🔹 Title:
Skywork-SWE: Unveiling Data Scaling Laws for Software Engineering in LLMs

🔹 Publication Date: Published on Jun 24

🔹 Abstract:
An automated data-curation pipeline for software engineering improves large language model performance on SWE tasks, achieving state-of-the-art results with and without test-time scaling techniques. AI-generated summary Software engineering (SWE) has recently emerged as a crucial testbed for next-generation LLM agents , demanding inherent capabilities in two critical dimensions: sustained iterative problem-solving (e.g., >50 interaction rounds) and long-context dependency resolution (e.g., >32k tokens). However, the data curation process in SWE remains notoriously time-consuming, as it heavily relies on manual annotation for code file filtering and the setup of dedicated runtime environments to execute and validate unit tests . Consequently, most existing datasets are limited to only a few thousand GitHub-sourced instances. To this end, we propose an incremental, automated data-curation pipeline that systematically scales both the volume and diversity of SWE datasets. Our dataset comprises 10,169 real-world Python task instances from 2,531 distinct GitHub repositories, each accompanied by a task specified in natural language and a dedicated runtime-environment image for automated unit-test validation. We have carefully curated over 8,000 successfully runtime-validated training trajectories from our proposed SWE dataset. When fine-tuning the Skywork-SWE model on these trajectories, we uncover a striking data scaling phenomenon: the trained model's performance for software engineering capabilities in LLMs continues to improve as the data size increases, showing no signs of saturation. Notably, our Skywork-SWE model achieves 38.0% pass@1 accuracy on the SWE-bench Verified benchmark without using verifiers or multiple rollouts, establishing a new state-of-the-art (SOTA) among the Qwen2.5-Coder-32B-based LLMs built on the OpenHands agent framework . Furthermore, with the incorporation of test-time scaling techniques , the performance further improves to 47.0% accuracy, surpassing the previous SOTA results for sub-32B parameter models. We release the Skywork-SWE-32B model checkpoint to accelerate future research.

🔹 Links:
• arXiv Page: https://arxiv.org/pdf/2506.19290
• PDF: https://arxiv.org/pdf/2506.19290
• Project Page: https://quixotic-sting-239.notion.site/eb17f379610040ceb54da5d5d24065bd

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

🔹 Title:
MADrive: Memory-Augmented Driving Scene Modeling

🔹 Publication Date: Published on Jun 26

🔹 Abstract:
MADrive enhances scene reconstruction for autonomous driving by integrating visually similar 3D car assets from an external memory bank to achieve photorealistic synthesis of altered scenarios. AI-generated summary Recent advances in scene reconstruction have pushed toward highly realistic modeling of autonomous driving (AD) environments using 3D Gaussian splatting . However, the resulting reconstructions remain closely tied to the original observations and struggle to support photorealistic synthesis of significantly altered or novel driving scenarios. This work introduces MADrive , a memory-augmented reconstruction framework designed to extend the capabilities of existing scene reconstruction methods by replacing observed vehicles with visually similar 3D assets retrieved from a large-scale external memory bank. Specifically, we release MAD-Cars , a curated dataset of {sim}70K 360{\deg} car videos captured in the wild and present a retrieval module that finds the most similar car instances in the memory bank, reconstructs the corresponding 3D assets from video, and integrates them into the target scene through orientation alignment and relighting . The resulting replacements provide complete multi-view representations of vehicles in the scene, enabling photorealistic synthesis of substantially altered configurations, as demonstrated in our experiments. Project page: https://yandex-research.github.io/ madrive /

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.21520
• PDF: https://arxiv.org/pdf/2506.21520
• Project Page: https://yandex-research.github.io/madrive/

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/yandex/mad-cars

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

🔹 Title:
Generative Blocks World: Moving Things Around in Pictures

🔹 Publication Date: Published on Jun 25

🔹 Abstract:
A generative method that edits 3D scenes using convex primitives and regenerates images with enhanced texture consistency and visual fidelity. AI-generated summary We describe Generative Blocks World to interact with the scene of a generated image by manipulating simple geometric abstractions. Our method represents scenes as assemblies of convex 3D primitives , and the same scene can be represented by different numbers of primitives, allowing an editor to move either whole structures or small details. Once the scene geometry has been edited, the image is generated by a flow-based method which is conditioned on depth and a texture hint . Our texture hint takes into account the modified 3D primitives, exceeding texture-consistency provided by existing key-value caching techniques. These texture hint s (a) allow accurate object and camera moves and (b) largely preserve the identity of objects depicted. Quantitative and qualitative experiments demonstrate that our approach outperforms prior works in visual fidelity , editability , and compositional generalization .

