OpenRaiser/Intern-Atlas huggingface dataset:
Performance Evaluation of Multi-Armed Bandit Algorithms for Wi-Fi Channel Access
Venue: arXiv cs.NI 2025
Year: 2025

The adoption of dynamic, self-learning solutions for real-time wireless network optimization has recently gained significant attention due to the limited adaptability of existing protocols. This paper investigates multi-armed bandit (MAB) strategies as a data-driven approach for decentralized, online channel access optimization in Wi-Fi, targeting dynamic channel access settings: primary channel, channel width, and contention window (CW) adjustment. Key design aspects are examined, including the adoption of joint versus factorial action spaces, the inclusion of contextual information, and the nature of the action-selection strategy (optimism-driven, unimodal, or randomized). State-of-the-art algorithms and a proposed lightweight contextual approach, E-RLB, are evaluated through simulations. Results show that contextual and optimism-driven strategies consistently achieve the highest performance and fastest adaptation under recurrent conditions. Unimodal structures require careful graph construction to ensure that the unimodality assumption holds. Randomized exploration, adopted in the proposed E-RLB, can induce disruptive parameter reallocations, especially in multi-player settings. Decomposing the action space across several specialized agents accelerates convergence but increases sensitivity to randomized exploration and demands coordination under shared rewards to avoid correlated learning. Finally, despite its inherent inefficiencies from epsilon-greedy exploration, E-RLB demonstrates effective adaptation and learning, highlighting its potential as a viable low-complexity solution for realistic dynamic deployments.
Homomorphism Testing with Resilience to Online Manipulations
Venue: arXiv cs.DS 2025
Year: 2025

A central challenge in property testing is verifying algebraic structure with minimal access to data. A landmark result addressing this challenge, the linearity test of Blum, Luby, and Rubinfeld (JCSS `93), spurred a rich body of work on testing algebraic properties such as linearity and its generalizations to low-degree polynomials and group homomorphisms. However, classical tests for these properties assume unrestricted, noise-free access to the input function--an assumption that breaks down in adversarial or dynamic settings. To address this, Kalemaj, Raskhodnikova, and Varma (Theory of Computing `23) introduced the online manipulation model, where an adversary may erase or corrupt query responses over time, based on the tester's past queries. We initiate the study of {manipulation-resilient} testing for {group homomorphism} in this online model. Our main result is an {optimal} tester that makes $O(1/\varepsilon+\log t)$ queries, where $\varepsilon$ is the distance parameter and $t$ is the number of function values the adversary can erase or corrupt per query. Our result recovers the celebrated $O(1/\varepsilon)$ bound by Ben-Or, Coppersmith, Luby, and Rubinfeld (Random Struct.\ Algorithms `08) for homomorphism testing in the standard property testing model, albeit with a different tester. Our tester, $\mathsf{Random\ Signs\ Test}$, {lifts} known manipulation-resilient linearity testers for $\mathbb{F}_2^n\to \mathbb{F}_2$ to general group domains and codomains by introducing more randomness: instead of verifying the homomorphism condition for a sum of random elements, it uses additions and subtractions of random elements, randomly selecting a sign for each element. We also obtain improved group-specific query bounds for key families of groups.
SimScale: Learning to Drive via Real-World Simulation at Scale
Venue: arXiv cs.CV 2025
Year: 2025

Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-distribution ones. However, such cases are underrepresented in real-world corpus collected by human experts. To complement for the lack of data diversity, we introduce a novel and scalable simulation framework capable of synthesizing massive unseen states upon existing driving logs. Our pipeline utilizes advanced neural rendering with a reactive environment to generate high-fidelity multi-view observations controlled by the perturbed ego trajectory. Furthermore, we develop a pseudo-expert trajectory generation mechanism for these newly simulated states to provide action supervision. Upon the synthesized data, we find that a simple co-training strategy on both real-world and simulated samples can lead to significant improvements in both robustness and generalization for various planning methods on challenging real-world benchmarks, up to +6.8 EPDMS on navhard and +2.9 on navtest. More importantly, such policy improvement scales smoothly by increasing simulation data only, even without extra real-world data streaming in. We further reveal several crucial findings of such a sim-real learning system, which we term SimScale, including the design of pseudo-experts and the scaling properties for different policy architectures. Our simulation data and code would be released.
Design, modelling and experimental validation of bipenniform shape memory alloy-based linear actuator integrable with hydraulic stroke amplification mechanism
Venue: arXiv cs.RO 2025
Year: 2025

