🔹 Title: UItron: Foundational GUI Agent with Advanced Perception and Planning

🔹 Publication Date: Published on Aug 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21767
• PDF: https://arxiv.org/pdf/2508.21767

🔹 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: Morae: Proactively Pausing UI Agents for User Choices

🔹 Publication Date: Published on Aug 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21456
• PDF: https://arxiv.org/pdf/2508.21456

🔹 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: AHELM: A Holistic Evaluation of Audio-Language Models

🔹 Publication Date: Published on Aug 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21376
• PDF: https://arxiv.org/pdf/2508.21376
• Project Page: https://crfm.stanford.edu/helm/audio/v1.0.0/

🔹 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: Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models

🔹 Publication Date: Published on Aug 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21365
• PDF: https://arxiv.org/pdf/2508.21365

🔹 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: HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation

🔹 Publication Date: Published on Aug 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.20085
• PDF: https://arxiv.org/pdf/2508.20085
• Project Page: https://gemcollector.github.io/HERMES/
• Github: https://gemcollector.github.io/HERMES/

🔹 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: HPSv3: Towards Wide-Spectrum Human Preference Score

🔹 Publication Date: Published on Aug 5

🔹 Abstract: HPSv3, a human preference score using a wide-spectrum dataset and uncertainty-aware ranking loss, enhances text-to-image generation quality through iterative refinement. AI-generated summary Evaluating text-to-image generation models requires alignment with human perception, yet existing human-centric metrics are constrained by limited data coverage, suboptimal feature extraction, and inefficient loss functions. To address these challenges, we introduce Human Preference Score v3 (HPSv3). (1) We release HPDv3 , the first wide-spectrum human preference dataset integrating 1.08M text-image pairs and 1.17M annotated pairwise comparisons from state-of-the-art generative models and low to high-quality real-world images. (2) We introduce a VLM-based preference model trained using an uncertainty-aware ranking loss for fine-grained ranking. Besides, we propose Chain-of-Human-Preference ( CoHP ), an iterative image refinement method that enhances quality without extra data, using HPSv3 to select the best image at each step. Extensive experiments demonstrate that HPSv3 serves as a robust metric for wide-spectrum image evaluation, and CoHP offers an efficient and human-aligned approach to improve image generation quality . The code and dataset are available at the HPSv3 Homepage.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03789

• PDF: https://arxiv.org/pdf/2508.03789

• Project Page: https://mizzenai.github.io/HPSv3.project/

• Github: https://github.com/MizzenAI/HPSv3

🔹 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: Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://www.arxiv.org/abs/2508.20470
• PDF: https://arxiv.org/pdf/2508.20470
• Github: https://dropletx.github.io/

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/DropletX/Droplet3D-4M

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

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

🔹 Title: CLIPSym: Delving into Symmetry Detection with CLIP

🔹 Publication Date: Published on Aug 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.14197
• PDF: https://arxiv.org/pdf/2508.14197
• Github: https://github.com/timyoung2333/CLIPSym

🔹 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: A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21148
• PDF: https://arxiv.org/pdf/2508.21148
• Github: https://github.com/open-sciencelab/Awesome-Scientific-Datasets-and-LLMs

🔹 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: Model-Task Alignment Drives Distinct RL Outcomes

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21188
• PDF: https://arxiv.org/pdf/2508.21188

🔹 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: Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula Discovery

🔹 Publication Date: Published on Aug 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17380
• PDF: https://arxiv.org/pdf/2508.17380
• Github: https://jiaaqiliu.github.io/VIPER-R1/

🔹 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: Deep Residual Echo State Networks: exploring residual orthogonal connections in untrained Recurrent Neural Networks

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21172
• PDF: https://arxiv.org/pdf/2508.21172
• Github: https://github.com/NennoMP/deepresesn

🔹 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: Quantization Robustness to Input Degradations for Object Detection

🔹 Publication Date: Published on Aug 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19600
• PDF: https://arxiv.org/pdf/2508.19600

🔹 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: EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks

🔹 Publication Date: Published on Aug 23

🔹 Paper Links:
• arXiv Page: https://huggingface.co/collections/yhua219/edurabsa-dataset-68b59bad56a9e1384de7faf2
• PDF: https://arxiv.org/pdf/2508.17008
• Github: https://github.com/yhua219/edurabsa_dataset_and_annotation_tool

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/yhua219/EduRABSA_ASTE
• https://huggingface.co/datasets/yhua219/EduRABSA_AOPE
• https://huggingface.co/datasets/yhua219/EduRABSA_ASQE
• https://huggingface.co/datasets/yhua219/EduRABSA_ACD

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

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

🔹 Title: PVPO: Pre-Estimated Value-Based Policy Optimization for Agentic Reasoning

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.21104
• PDF: https://arxiv.org/pdf/2508.21104

🔹 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: No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes

🔹 Publication Date: Published on Aug 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19060
• PDF: https://arxiv.org/pdf/2508.19060
• Github: https://github.com/blaz-r/SuperSimplenet

🔹 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: SWE-Exp: Experience-Driven Software Issue Resolution

🔹 Publication Date: Published on Jul 31

🔹 Abstract: SWE-Exp enhances software issue resolution by systematically accumulating and leveraging repair expertise from past agent experiences, improving resolution rates. AI-generated summary Recent advances in large language model (LLM) agents have shown remarkable progress in software issue resolution, leveraging advanced techniques such as multi-agent collaboration and Monte Carlo Tree Search (MCTS) . However, current agents act as memoryless explorers - treating each problem separately without retaining or reusing knowledge from previous repair experiences. This leads to redundant exploration of failed trajectories and missed chances to adapt successful issue resolution methods to similar problems. To address this problem, we introduce SWE-Exp, an experience - enhanced approach that distills concise and actionable experience from prior agent trajectories, enabling continuous learning across issues. Our method introduces a multi-faceted experience bank that captures both successful and failed repair attempts. Specifically, it extracts reusable issue resolution knowledge at different levels - from high-level problem comprehension to specific code changes. Experiments show that SWE-Exp achieves state-of-the-art resolution rate (41.6% Pass@1) on SWE-bench-Verified under open-source agent frameworks . Our approach establishes a new paradigm in which automated software engineering agents systematically accumulate and leverage repair expertise, fundamentally shifting from trial-and-error exploration to strategic, experience-driven issue resolution.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.23361

• PDF: https://arxiv.org/pdf/2507.23361

• Github: https://github.com/YerbaPage/SWE-Exp

🔹 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: How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on τ-bench

🔹 Publication Date: Published on Aug 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.20931
• PDF: https://arxiv.org/pdf/2508.20931

🔹 Datasets citing this paper:
No datasets found

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

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