OpenRaiser/Intern-Atlas huggingface dataset:
Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures
Venue: arXiv cs.DC 2025
Year: 2025

The rapid scaling of large language models (LLMs) has unveiled critical limitations in current hardware architectures, including constraints in memory capacity, computational efficiency, and interconnection bandwidth. DeepSeek-V3, trained on 2,048 NVIDIA H800 GPUs, demonstrates how hardware-aware model co-design can effectively address these challenges, enabling cost-efficient training and inference at scale. This paper presents an in-depth analysis of the DeepSeek-V3/R1 model architecture and its AI infrastructure, highlighting key innovations such as Multi-head Latent Attention (MLA) for enhanced memory efficiency, Mixture of Experts (MoE) architectures for optimized computation-communication trade-offs, FP8 mixed-precision training to unlock the full potential of hardware capabilities, and a Multi-Plane Network Topology to minimize cluster-level network overhead. Building on the hardware bottlenecks encountered during DeepSeek-V3's development, we engage in a broader discussion with academic and industry peers on potential future hardware directions, including precise low-precision computation units, scale-up and scale-out convergence, and innovations in low-latency communication fabrics. These insights underscore the critical role of hardware and model co-design in meeting the escalating demands of AI workloads, offering a practical blueprint for innovation in next-generation AI systems.
GreenFactory: Ensembling Zero-Cost Proxies to Estimate Performance of Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Determining the performance of a Deep Neural Network during Neural Architecture Search processes is essential for identifying optimal architectures and hyperparameters. Traditionally, this process requires training and evaluation of each network, which is time-consuming and resource-intensive. Zero-cost proxies estimate performance without training, serving as an alternative to traditional training. However, recent proxies often lack generalization across diverse scenarios and provide only relative rankings rather than predicted accuracies. To address these limitations, we propose GreenFactory, an ensemble of zero-cost proxies that leverages a random forest regressor to combine multiple predictors' strengths and directly predict model test accuracy. We evaluate GreenFactory on NATS-Bench, achieving robust results across multiple datasets. Specifically, GreenFactory achieves high Kendall correlations on NATS-Bench-SSS, indicating substantial agreement between its predicted scores and actual performance: 0.907 for CIFAR-10, 0.945 for CIFAR-100, and 0.920 for ImageNet-16-120. Similarly, on NATS-Bench-TSS, we achieve correlations of 0.921 for CIFAR-10, 0.929 for CIFAR-100, and 0.908 for ImageNet-16-120, showcasing its reliability in both search spaces.
Exploiting the Potential Supervision Information of Clean Samples in Partial Label Learning
Venue: arXiv cs.LG 2025
Year: 2025

Diminishing the impact of false-positive labels is critical for conducting disambiguation in partial label learning. However, the existing disambiguation strategies mainly focus on exploiting the characteristics of individual partial label instances while neglecting the strong supervision information of clean samples randomly lying in the datasets. In this work, we show that clean samples can be collected to offer guidance and enhance the confidence of the most possible candidates. Motivated by the manner of the differentiable count loss strat- egy and the K-Nearest-Neighbor algorithm, we proposed a new calibration strategy called CleanSE. Specifically, we attribute the most reliable candidates with higher significance under the assumption that for each clean sample, if its label is one of the candidates of its nearest neighbor in the representation space, it is more likely to be the ground truth of its neighbor. Moreover, clean samples offer help in characterizing the sample distributions by restricting the label counts of each label to a specific interval. Extensive experiments on 3 synthetic benchmarks and 5 real-world PLL datasets showed this calibration strategy can be applied to most of the state-of-the-art PLL methods as well as enhance their performance.
RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo
Venue: arXiv cs.CV 2025
Year: 2025

Standard benchmarks for optical flow, scene flow, and stereo vision algorithms generally focus on model accuracy rather than robustness to image corruptions like noise or rain. Hence, the resilience of models to such real-world perturbations is largely unquantified. To address this, we present RobustSpring, a comprehensive dataset and benchmark for evaluating robustness to image corruptions for optical flow, scene flow, and stereo models. RobustSpring applies 20 different image corruptions, including noise, blur, color changes, quality degradations, and weather distortions, in a time-, stereo-, and depth-consistent manner to the high-resolution Spring dataset, creating a suite of 20,000 corrupted images that reflect challenging conditions. RobustSpring enables comparisons of model robustness via a new corruption robustness metric. Integration with the Spring benchmark enables public two-axis evaluations of both accuracy and robustness. We benchmark a curated selection of initial models, observing that accurate models are not necessarily robust and that robustness varies widely by corruption type. RobustSpring is a new computer vision benchmark that treats robustness as a first-class citizen to foster models that combine accuracy with resilience. It will be available at https://spring-benchmark.org.
Text-driven Motion Generation: Overview, Challenges and Directions
Venue: arXiv cs.CV 2025
Year: 2025