The increasing industrial demand for alternative actuators over conventional electromagnetism-based systems having limited efficiency, bulky size, complex design due to in-built gear-train mechanisms, and high production and amortization costs necessitates the innovation in new actuator development. Integrating bio-inspired design principles into linear actuators could bring forth the next generation of adaptive and energy efficient smart material-based actuation systems. The present study amalgamates the advantages of bipenniform architecture, which generates high force in the given physiological region and a high power-to-weight ratio of shape memory alloy (SMA), into a novel bio-inspired SMA-based linear actuator. A mathematical model of a multi-layered bipenniform configuration-based SMA actuator was developed and validated experimentally. The current research also caters to the incorporation of failure mitigation strategies using design failure mode and effects analysis along with the experimental assessment of the performance of the developed actuator. The system has been benchmarked against an industry-developed stepper motor-driven actuator. It has shown promising results generating an actuation force of 257 N with 15 V input voltage, meeting the acceptable range for actuation operation. It further exhibits about 67% reduction in the weight of the drive mechanism, with 80% lesser component, 32% cost reduction, and 19% energy savings and similar envelope dimensions for assembly compatibility with dampers and louvers for easy onsite deployment. The study introduces SMA coil-based actuator as an advanced design that can be deployed for high force-high stroke applications. The bio-inspired SMA-based linear actuator has applications ranging from building automation controls to lightweight actuation systems for space robotics and medical prosthesis.
Hierarchical AI-Meteorologist: LLM-Agent System for Multi-Scale and Explainable Weather Forecast Reporting
Venue: arXiv cs.AI 2025
Year: 2025

We present the Hierarchical AI-Meteorologist, an LLM-agent system that generates explainable weather reports using a hierarchical forecast reasoning and weather keyword generation. Unlike standard approaches that treat forecasts as flat time series, our framework performs multi-scale reasoning across hourly, 6-hour, and daily aggregations to capture both short-term dynamics and long-term trends. Its core reasoning agent converts structured meteorological inputs into coherent narratives while simultaneously extracting a few keywords effectively summarizing the dominant meteorological events. These keywords serve as semantic anchors for validating consistency, temporal coherence and factual alignment of the generated reports. Using OpenWeather and Meteostat data, we demonstrate that hierarchical context and keyword-based validation substantially improve interpretability and robustness of LLM-generated weather narratives, offering a reproducible framework for semantic evaluation of automated meteorological reporting and advancing agent-based scientific reasoning.
The Price of Progress: Algorithmic Efficiency and the Falling Cost of AI Inference
Venue: arXiv cs.LG 2025
Year: 2025

Language models have seen enormous progress on advanced benchmarks in recent years, but much of this progress has only been possible by using more costly models. Benchmarks may therefore present a warped picture of progress in practical capabilities per dollar. To remedy this, we use data from Artificial Analysis and Epoch AI to form the largest dataset of current and historical prices to run benchmarks to date. We find that the price for a given level of benchmark performance has decreased remarkably fast, around $5\times$ to $10\times$ per year, for frontier models on knowledge, reasoning, math, and software engineering benchmarks. These reductions in the cost of AI inference are due to economic forces, hardware efficiency improvements, and algorithmic efficiency improvements. Isolating out open models to control for competition effects and dividing by hardware price declines, we estimate that algorithmic efficiency progress is around $3\times$ per year. Finally, we recommend that evaluators both publicize and take into account the price of benchmarking as an essential part of measuring the real-world impact of AI.
Visual Generation Tuning
Venue: arXiv cs.CV 2025
Year: 2025