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.20703
• PDF: https://arxiv.org/pdf/2506.20703

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

🔹 Title:
SAM4D: Segment Anything in Camera and LiDAR Streams

🔹 Publication Date: Published on Jun 26

🔹 Abstract:
SAM4D is a multi-modal and temporal foundation model for segmentation in autonomous driving using Unified Multi-modal Positional Encoding and Motion-aware Cross-modal Memory Attention, with a multi-modal automated data engine generating pseudo-labels. AI-generated summary We present SAM4D, a multi-modal and temporal foundation model designed for promptable segmentation across camera and LiDAR streams. Unified Multi-modal Positional Encoding (UMPE) is introduced to align camera and LiDAR features in a shared 3D space , enabling seamless cross-modal prompting and interaction. Additionally, we propose Motion-aware Cross-modal Memory Attention (MCMA), which leverages ego-motion compensation to enhance temporal consistency and long-horizon feature retrieval, ensuring robust segmentation across dynamically changing autonomous driving scenes. To avoid annotation bottlenecks, we develop a multi-modal automated data engine that synergizes VFM-driven video masklets, spatiotemporal 4D reconstruction , and cross-modal masklet fusion . This framework generates camera - LiDAR aligned pseudo-labels at a speed orders of magnitude faster than human annotation while preserving VFM-derived semantic fidelity in point cloud representations. We conduct extensive experiments on the constructed Waymo-4DSeg , which demonstrate the powerful cross-modal segmentation ability and great potential in data annotation of proposed SAM4D.

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.21547
• PDF: https://arxiv.org/pdf/2506.21547
• Project Page: https://SAM4D-Project.github.io
• Github: https://github.com/CN-ADLab/SAM4D

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

🔹 Title:
Aligned Novel View Image and Geometry Synthesis via Cross-modal Attention Instillation

🔹 Publication Date: Published on Jun 13

🔹 Abstract:
A diffusion-based framework generates aligned novel views of images and geometry using warping-and-inpainting with cross-modal attention distillation and proximity-based mesh conditioning, achieving high-fidelity synthesis and 3D completion. AI-generated summary We introduce a diffusion-based framework that performs aligned novel view image and geometry generation via a warping-and-inpainting methodology. Unlike prior methods that require dense posed images or pose-embedded generative models limited to in-domain views, our method leverages off-the-shelf geometry predictors to predict partial geometries viewed from reference images, and formulates novel-view synthesis as an inpainting task for both image and geometry. To ensure accurate alignment between generated images and geometry, we propose cross-modal attention distillation , where attention maps from the image diffusion branch are injected into a parallel geometry diffusion branch during both training and inference. This multi-task approach achieves synergistic effects, facilitating geometrically robust image synthesis as well as well-defined geometry prediction . We further introduce proximity-based mesh conditioning to integrate depth and normal cues, interpolating between point cloud and filtering erroneously predicted geometry from influencing the generation process. Empirically, our method achieves high-fidelity extrapolative view synthesis on both image and geometry across a range of unseen scenes, delivers competitive reconstruction quality under interpolation settings, and produces geometrically aligned colored point clouds for comprehensive 3D completion . Project page is available at https://cvlab-kaist.github.io/MoAI.

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.11924
• PDF: https://arxiv.org/pdf/2506.11924

