Data science research papers

Orca: The World is in Your Mind 📅 Publication Date: Jun 29, 2026 📑 Paper: https://arxiv.org/pdf/2606.30534.pdf 🔗 Code: https://github.com/huggingface 📝 Description: Orca establishes a unified world latent space through next-state-prediction modeling using multimodal data and demonstrates superior performance in downstream tasks compared to specialized baselines.

Grouped Query Experts: Mixture-of-Experts on GQA Self-Attention 📅 Publication Date: Jun 18, 2026 📑 Paper: https://arxiv.org/pdf/2606.20945.pdf 🔗 Code: https://github.com/huggingface 📝 Description: Grouped Query Experts (GQE) improves Transformer efficiency by selectively activating query heads based on token content while maintaining key-value cache benefits of grouped-query attention.

PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystems 📅 Publication Date: Jun 21, 2026 📑 Paper: https://arxiv.org/pdf/2606.22388.pdf 🔗 Code: https://github.com/huggingface 📝 Description: PlanBench-XL evaluates large language model agents' ability to plan and adapt in complex tool-rich environments with limited visibility and dynamic disruptions.

Are We Ready For An Agent-Native Memory System? 📅 Publication Date: Jun 23, 2026 📑 Paper: https://arxiv.org/pdf/2606.24775.pdf 🔗 Code: https://github.com/huggingface 📝 Description: Large language model agents' memory systems have evolved into complex data management frameworks requiring systematic evaluation across multiple modules and workloads to understand their performance characteristics and trade-offs.

EnterpriseClawBench: Benchmarking Agents from Real Workplace Sessions 📅 Publication Date: Jun 22, 2026 📑 Paper: https://arxiv.org/pdf/2606.23654.pdf 🔗 Code: https://github.com/huggingface 📝 Description: EnterpriseClawBench presents a benchmark for enterprise agents based on real-world sessions with 852 reproducible tasks, emphasizing comprehensive evaluation metrics beyond single performance scores.

OpenRath: Session-Centered Runtime State for Agent Systems 📅 Publication Date: Jun 17, 2026 📑 Paper: https://arxiv.org/pdf/2606.19409.pdf 🔗 Code: https://github.com/huggingface 📝 Description: OpenRath introduces a PyTorch-like programming model for multi-agent systems using Session as a central runtime abstraction that enables explicit fork, merge, and replay operations while recording comprehensive execution state.

MemSlides: A Hierarchical Memory Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision 📅 Publication Date: Jun 15, 2026 📑 Paper: https://arxiv.org/pdf/2606.17162.pdf 🔗 Code: https://github.com/huggingface 📝 Description: MemSlides presents a hierarchical memory framework for personalized presentation agents that separates long-term user profiles, working memory for session constraints, and tool memory for reusable execution experiences to enable stable personalization and reliable local edits.

Formalizing Latent Thoughts: Four Axioms of Thought Representation in LLMs 📅 Publication Date: May 7, 2026 📑 Paper: https://arxiv.org/pdf/2606.27378.pdf 🔗 Code: N/A 📝 Description: An axiomatic evaluation framework reveals systematic failures in latent thought representations of LLMs across multiple reasoning tasks, demonstrating that current representations fail to satisfy fundamental functional axioms consistently across different model architectures.

EnterpriseClawBench: Benchmarking Agents from Real Workplace Sessions 📅 Publication Date: Jun 22, 2026 📑 Paper: https://arxiv.org/pdf/2606.23654.pdf 🔗 Code: https://github.com/huggingface 📝 Description: EnterpriseClawBench presents a benchmark for enterprise agents based on real-world sessions with 852 reproducible tasks, emphasizing comprehensive evaluation metrics beyond single performance scores.

Heterogeneous Scientific Foundation Model Collaboration 📅 Publication Date: Apr 30, 2026 📑 Paper: https://arxiv.org/pdf/2604.27351.pdf 🔗 Code: https://github.com/Violet24K/Eywa 📝 Description: Eywa is a heterogeneous agentic framework that extends language-centric systems to scientific foundation models by integrating domain-specific models with language-based reasoning interfaces for improved performance across diverse scientific domains.

OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents 📅 Publication Date: May 6, 2026 📑 Paper: https://arxiv.org/pdf/2605.05185.pdf 🔗 Code: https://github.com/shawn0728/OpenSearch-VL 📝 Description: OpenSearch-VL presents an open-source framework for training advanced multimodal search agents using reinforcement learning, featuring specialized data curation, diverse tool environments, and a novel training algorithm that improves performance across multiple benchmarks.