Text-driven motion generation offers a powerful and intuitive way to create human movements directly from natural language. By removing the need for predefined motion inputs, it provides a flexible and accessible approach to controlling animated characters. This makes it especially useful in areas like virtual reality, gaming, human-computer interaction, and robotics. In this review, we first revisit the traditional perspective on motion synthesis, where models focused on predicting future poses from observed initial sequences, often conditioned on action labels. We then provide a comprehensive and structured survey of modern text-to-motion generation approaches, categorizing them from two complementary perspectives: (i) architectural, dividing methods into VAE-based, diffusion-based, and hybrid models; and (ii) motion representation, distinguishing between discrete and continuous motion generation strategies. In addition, we explore the most widely used datasets, evaluation methods, and recent benchmarks that have shaped progress in this area. With this survey, we aim to capture where the field currently stands, bring attention to its key challenges and limitations, and highlight promising directions for future exploration. We hope this work offers a valuable starting point for researchers and practitioners working to push the boundaries of language-driven human motion synthesis.
Generalized Code Index Modulation Aided AFDM for Spread Spectrum Systems
Venue: arXiv cs.IT 2025
Year: 2025

The recently proposed affine frequency division multiplexing (AFDM) is a new transmission waveform that has shown excellent performance in high-mobility environments, making it a sensible option for the next-generation wireless networks. In this paper, we investigate an energy-efficient generalized code index modulation scheme for AFDM by leveraging spread spectrum, referred to as GCIM-AFDM-SS, to combat the interference caused by the doubly dispersive channels. Specifically, the information bits are conveyed by the transmitted symbols as well as the indices of the selected spreading codes in our proposed GCIM-AFDM-SS scheme. To avoid extensive computations, we also develop a lowcomplexity maximal ratio combining (MRC) detector algorithm, which recovers the spreading codes first and demodulates the symbols afterwards. Moreover, an upper bound on the bit error rate (BER) of the proposed GCIM-AFDM-SS system with maximum-likelihood (ML) detection is derived. Numerical results demonstrate the superiority of the proposed GCIM-AFDM-SS system over the classical AFDM spread spectrum (AFDM-SS) and the existing index modulated AFDM (IM-AFDM) systems.
Utilization of Skin Color Change for Image-based Tactile Sensing
Venue: arXiv cs.HC 2025
Year: 2025

Measurement of pressure distribution applied to a fingertip is crucial for the teleoperation of robots and human computer interface. Previous studies have acquired pressure distribution by affixing a sensor array to the fingertip or by optically recording the deformation of an object. However, these existing methods inhibit the fingertip from directly contacting the texture, and the pressure applied to the fingertip is measured indirectly. In this study, we propose a method to measure pressure distribution by directly touching a transparent object, focusing on the change in skin color induced by the applied pressure, caused by blood flow. We evaluated the relationship between pressure and skin color change when local pressure is applied, and found a correlation between the pressure and the color change. However, the contact area and the color change area did not align perfectly. We further explored the factor causing the spatial non-uniformity of the color change, by accounting for the stress distribution using finite element analysis. These results suggest that the proposed measurement method can be utilized to measure the internal stress distribution, and it is anticipated to serve as a simple sensor in the field of human computer interface.
Exploring Pose-Guided Imitation Learning for Robotic Precise Insertion
Venue: arXiv cs.RO 2025
Year: 2025