Large Vision Language Models (VLMs) effectively bridge the modality gap through extensive pretraining, acquiring sophisticated visual representations aligned with language. However, it remains underexplored whether these representations, optimized for multimodal understanding tasks, harbor an inherent potential for visual generation. In this paper, we propose VGT, Visual Generation Tuning, a novel paradigm designed to stimulate the underlying capabilities of visual generation within any vision language models. By performing efficient visual generation tuning on well-pretrained VLMs, we significantly mitigate the alignment costs and accelerate the convergence of autoregressive modeling in the continuous space (20x speedup). Specifically, we dismiss the entangled pixel-level VAEs designed for diffusion transformers and formulate VGT-AE through aligning the semantic encoders from pretrained VLMs with the latent representations of pixel decoders. In image reconstruction tasks, we achieve 26.67 PSNR and 0.50 rFID at a 28x compression ratio, outperforming specialized VAEs; in visual generation tasks, we achieve state-of-the-art outcomes among autoregressive models, 0.77 on GenEval and 78.73 on DPG-Bench. Furthermore, our proposed VGT showcases significant scaling promise and is versatile for endowing any VLMs trained for multimodal understanding with the capabilities of visual generation, which paves the new avenue to explore next-generation unified multimodal foundation models. Models and codes are available at https://github.com/hustvl/VGT.
Video-CoM: Interactive Video Reasoning via Chain of Manipulations
Venue: arXiv cs.CV 2025
Year: 2025

Recent multimodal large language models (MLLMs) have advanced video understanding, yet most still "think about videos" ie once a video is encoded, reasoning unfolds entirely in text, treating visual input as a static context. This passive paradigm creates a semantic bottleneck: models cannot rewatch, refocus, or verify evidence, leading to shallow visual reasoning on tasks requiring fine grained spatio temporal understanding. In this work, we introduce Interactive Video Reasoning, a new paradigm that transforms video into an active cognitive workspace, enabling models to "think with videos". Our model, Video CoM, reasons through a Chain of Manipulations (CoM), performing iterative visual actions to gather and refine evidence. To support this behavior, we construct Video CoM Instruct, an 18K instruction tuning dataset curated for multi step manipulation reasoning. Beyond supervised learning, we further optimize the manipulation policy via reinforcement learning with reasoning aware Group Relative Policy Optimization (GRPO). Unlike prior work that relies solely on sparse answer rewards, our method introduces step level reasoning rewards, guiding the model toward grounded and consistent reasoning. Video CoM achieves strong results across nine video reasoning benchmarks, improving average performance by 3.6 percent over recent state of the art models, while training on only 25K SFT and 3K GRPO video samples, significantly fewer than comparable large scale models. Ablation studies demonstrate that reasoning aware rewards improve both accuracy and interpretability. Code: https://github.com/mbzuai-oryx/Video-CoM
DREAMer-VXS: A Latent World Model for Sample-Efficient AGV Exploration in Stochastic, Unobserved Environments
Venue: arXiv cs.RO 2025
Year: 2025

The paradigm of learning-based robotics holds immense promise, yet its translation to real-world applications is critically hindered by the sample inefficiency and brittleness of conventional model-free reinforcement learning algorithms. In this work, we address these challenges by introducing DREAMer-VXS, a model-based framework for Autonomous Ground Vehicle (AGV) exploration that learns to plan from imagined latent trajectories. Our approach centers on learning a comprehensive world model from partial and high-dimensional LiDAR observations. This world model is composed of a Convolutional Variational Autoencoder (VAE), which learns a compact representation of the environment's structure, and a Recurrent State-Space Model (RSSM), which models complex temporal dynamics. By leveraging this learned model as a high-speed simulator, the agent can train its navigation policy almost entirely in imagination. This methodology decouples policy learning from real-world interaction, culminating in a 90% reduction in required environmental interactions to achieve expert-level performance when compared to state-of-the-art model-free SAC baselines. The agent's behavior is guided by an actor-critic policy optimized with a composite reward function that balances task objectives with an intrinsic curiosity bonus, promoting systematic exploration of unknown spaces. We demonstrate through extensive simulated experiments that DREAMer-VXS not only learns orders of magnitude faster but also develops more generalizable and robust policies, achieving a 45% increase in exploration efficiency in unseen environments and superior resilience to dynamic obstacles.
Development and Benchmarking of a Blended Human-AI Qualitative Research Assistant
Venue: arXiv cs.HC 2025
Year: 2025