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

🔹 Title:
FairyGen: Storied Cartoon Video from a Single Child-Drawn Character

🔹 Publication Date: Published on Jun 26

🔹 Abstract:
FairyGen generates story-driven cartoon videos from a single drawing by disentangling character modeling and background styling, employing MLLM for storyboards, style propagation for consistency, and MMDiT-based diffusion models for motion. AI-generated summary We propose FairyGen, an automatic system for generating story-driven cartoon videos from a single child's drawing, while faithfully preserving its unique artistic style. Unlike previous storytelling methods that primarily focus on character consistency and basic motion, FairyGen explicitly disentangles character modeling from stylized background generation and incorporates cinematic shot design to support expressive and coherent storytelling. Given a single character sketch, we first employ an MLLM to generate a structured storyboard with shot-level descriptions that specify environment settings, character actions, and camera perspectives. To ensure visual consistency, we introduce a style propagation adapter that captures the character's visual style and applies it to the background, faithfully retaining the character's full visual identity while synthesizing style-consistent scenes. A shot design module further enhances visual diversity and cinematic quality through frame cropping and multi-view synthesis based on the storyboard . To animate the story, we reconstruct a 3D proxy of the character to derive physically plausible motion sequences, which are then used to fine-tune an MMDiT-based image-to-video diffusion model. We further propose a two-stage motion customization adapter: the first stage learns appearance features from temporally unordered frames, disentangling identity from motion; the second stage models temporal dynamics using a timestep-shift strategy with frozen identity weights. Once trained, FairyGen directly renders diverse and coherent video scenes aligned with the storyboard . Extensive experiments demonstrate that our system produces animations that are stylistically faithful, narratively structured natural motion, highlighting its potential for personalized and engaging story animation. The code will be available at https://github.com/GVCLab/FairyGen

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.21272
• PDF: https://arxiv.org/pdf/2506.21272
• Project Page: https://jayleejia.github.io/FairyGen/
• Github: https://github.com/GVCLab/FairyGen

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

🔹 Title:
Seedance 1.0: Exploring the Boundaries of Video Generation Models

🔹 Publication Date: Published on Jun 10

🔹 Abstract:
Seedance 1.0 offers high-performance video generation by integrating advanced data curation, efficient architecture, post-training optimization, and model acceleration, resulting in superior quality and speed. AI-generated summary Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously balancing prompt following, motion plausibility, and visual quality. In this report, we introduce Seedance 1.0, a high-performance and inference-efficient video foundation generation model that integrates several core technical improvements: (i) multi-source data curation augmented with precision and meaningful video captioning , enabling comprehensive learning across diverse scenarios; (ii) an efficient architecture design with proposed training paradigm , which allows for natively supporting multi-shot generation and jointly learning of both text-to-video and image-to-video tasks. (iii) carefully-optimized post-training approaches leveraging fine-grained supervised fine-tuning , and video-specific RLHF with multi-dimensional reward mechanisms for comprehensive performance improvements; (iv) excellent model acceleration achieving ~10x inference speedup through multi-stage distillation strategies and system-level optimizations. Seedance 1.0 can generate a 5-second video at 1080p resolution only with 41.4 seconds (NVIDIA-L20). Compared to state-of-the-art video generation models, Seedance 1.0 stands out with high-quality and fast video generation having superior spatiotemporal fluidity with structural stability , precise instruction adherence in complex multi-subject contexts, native multi-shot narrative coherence with consistent subject representation .

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.09113
• PDF: https://arxiv.org/pdf/2506.09113
• Project Page: https://seed.bytedance.com/seedance

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

Article Title:
Large Language Models for Cyber Security: A Systematic Literature Review

Article Date: 8 May 2024

Article Description:
The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in various domains, including cybersecurity. As the volume and sophistication of cyber threats continue to grow, there is an increasing need for intelligent systems that can automatically detect vulnerabilities, analyze malware, and respond to attacks. In this survey, we conduct a comprehensive review of the literature on the application of LLMs in cybersecurity (LLM4Security). By comprehensively collecting over 30K relevant papers and systematically analyzing 127 papers from top security and software engineering venues, we aim to provide a holistic view of how LLMs are being used to solve diverse problems across the cybersecurity domain. Through our analysis, we identify several key findings. First, we observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. Second, we find that the datasets used for training and evaluating LLMs in these tasks are often limited in size and diversity, highlighting the need for more comprehensive and representative datasets. Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training. Finally, we discuss the main challenges and opportunities for future research in LLM4Security, including the need for more interpretable and explainable models, the importance of addressing data privacy and security concerns, and the potential for leveraging LLMs for proactive defense and threat hunting. Overall, our survey provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2405.04760v4.pdf

GitHub:
• https://github.com/hiyouga/llama-efficient-tuning

Datasets:
• 10,000 People - Human Pose Recognition Data
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