WorldOlympiad: Can Your World Model Survive a Triathlon? 📅 Publication Date: Jun 9, 2026 📑 Paper: https://arxiv.org/pdf/2606.11129 💻 Project Page: https://alibaba-damo-academy.github.io/WorldOlympiad/ 📝 Description: The paper introduces WorldOlympiad, a comprehensive benchmark for evaluating video-based world models. The problem with current generative models is that they often focus on visual quality, but lack physical faithfulness, geometric consistency, and interaction fidelity. To address this gap, WorldOlympiad decomposes world-model evaluation into three dimensions: physical faithfulness, geometric consistency, and interaction fidelity. WorldOlympiad covers three major downstream scenarios, including gaming, robotics, and general real-world videos, capturing diverse challenges from interactive control and embodied manipulation to open-domain motion and camera dynamics. #WorldModelEvaluation #VideoBasedWorldModels #PhysicalFaithfulness #GeometricConsistency

Qwen-AgentWorld: Language World Models for General Agents 📅 Publication Date: Jun 23, 2026 📑 Paper: https://arxiv.org/pdf/2606.24597.pdf 🔗 Code: https://github.com/huggingface 📝 Description: Language-based world models enable agentic environment simulation across multiple domains and enhance general agent performance through scalable simulation and improved downstream task performance.

HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation 📅 Publication Date: Apr 30, 2026 📑 Paper: https://arxiv.org/pdf/2604.28196.pdf 🔗 Code: https://github.com/H-EmbodVis/HERMESV2 📝 Description: HERMES++ combines 3D scene understanding and future geometry prediction through BEV representation, LLM-enhanced queries, temporal linking, and joint geometric optimization for autonomous driving applications.

ABot-Earth 0.5: Generative 3D Earth Model 📅 Publication Date: Jun 8, 2026 📑 Paper: https://arxiv.org/pdf/2606.09967 💻 Project Page: https://abot-earth.amap.com/ 📝 Description: The paper presents ABot-Earth 0.5, a generative framework that creates realistic 3D environments from satellite imagery. The problem addressed is the need for large-scale 3D reconstruction, which is currently expensive and technically challenging. The authors propose a novel generative model based on 3D Gaussian Splatting representation, which is trained on a diverse set of real-world urban reconstructions. This model learns to generate realistic geometry and textures, and can synthesize novel 3D scenes conditioned solely on satellite imagery in under 10 minutes per square kilometer. #Generative3DModeling #3DGaussianSplatting #SatelliteImageryReconstruction #GeospatialModeling

OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories 📅 Publication Date: May 5, 2026 📑 Paper: https://arxiv.org/pdf/2605.04036.pdf 🔗 Code: https://github.com/PolarSeeker/OpenSeeker 📝 Description: A simple supervised fine-tuning approach achieves state-of-the-art performance in deep search capabilities using minimal data, outperforming complex industrial pipelines and demonstrating the effectiveness of academic-led development in large language model agents.

World Model for Robot Learning: A Comprehensive Survey 📅 Publication Date: Apr 30, 2026 📑 Paper: https://arxiv.org/pdf/2605.00080 💻 Project Page: https://ntumars.github.io/wm-robot-survey/ 🔗 Code: https://github.com/NTUMARS/Awesome-World-Model-for-Robotics-Policy 📝 Description: The paper provides a comprehensive survey of world models for robot learning, which are predictive representations of environmental dynamics that support policy learning, planning, and simulation. The authors note that the literature on world models is fragmented across different architectures, functional roles, and application domains, making it difficult to understand the current state of the field. To address this gap, the authors present a systematic review of world models from a robot learning perspective, examining how they are coupled with robot policies, used as learned simulators for reinforcement learning and evaluation, and have progressed in terms of robotic video world models. #RobotLearning #RobotPolicies

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond 📅 Publication Date: Apr 24, 2026 📑 Paper: https://arxiv.org/pdf/2604.22748.pdf 🔗 Code: https://github.com/matrix-agent/awesome-agentic-world-modeling 📝 Description: World models are categorized into three capability levels and four law regimes to better understand and develop predictive environment models for AI agents across diverse domains.

Asymmetric Flow Models 📅 Publication Date: May 13, 2026 📑 Paper: https://arxiv.org/pdf/2605.12964 💻 Project Page: https://hanshengchen.com/asymflow/ 🔗 Code: https://github.com/Lakonik/LakonLab ⭐️ 324 📊 Models citing this paper: • https://huggingface.co/Lakonik/AsymFLUX.2-klein-9B • https://huggingface.co/Lakonik/AsymFlow-ImageNet • https://huggingface.co/OJ-1/AsymFLUX.2-klein-9B 📝 Description: The paper introduces Asymmetric Flow Modeling, a method for efficient high-dimensional flow-based generation. The problem with existing flow-based generation methods is that they require modeling high-dimensional noise, which is difficult even when the data has a strong low-rank structure. To address this, the authors propose a rank-asymmetric velocity parameterization that restricts noise prediction to a low-rank subspace while keeping data prediction full-dimensional. #AsymmetricFlowModels #FlowBasedGeneration #RankAsymmetricVelocity