Qualitative research emphasizes constructing meaning through iterative engagement with textual data. Traditionally this human-driven process requires navigating coder fatigue and interpretative drift, thus posing challenges when scaling analysis to larger, more complex datasets. Computational approaches to augment qualitative research have been met with skepticism, partly due to their inability to replicate the nuance, context-awareness, and sophistication of human analysis. Large language models, however, present new opportunities to automate aspects of qualitative analysis while upholding rigor and research quality in important ways. To assess their benefits and limitations - and build trust among qualitative researchers - these approaches must be rigorously benchmarked against human-generated datasets. In this work, we benchmark Muse, an interactive, AI-powered qualitative research system that allows researchers to identify themes and annotate datasets, finding an inter-rater reliability between Muse and humans of Cohen's $κ$ = 0.71 for well-specified codes. We also conduct robust error analysis to identify failure mode, guide future improvements, and demonstrate the capacity to correct for human bias.
Leveraging LLMs for Design Ideation: An AI Tool to Assist Creativity
Venue: arXiv cs.HC 2025
Year: 2025

The creative potential of computers has intrigued researchers for decades. Since the emergence of Generative AI (Gen AI), computer creativity has found many new dimensions and applications. As Gen AI permeates mainstream discourse and usage, researchers are delving into how it can improve and complement what humans do. Creative potential is a highly relevant notion to design practice and research, especially in the initial stages of ideation and conceptualisation. There is scope to improve creative potential in these stages, especially using machine intelligence. We propose a structured ideation session involving inspirational stimuli and utilise Gen AI in delivering this structure to designers through ALIA: Analogical LLM Ideation Agent, a tool for small-group ideation scenarios. The tool is developed by enabling speech based interactions with a Large Language Model (LLM) for inference generation. Inspiration is drawn from the synectic ideation method and the dialectics philosophy to design the optimal stimuli in group ideation. The tool is tested in design ideation sessions to compare the output of the AI-assisted ideation sessions to that of tradi tional ideation sessions. Preliminary findings showcase that participants have rated their ideas better when assisted by ALIA and respond favourably to speech-based interactions.
Cultural Prompting Improves the Empathy and Cultural Responsiveness of GPT-Generated Therapy Responses
Venue: arXiv cs.HC 2025
Year: 2025

Large Language Model (LLM)-based conversational agents offer promising solutions for mental health support, but lack cultural responsiveness for diverse populations. This study evaluated the effectiveness of cultural prompting in improving cultural responsiveness and perceived empathy of LLM-generated therapeutic responses for Chinese American family caregivers. Using a randomized controlled experiment, we compared GPT-4o and Deepseek-V3 responses with and without cultural prompting. Thirty-six participants evaluated input-response pairs on cultural responsiveness (competence and relevance) and perceived empathy. Results showed that cultural prompting significantly enhanced GPT-4o's performance across all dimensions, with GPT-4o with cultural prompting being the most preferred, while improvements in DeepSeek-V3 responses were not significant. Mediation analysis revealed that cultural prompting improved empathy through improving cultural responsiveness. This study demonstrated that prompt-based techniques can effectively enhance the cultural responsiveness of LLM-generated therapeutic responses, highlighting the importance of cultural responsiveness in delivering empathetic AI-based therapeutic interventions to culturally and linguistically diverse populations.
A Comprehensive Survey on Surgical Digital Twin
Venue: arXiv cs.RO 2025
Year: 2025