🔹 Title:
PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework

🔹 Publication Date: Published on Jun 12

🔹 Abstract:
PosterCraft improves aesthetic poster generation through a unified, modular pipeline with enhanced text rendering, region-aware fine-tuning, aesthetic reinforcement learning, and joint vision-language refinement. AI-generated summary Generating aesthetic posters is more challenging than simple design images: it requires not only precise text rendering but also the seamless integration of abstract artistic content, striking layouts, and overall stylistic harmony. To address this, we propose PosterCraft, a unified framework that abandons prior modular pipelines and rigid, predefined layouts, allowing the model to freely explore coherent, visually compelling compositions. PosterCraft employs a carefully designed, cascaded workflow to optimize the generation of high-aesthetic posters: (i) large-scale text-rendering optimization on our newly introduced Text-Render-2M dataset; (ii) region-aware supervised fine-tuning on HQ-Poster100K ; (iii) aesthetic-text-reinforcement learning via best-of-n preference optimization ; and (iv) joint vision-language feedback refinement. Each stage is supported by a fully automated data-construction pipeline tailored to its specific needs, enabling robust training without complex architectural modifications. Evaluated on multiple experiments, PosterCraft significantly outperforms open-source baselines in rendering accuracy, layout coherence, and overall visual appeal-approaching the quality of SOTA commercial systems. Our code, models, and datasets can be found in the Project page: https://ephemeral182.github.io/PosterCraft

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.10741
• PDF: https://arxiv.org/pdf/2506.10741
• Project Page: https://ephemeral182.github.io/PosterCraft/
• Github: https://ephemeral182.github.io/PosterCraft

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

🔹 Title:
Scaling Reasoning without Attention

🔹 Publication Date: Published on May 28

🔹 Abstract:
A new attention-free language model using state space dual layers and a two-phase curriculum fine-tuning strategy outperforms Transformer models on complex reasoning tasks. AI-generated summary Large language models ( LLMs ) have made significant advances in complex reasoning tasks, yet they remain bottlenecked by two core challenges: architectural inefficiency due to reliance on Transformers , and a lack of structured fine-tuning for high-difficulty domains. We introduce \ourmodel, an attention-free language model that addresses both issues through architectural and data-centric innovations. Built on the state space dual (SSD) layers of Mamba-2 , our model eliminates the need for self-attention and key-value caching, enabling fixed-memory, constant-time inference . To train it for complex reasoning, we propose a two-phase curriculum fine-tuning strategy based on the PromptCoT synthesis paradigm , which generates pedagogically structured problems via abstract concept selection and rationale-guided generation. On benchmark evaluations, \ourmodel-7B outperforms strong Transformer and hybrid models of comparable scale, and even surpasses the much larger Gemma3-27B by 2.6\% on AIME 24 , 0.6\% on AIME 25 , and 3.0\% on Livecodebench . These results highlight the potential of state space models as efficient and scalable alternatives to attention-based architectures for high-capacity reasoning .

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2505.22425
• PDF: https://arxiv.org/pdf/2505.22425

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

Article Title:
Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents

Article Date: 17 May 2025

Article Description:
Large Language Model (LLM)-based search agents have shown remarkable capabilities in solving complex tasks by dynamically decomposing problems and addressing them through interleaved reasoning and retrieval. However, this interleaved paradigm introduces substantial efficiency bottlenecks. First, we observe that both highly accurate and overly approximate retrieval methods degrade system efficiency: exact search incurs significant retrieval overhead, while coarse retrieval requires additional reasoning steps during generation. Second, we identify inefficiencies in system design, including improper scheduling and frequent retrieval stalls, which lead to cascading latency -- where even minor delays in retrieval amplify end-to-end inference time. To address these challenges, we introduce SearchAgent-X, a high-efficiency inference framework for LLM-based search agents. SearchAgent-X leverages high-recall approximate retrieval and incorporates two key techniques: priority-aware scheduling and non-stall retrieval. Extensive experiments demonstrate that SearchAgent-X consistently outperforms state-of-the-art systems such as vLLM and HNSW-based retrieval across diverse tasks, achieving up to 3.4$\times$ higher throughput and 5$\times$ lower latency, without compromising generation quality. SearchAgent-X is available at https://github.com/tiannuo-yang/SearchAgent-X.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2505.12065v1.pdf