Recent studies have proved that imitation learning shows strong potential in the field of robotic manipulation. However, existing methods still struggle with precision manipulation task and rely on inefficient image/point cloud observations. In this paper, we explore to introduce SE(3) object pose into imitation learning and propose the pose-guided efficient imitation learning methods for robotic precise insertion task. First, we propose a precise insertion diffusion policy which utilizes the relative SE(3) pose as the observation-action pair. The policy models the source object SE(3) pose trajectory relative to the target object. Second, we explore to introduce the RGBD data to the pose-guided diffusion policy. Specifically, we design a goal-conditioned RGBD encoder to capture the discrepancy between the current state and the goal state. In addition, a pose-guided residual gated fusion method is proposed, which takes pose features as the backbone, and the RGBD features selectively compensate for pose feature deficiencies through an adaptive gating mechanism. Our methods are evaluated on 6 robotic precise insertion tasks, demonstrating competitive performance with only 7-10 demonstrations. Experiments demonstrate that the proposed methods can successfully complete precision insertion tasks with a clearance of about 0.01 mm. Experimental results highlight its superior efficiency and generalization capability compared to existing baselines. Code will be available at https://github.com/sunhan1997/PoseInsert.
CXMArena: Unified Dataset to benchmark performance in realistic CXM Scenarios
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) hold immense potential for revolutionizing Customer Experience Management (CXM), particularly in contact center operations. However, evaluating their practical utility in complex operational environments is hindered by data scarcity (due to privacy concerns) and the limitations of current benchmarks. Existing benchmarks often lack realism, failing to incorporate deep knowledge base (KB) integration, real-world noise, or critical operational tasks beyond conversational fluency. To bridge this gap, we introduce CXMArena, a novel, large-scale synthetic benchmark dataset specifically designed for evaluating AI in operational CXM contexts. Given the diversity in possible contact center features, we have developed a scalable LLM-powered pipeline that simulates the brand's CXM entities that form the foundation of our datasets-such as knowledge articles including product specifications, issue taxonomies, and contact center conversations. The entities closely represent real-world distribution because of controlled noise injection (informed by domain experts) and rigorous automated validation. Building on this, we release CXMArena, which provides dedicated benchmarks targeting five important operational tasks: Knowledge Base Refinement, Intent Prediction, Agent Quality Adherence, Article Search, and Multi-turn RAG with Integrated Tools. Our baseline experiments underscore the benchmark's difficulty: even state of the art embedding and generation models achieve only 68% accuracy on article search, while standard embedding methods yield a low F1 score of 0.3 for knowledge base refinement, highlighting significant challenges for current models necessitating complex pipelines and solutions over conventional techniques.
Flash-VL 2B: Optimizing Vision-Language Model Performance for Ultra-Low Latency and High Throughput
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we introduce Flash-VL 2B, a novel approach to optimizing Vision-Language Models (VLMs) for real-time applications, targeting ultra-low latency and high throughput without sacrificing accuracy. Leveraging advanced architectural enhancements and efficient computational strategies, Flash-VL 2B is designed to maximize throughput by reducing processing time while maintaining competitive performance across multiple vision-language benchmarks. Our approach includes tailored architectural choices, token compression mechanisms, data curation, training schemes, and a novel image processing technique called implicit semantic stitching that effectively balances computational load and model performance. Through extensive evaluations on 11 standard VLM benchmarks, we demonstrate that Flash-VL 2B achieves state-of-the-art results in both speed and accuracy, making it a promising solution for deployment in resource-constrained environments and large-scale real-time applications.
Evaluation Metrics for Misinformation Warning Interventions: Challenges and Prospects
Venue: arXiv cs.HC 2025
Year: 2025

Misinformation has become a widespread issue in the 21st century, impacting numerous areas of society and underscoring the need for effective intervention strategies. Among these strategies, user-centered interventions, such as warning systems, have shown promise in reducing the spread of misinformation. Many studies have used various metrics to evaluate the effectiveness of these warning interventions. However, no systematic review has thoroughly examined these metrics in all studies. This paper provides a comprehensive review of existing metrics for assessing the effectiveness of misinformation warnings, categorizing them into four main groups: behavioral impact, trust and credulity, usability, and cognitive and psychological effects. Through this review, we identify critical challenges in measuring the effectiveness of misinformation warnings, including inconsistent use of cognitive and attitudinal metrics, the lack of standardized metrics for affective and emotional impact, variations in user trust, and the need for more inclusive warning designs. We present an overview of these metrics and propose areas for future research.
GlobalMood: A cross-cultural benchmark for music emotion recognition
Venue: arXiv cs.IR 2025
Year: 2025

Human annotations of mood in music are essential for music generation and recommender systems. However, existing datasets predominantly focus on Western songs with terms derived from English, which may limit generalizability across diverse linguistic and cultural backgrounds. We introduce 'GlobalMood', a novel cross-cultural benchmark dataset comprising 1,180 songs sampled from 59 countries, with large-scale annotations collected from 2,519 individuals across five culturally and linguistically distinct locations: U.S., France, Mexico, S. Korea, and Egypt. Rather than imposing predefined emotion and mood categories, we implement a bottom-up, participant-driven approach to organically elicit culturally specific music-related emotion terms. We then recruit another pool of human participants to collect 988,925 ratings for these culture-specific descriptors. Our analysis confirms the presence of a valence-arousal structure shared across cultures, yet also reveals significant divergences in how certain emotion terms (despite being dictionary equivalents) are perceived cross-culturally. State-of-the-art multimodal models benefit substantially from fine-tuning on our cross-culturally balanced dataset, particularly in non-English contexts. Broadly, our findings inform the ongoing debate on the universality versus cultural specificity of emotional descriptors, and our methodology can contribute to other multimodal and cross-lingual research.
MigrationBench: Repository-Level Code Migration Benchmark from Java 8
Venue: arXiv cs.SE 2025
Year: 2025