With the accelerating availability of multimodal surgical data and real-time computation, Surgical Digital Twins (SDTs) have emerged as virtual counterparts that mirror, predict, and inform decisions across pre-, intra-, and postoperative care. Despite promising demonstrations, SDTs face persistent challenges: fusing heterogeneous imaging, kinematics, and physiology under strict latency budgets; balancing model fidelity with computational efficiency; ensuring robustness, interpretability, and calibrated uncertainty; and achieving interoperability, privacy, and regulatory compliance in clinical environments. This survey offers a critical, structured review of SDTs. We clarify terminology and scope, propose a taxonomy by purpose, model fidelity, and data sources, and synthesize state-of-the-art achievements in deformable registration and tracking, real-time simulation and co-simulation, AR/VR guidance, edge-cloud orchestration, and AI for scene understanding and prediction. We contrast non-robotic twins with robot-in-the-loop architectures for shared control and autonomy, and identify open problems in validation and benchmarking, safety assurance and human factors, lifecycle "digital thread" integration, and scalable data governance. We conclude with a research agenda toward trustworthy, standards-aligned SDTs that deliver measurable clinical benefit. By unifying vocabulary, organizing capabilities, and highlighting gaps, this work aims to guide SDT design and deployment and catalyze translation from laboratory prototypes to routine surgical care.
XFlowMP: Task-Conditioned Motion Fields for Generative Robot Planning with Schrodinger Bridges
Venue: arXiv cs.RO 2025
Year: 2025

Generative robotic motion planning requires not only the synthesis of smooth and collision-free trajectories but also feasibility across diverse tasks and dynamic constraints. Prior planning methods, both traditional and generative, often struggle to incorporate high-level semantics with low-level constraints, especially the nexus between task configurations and motion controllability. In this work, we present XFlowMP, a task-conditioned generative motion planner that models robot trajectory evolution as entropic flows bridging stochastic noises and expert demonstrations via Schrodinger bridges given the inquiry task configuration. Specifically, our method leverages Schrodinger bridges as a conditional flow matching coupled with a score function to learn motion fields with high-order dynamics while encoding start-goal configurations, enabling the generation of collision-free and dynamically-feasible motions. Through evaluations, XFlowMP achieves up to 53.79% lower maximum mean discrepancy, 36.36% smoother motions, and 39.88% lower energy consumption while comparing to the next-best baseline on the RobotPointMass benchmark, and also reducing short-horizon planning time by 11.72%. On long-horizon motions in the LASA Handwriting dataset, our method maintains the trajectories with 1.26% lower maximum mean discrepancy, 3.96% smoother, and 31.97% lower energy. We further demonstrate the practicality of our method on the Kinova Gen3 manipulator, executing planning motions and confirming its robustness in real-world settings.
ICD-Net: Inertial Covariance Displacement Network for Drone Visual-Inertial SLAM
Venue: arXiv cs.RO 2025
Year: 2025

Visual-inertial SLAM systems often exhibit suboptimal performance due to multiple confounding factors including imperfect sensor calibration, noisy measurements, rapid motion dynamics, low illumination, and the inherent limitations of traditional inertial navigation integration methods. These issues are particularly problematic in drone applications where robust and accurate state estimation is critical for safe autonomous operation. In this work, we present ICD-Net, a novel framework that enhances visual-inertial SLAM performance by learning to process raw inertial measurements and generating displacement estimates with associated uncertainty quantification. Rather than relying on analytical inertial sensor models that struggle with real-world sensor imperfections, our method directly extracts displacement maps from sensor data while simultaneously predicting measurement covariances that reflect estimation confidence. We integrate ICD-Net outputs as additional residual constraints into the VINS-Fusion optimization framework, where the predicted uncertainties appropriately weight the neural network contributions relative to traditional visual and inertial terms. The learned displacement constraints provide complementary information that compensates for various error sources in the SLAM pipeline. Our approach can be used under both normal operating conditions and in situations of camera inconsistency or visual degradation. Experimental evaluation on challenging high-speed drone sequences demonstrated that our approach significantly improved trajectory estimation accuracy compared to standard VINS-Fusion, with more than 38% improvement in mean APE and uncertainty estimates proving crucial for maintaining system robustness. Our method shows that neural network enhancement can effectively address multiple sources of SLAM degradation while maintaining real-time performance requirements.