GitHub:
• https://github.com/tiannuo-yang/searchagent-x

Datasets:
• No datasets information available
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

🔹 Title:
Sekai: A Video Dataset towards World Exploration

🔹 Publication Date: Published on Jun 18

🔹 Abstract:
Sekai, a worldwide video dataset with comprehensive annotations, is introduced to support world exploration applications, enhancing video generation models. AI-generated summary Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well-suited for world exploration training as they suffer from some limitations: limited locations, short duration, static scenes, and a lack of annotations about exploration and the world. In this paper, we introduce Sekai (meaning ``world'' in Japanese), a high-quality first-person view worldwide video dataset with rich annotations for world exploration. It consists of over 5,000 hours of walking or drone view ( FPV and UVA ) videos from over 100 countries and regions across 750 cities. We develop an efficient and effective toolbox to collect, pre-process and annotate videos with location, scene, weather, crowd density, captions, and camera trajectories . Experiments demonstrate the quality of the dataset. And, we use a subset to train an interactive video world exploration model , named YUME (meaning ``dream'' in Japanese). We believe Sekai will benefit the area of video generation and world exploration, and motivate valuable applications.

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.15675
• PDF: https://arxiv.org/pdf/2506.15675
• Project Page: https://huggingface.co/datasets/Lixsp11/Sekai-Project
• Github: https://github.com/Lixsp11/sekai-codebase

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/Lixsp11/Sekai-Project

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

Article Title:
MUSt3R: Multi-view Network for Stereo 3D Reconstruction

Article Date: Yohann Cabon

Article Description:
DUSt3R introduced a novel paradigm in geometric computer vision by proposing a model that can provide dense and unconstrained Stereo 3D Reconstruction of arbitrary image collections with no prior information about camera calibration nor viewpoint poses. Under the hood, however, DUSt3R processes image pairs, regressing local 3D reconstructions that need to be aligned in a global coordinate system. The number of pairs, growing quadratically, is an inherent limitation that becomes especially concerning for robust and fast optimization in the case of large image collections. In this paper, we propose an extension of DUSt3R from pairs to multiple views, that addresses all aforementioned concerns. Indeed, we propose a Multi-view Network for Stereo 3D Reconstruction, or MUSt3R, that modifies the DUSt3R architecture by making it symmetric and extending it to directly predict 3D structure for all views in a common coordinate frame. Second, we entail the model with a multi-layer memory mechanism which allows to reduce the computational complexity and to scale the reconstruction to large collections, inferring thousands of 3D pointmaps at high frame-rates with limited added complexity. The framework is designed to perform 3D reconstruction both offline and online, and hence can be seamlessly applied to SfM and visual SLAM scenarios showing state-of-the-art performance on various 3D downstream tasks, including uncalibrated Visual Odometry, relative camera pose, scale and focal estimation, 3D reconstruction and multi-view depth estimation.PDFAbstractCVPR 2025 PDFCVPR 2025 Abstract

PDF Download Link:
https://arxiv.org/pdf/2503.01661v1.pdf

GitHub:
• https://github.com/naver/must3r

Datasets:
• KITTI
• ScanNet
• TUM RGB-D
• MegaDepth
• ETH3D
• BlendedMVS
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

Article Title:
Spiking Graph Convolutional Networks

Article Date: 5 May 2022

Article Description:
Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be deployed on battery-powered devices. In contrast, Spiking Neural Networks (SNNs), which perform a bio-fidelity inference process, offer an energy-efficient neural architecture. In this work, we propose SpikingGCN, an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs. The original graph data are encoded into spike trains based on the incorporation of graph convolution. We further model biological information processing by utilizing a fully connected layer combined with neuron nodes. In a wide range of scenarios (e.g. citation networks, image graph classification, and recommender systems), our experimental results show that the proposed method could gain competitive performance against state-of-the-art approaches. Furthermore, we show that SpikingGCN on a neuromorphic chip can bring a clear advantage of energy efficiency into graph data analysis, which demonstrates its great potential to construct environment-friendly machine learning models.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2205.02767v2.pdf

