthogonal linear transformations applied independently within its two activation subspaces. This invariance allows CoB transformations to be merged into surrounding weights without incurring extra parameters. We position this work as a proof-of-concept that a rotationally invariant design may offer a principled approach towards change-of-basis pruning. We do not provide an analysis of multiple TSRA candidates nor do we explore weight initialization for any TSRAs. These limitations, combined with other necessary modifications we make to permit rotational invariance, result in a slight accuracy drop of $4.52\%$ compared to a ReLU-based control. However, using activation-magnitude importance, VGG-16 implementing our CoB+TSRA framework shows encouraging results on CIFAR-10. Under fixed-ratio structured pruning, CoB improves accuracy over a TSRA baseline at all pruning ratios and extends reliable pruning frontier from roughly $30\%$ to $70\%$ of parameters without post-prune fine tuning. Under threshold-based pruning strategies, CoB prunes $90-96\%$ of parameters while maintaining $1-6\%$ accuracy drop after fine-tuning. Together, these results indicate that rotationally invariant architectures may offer a promising path towards CoB pruning.
Modeling Pointing, Acquisition, and Tracking Delays in Free-Space Optical Satellite Networks
Venue: arXiv cs.NI 2025
Year: 2025

Free-space optical inter-satellite links (OISLs) enable high-capacity space communications but require precise Pointing, Acquisition, and Tracking (PAT) between links. Current scheduling approaches often overlook or oversimplify PAT delays, leading to inefficient contact planning and overestimated network capacities. We present a validated model for quantifying retargeting delays, defined as the delay-inducing portion of PAT before data transmission begins, encompassing coarse pointing, fine pointing, and the handover to tracking. The model is grounded in mission data from NASA TBIRD, LLCD, DSOC, and ESA's Lunar Optical Communication Terminal. We find that PAT delays exhibit multimodal distributions based on prior link geometry and scale nonlinearly with initial pointing uncertainty and optical beam width. Integrating these delay models into routing and scheduling algorithms will enable more accurate contact planning and higher utilization in optical networks. The proposed model provides a foundation for evaluating performance and designing algorithms for future large-scale optical satellite networks.
ILoRA: Federated Learning with Low-Rank Adaptation for Heterogeneous Client Aggregation
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning with Low-Rank Adaptation (LoRA) faces three critical challenges under client heterogeneity: (1) Initialization-Induced Instability due to random initialization misaligning client subspaces; (2) Rank Incompatibility and Aggregation Error when averaging LoRA parameters of different ranks, which biases the global model; and (3) exacerbated Client Drift under Non-IID Data, impairing generalization. To address these challenges, we propose ILoRA, a unified framework that integrates three core innovations: a QR-based orthonormal initialization to ensure all clients start in a coherent subspace; a Concatenated QR Aggregation mechanism that fuses heterogeneous-rank updates via concatenation and decomposition, preserving information while maintaining dimension alignment; and an AdamW optimizer with rank-aware control variates to correct local updates and mitigate client drift. Supported by theoretical convergence guarantees, extensive experiments on vision and NLP benchmarks demonstrate that ILoRA consistently achieves superior accuracy and convergence stability compared to existing federated LoRA methods.