With the rapid advancement of powerful large language models (LLMs) in recent years, a wide range of software engineering tasks can now be addressed using LLMs, significantly enhancing productivity and scalability. Numerous benchmark datasets have been developed to evaluate the coding capabilities of these models, while they primarily focus on code generation and issue-resolution tasks. In contrast, we introduce a new coding benchmark MigrationBench with a distinct focus: code migration. MigrationBench aims to serve as a comprehensive benchmark for migration from Java $8$ to the latest long-term support (LTS) versions (Java $17$, $21$), including a full dataset and its subset selected with $5,102$ and $300$ repositories respectively. Selected is a representative subset curated for complexity and difficulty, offering a versatile resource to support research in the field of code migration. Additionally, we provide a comprehensive evaluation framework to facilitate rigorous and standardized assessment of LLMs on this challenging task. We further propose SD-Feedback and demonstrate that LLMs can effectively tackle repository-level code migration to Java $17$. For the selected subset with Claude-3.5-Sonnet-v2, SD-Feedback achieves $62.33\%$ and $27.33\%$ success rate (pass@1) for minimal and maximal migration respectively. The benchmark dataset and source code are available at: https://huggingface.co/collections/AmazonScience/migrationbench-68125452fc21a4564b92b6c3 and https://github.com/amazon-science/MigrationBench respectively.
Ethics and Persuasion in Reinforcement Learning from Human Feedback: A Procedural Rhetorical Approach
Venue: arXiv cs.CY 2025
Year: 2025

Since 2022, versions of generative AI chatbots such as ChatGPT and Claude have been trained using a specialized technique called Reinforcement Learning from Human Feedback (RLHF) to fine-tune language model output using feedback from human annotators. As a result, the integration of RLHF has greatly enhanced the outputs of these large language models (LLMs) and made the interactions and responses appear more "human-like" than those of previous versions using only supervised learning. The increasing convergence of human and machine-written text has potentially severe ethical, sociotechnical, and pedagogical implications relating to transparency, trust, bias, and interpersonal relations. To highlight these implications, this paper presents a rhetorical analysis of some of the central procedures and processes currently being reshaped by RLHF-enhanced generative AI chatbots: upholding language conventions, information seeking practices, and expectations for social relationships. Rhetorical investigations of generative AI and LLMs have, to this point, focused largely on the persuasiveness of the content generated. Using Ian Bogost's concept of procedural rhetoric, this paper shifts the site of rhetorical investigation from content analysis to the underlying mechanisms of persuasion built into RLHF-enhanced LLMs. In doing so, this theoretical investigation opens a new direction for further inquiry in AI ethics that considers how procedures rerouted through AI-driven technologies might reinforce hegemonic language use, perpetuate biases, decontextualize learning, and encroach upon human relationships. It will therefore be of interest to educators, researchers, scholars, and the growing number of users of generative AI chatbots.
MDTP -- An Adaptive Multi-Source Data Transfer Protocol
Venue: arXiv cs.DC 2025
Year: 2025

Scientific data volume is growing in size, and as a direct result, the need for faster transfers is also increasing. The scientific community has sought to leverage parallel transfer methods using multi-threaded and multi-source download models to reduce download times. In multi-source transfers, a client downloads data from multiple replicated servers in parallel. Tools such as Aria2 and BitTorrent support such multi-source transfers and have shown improved transfer times. In this work, we introduce Multi-Source Data Transfer Protocol, MDTP, which further improves multi-source transfer performance. MDTP logically divides a file request into smaller chunk requests and distributes the chunk requests across multiple servers. Chunk sizes are adapted based on each server's performance but selected in a way that ensures each round of requests completes around the same time. We formulate this chunk-size allocation problem as a variant of the bin-packing problem, where adaptive chunking efficiently fills the available capacity "bins" corresponding to each server. Our evaluation shows that MDTP reduces transfer times by 10-22% compared to Aria2, the fastest alternative. Comparisons with other protocols, such as static chunking and BitTorrent, demonstrate even greater improvements. Additionally, we show that MDTP distributes load proportionally across all available replicas, not just the fastest ones, which improves throughput. Finally, we show MDTP maintains high throughput even when latency increases or bandwidth to the fastest server decreases.
LightLab: Controlling Light Sources in Images with Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

We present a simple, yet effective diffusion-based method for fine-grained, parametric control over light sources in an image. Existing relighting methods either rely on multiple input views to perform inverse rendering at inference time, or fail to provide explicit control over light changes. Our method fine-tunes a diffusion model on a small set of real raw photograph pairs, supplemented by synthetically rendered images at scale, to elicit its photorealistic prior for relighting. We leverage the linearity of light to synthesize image pairs depicting controlled light changes of either a target light source or ambient illumination. Using this data and an appropriate fine-tuning scheme, we train a model for precise illumination changes with explicit control over light intensity and color. Lastly, we show how our method can achieve compelling light editing results, and outperforms existing methods based on user preference.