GitHub:
• https://github.com/zulunzhu/spikinggcn

Datasets:
• MNIST
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

🔹 Title:
Unified Vision-Language-Action Model

🔹 Publication Date: Published on Jun 24

🔹 Abstract:
UniVLA is a multimodal VLA model that autoregressively processes vision, language, and action as token sequences, incorporating world modeling for effective long-horizon policy learning and achieving state-of-the-art results across simulation and real-world benchmarks. AI-generated summary Vision-language-action models ( VLAs ) have garnered significant attention for their potential in advancing robotic manipulation. However, previous approaches predominantly rely on the general comprehension capabilities of vision-language models ( VLMs ) to generate action signals, often overlooking the rich temporal and causal structure embedded in visual observations. In this paper, we present UniVLA, a unified and native multimodal VLA model that autoregressively models vision, language, and action signals as discrete token sequences . This formulation enables flexible multimodal tasks learning , particularly from large-scale video data. By incorporating world modeling during post-training, UniVLA captures causal dynamics from videos, facilitating effective transfer to downstream policy learning --especially for long-horizon tasks. Our approach sets new state-of-the-art results across several widely used simulation benchmarks, including CALVIN , LIBERO , and Simplenv-Bridge , significantly surpassing previous methods. For example, UniVLA achieves 95.5% average success rate on LIBERO benchmark, surpassing pi0-FAST's 85.5%. We further demonstrate its broad applicability on real-world ALOHA manipulation and autonomous driving.

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.19850
• PDF: https://arxiv.org/pdf/2506.19850

🔹 Datasets citing this paper:
No datasets found

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

🔹 Title:
Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content

🔹 Publication Date: Published on Jun 25

🔹 Abstract:
A biomedical text dataset, constructed from PubMed, uses a two-stage annotation process involving large and small language models to fine-tune and extract subsets for clinical NLP, improving pretraining efficiency and performance. AI-generated summary We introduce Biomed-Enriched , a biomedical text dataset constructed from PubMed via a two-stage annotation process. In the first stage, a large language model annotates 400K paragraphs from PubMed scientific articles, assigning scores for their type (review, study, clinical case, other), domain (clinical, biomedical, other), and educational quality . The educational quality score (rated 1 to 5) estimates how useful a paragraph is for college-level learning. These annotations are then used to fine-tune a small language model , which propagates the labels across the full PMC-OA corpus . The resulting metadata allows us to extract refined subsets, including 2M clinical case paragraphs with over 450K high-quality ones from articles with commercial-use licenses, and to construct several variants via quality filtering and domain upsampling. Clinical text is typically difficult to access due to privacy constraints, as hospital records cannot be publicly shared. Hence, our dataset provides an alternative large-scale, openly available collection of clinical cases from PubMed , making it a valuable resource for biomedical and clinical NLP. Preliminary continual-pretraining experiments with OLMo2 suggest these curated subsets enable targeted improvements, with clinical upsampling boosting performance by ~5% on MMLU ProfMed and educational quality filtering improving MedQA and MedMCQA by ~1%. Combinations of these techniques led to faster convergence, reaching same performance with a third of training tokens , indicating potential for more efficient and effective biomedical pretraining strategies.

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.20331
• PDF: https://arxiv.org/pdf/2506.20331

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/almanach/Biomed-Enriched

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

Article Title:
PixelsDB: Serverless and NL-Aided Data Analytics with Flexible Service Levels and Prices

Article Date: 30 May 2024

Article Description:
Serverless query processing has become increasingly popular due to its advantages, including automated resource management, high elasticity, and pay-as-you-go pricing. For users who are not system experts, serverless query processing greatly reduces the cost of owning a data analytic system. However, it is still a significant challenge for non-expert users to transform their complex and evolving data analytic needs into proper SQL queries and select a serverless query service that delivers satisfactory performance and price for each type of query. This paper presents PixelsDB, an open-source data analytic system that allows users who lack system or SQL expertise to explore data efficiently. It allows users to generate and debug SQL queries using a natural language interface powered by fine-tuned language models. The queries are then executed by a serverless query engine that offers varying prices for different performance service levels (SLAs). The performance SLAs are natively supported by dedicated architecture design and heterogeneous resource scheduling that can apply cost-efficient resources to process non-urgent queries. We demonstrate that the combination of a serverless paradigm, a natural-language-aided interface, and flexible SLAs and prices will substantially improve the usability of cloud data analytic systems.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2405.19784v2.pdf

GitHub:
• https://github.com/pixelsdb/pixels

Datasets:
• No datasets information available
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

Article Title:
Optuna: A Next-generation Hyperparameter Optimization Framework

Article Date: 25 Jul 2019

Article Description:
The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to light-weight experiment conducted via interactive interface. In order to prove our point, we will introduce Optuna, an optimization software which is a culmination of our effort in the development of a next generation optimization software. As an optimization software designed with define-by-run principle, Optuna is particularly the first of its kind. We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications. Our software is available under the MIT license (https://github.com/pfnet/optuna/).PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/1907.10902v1.pdf

GitHub:
• https://github.com/pfnet/optuna
• https://github.com/optuna/optuna
• https://github.com/Automunge/AutoMunge
• https://github.com/optuna/optuna-integration
• https://github.com/crcrpar/benchmark-runner-ci
• https://github.com/crcrpar/optuna-mirror
• https://github.com/himkt/optuna-test-rtds
• https://github.com/crcrpar/ci-example-execution
• https://github.com/yqian4/optuna
• https://github.com/brethvoice/optuna_demo_MNIST
• https://github.com/rickyHong/optuna-repl

Datasets:
• No datasets information available
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

🔹 Title:
OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling

🔹 Publication Date: Published on Jun 25

🔹 Abstract:
Investigating mid-training strategies reveals that high-quality mathematical corpora and well-formatted chain-of-thought reasoning examples enhance reinforcement learning performance in language models, leading to the development of OctoThinker. AI-generated summary Different base language model families, such as Llama and Qwen, exhibit divergent behaviors during post-training with reinforcement learning (RL), especially on reasoning-intensive tasks. What makes a base language model suitable for reinforcement learning ? Gaining deeper insight into this question is essential for developing RL-scalable foundation models of the next generation. In this work, we investigate how mid-training strategies shape RL dynamics, focusing on two representative model families: Qwen and Llama. Our study reveals that (1) high-quality mathematical corpora, such as MegaMath-Web-Pro , significantly improve both base model and RL performance, while existing alternatives (e.g., FineMath-4plus) fail to do so; (2) further adding QA-style data , particularly long chain-of-thought (CoT) reasoning examples, enhances RL outcomes, and instruction data further unlocks this effect; (3) while long-CoT improves reasoning depth, it can also induce verbosity of model responses and unstability of RL training, underscoring the importance of data formatting ; (4) scaling mid-training consistently leads to stronger downstream RL performance. Building on these insights, we introduce a two-stage mid-training strategy , Stable-then-Decay, in which base models are first trained on 200B tokens with a constant learning rate, followed by 20B tokens across three CoT-focused branches with learning rate decay . This yields OctoThinker , a family of models demonstrating strong RL compatibility and closing the performance gap with more RL-friendly model families, i.e., Qwen. We hope our work will help shape pre-training strategies for foundation models in the RL era. To support further research, we release our open-source models along with a curated math reasoning-intensive corpus of over 70 billion tokens (i.e., MegaMath-Web-Pro-Max ).

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.20512
• PDF: https://arxiv.org/pdf/2506.20512
• Github: https://github.com/GAIR-NLP/OctoThinker

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/OctoThinker/MegaMath-Web-Pro-Max

🔹 Spaces citing this paper:
No spaces found
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT

Article Title:
pySLAM: An Open-Source, Modular, and Extensible Framework for SLAM

Article Date: 17 Feb 2025

Article Description:
pySLAM is an open-source Python framework for Visual SLAM, supporting monocular, stereo, and RGB-D cameras. It provides a flexible interface for integrating both classical and modern local features, making it adaptable to various SLAM tasks. The framework includes different loop closure methods, a volumetric reconstruction pipeline, and support for depth prediction models. Additionally, it offers a suite of tools for visual odometry and SLAM applications. Designed for both beginners and experienced researchers, pySLAM encourages community contributions, fostering collaborative development in the field of Visual SLAM.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2502.11955v2.pdf

GitHub:
• https://github.com/luigifreda/pyslam

Datasets:
• KITTI
• Replica
• TUM RGB-D
• EuRoC MAV
==================================

For more data science resources:
✓ https://t.iss.one/DataScienceT