πΉ Title: Hermes 4 Technical Report
πΉ Publication Date: Published on Aug 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18255
β’ PDF: https://arxiv.org/pdf/2508.18255
β’ Project Page: https://hermes4.nousresearch.com/
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18255
β’ PDF: https://arxiv.org/pdf/2508.18255
β’ Project Page: https://hermes4.nousresearch.com/
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
β€1
πΉ Title: Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing
πΉ Publication Date: Published on Aug 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.17326
β’ PDF: https://arxiv.org/pdf/2508.17326
β’ Github: https://github.com/tristan-deep/semantic-diffusion-echo-dehazing
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.17326
β’ PDF: https://arxiv.org/pdf/2508.17326
β’ Github: https://github.com/tristan-deep/semantic-diffusion-echo-dehazing
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Title: Understanding Tool-Integrated Reasoning
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.19201
β’ PDF: https://arxiv.org/pdf/2508.19201
β’ Project Page: https://zhongwenxu.notion.site/Understanding-Tool-Integrated-Reasoning-2551c4e140e3805489fadcc802a1ea83
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.19201
β’ PDF: https://arxiv.org/pdf/2508.19201
β’ Project Page: https://zhongwenxu.notion.site/Understanding-Tool-Integrated-Reasoning-2551c4e140e3805489fadcc802a1ea83
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Title: Spacer: Towards Engineered Scientific Inspiration
πΉ Publication Date: Published on Aug 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.17661
β’ PDF: https://arxiv.org/pdf/2508.17661
β’ Github: https://github.com/asteromorph-corp/spacer
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.17661
β’ PDF: https://arxiv.org/pdf/2508.17661
β’ Github: https://github.com/asteromorph-corp/spacer
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Title: VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.19247
β’ PDF: https://arxiv.org/pdf/2508.19247
β’ Project Page: https://huanngzh.github.io/VoxHammer-Page/
β’ Github: https://github.com/Nelipot-Lee/VoxHammer/Edit3D-Bench
πΉ Datasets citing this paper:
β’ https://huggingface.co/datasets/huanngzh/Edit3D-Bench
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.19247
β’ PDF: https://arxiv.org/pdf/2508.19247
β’ Project Page: https://huanngzh.github.io/VoxHammer-Page/
β’ Github: https://github.com/Nelipot-Lee/VoxHammer/Edit3D-Bench
πΉ Datasets citing this paper:
β’ https://huggingface.co/datasets/huanngzh/Edit3D-Bench
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
β€1
πΉ Title: Unraveling the cognitive patterns of Large Language Models through module communities
πΉ Publication Date: Published on Aug 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18192
β’ PDF: https://arxiv.org/pdf/2508.18192
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18192
β’ PDF: https://arxiv.org/pdf/2508.18192
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
β€2
ML Research Hub pinned Β«π Searching for fast, reliable proxies for your data science and machine learning projects? Thordata provides the perfect solution for all your data scraping needs! π https://www.thordata.com/?ls=DhthVzyG&lk=Data β¨ Why Choose Thordata? β
Rotating & Stickyβ¦Β»
πΉ Title: OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.19209
β’ PDF: https://arxiv.org/pdf/2508.19209
β’ Project Page: https://omnihuman-lab.github.io/v1_5/
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.19209
β’ PDF: https://arxiv.org/pdf/2508.19209
β’ Project Page: https://omnihuman-lab.github.io/v1_5/
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
β€1
πΉ Title: UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18756
β’ PDF: https://arxiv.org/pdf/2508.18756
β’ Github: https://github.com/ZihaoHuang-notabot/Ultra-Sparse-Memory-Network
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18756
β’ PDF: https://arxiv.org/pdf/2508.18756
β’ Github: https://github.com/ZihaoHuang-notabot/Ultra-Sparse-Memory-Network
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Title: CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics
πΉ Publication Date: Published on Aug 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18124
β’ PDF: https://arxiv.org/pdf/2508.18124
β’ Github: https://github.com/CMPhysBench/CMPhysBench%5D
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18124
β’ PDF: https://arxiv.org/pdf/2508.18124
β’ Github: https://github.com/CMPhysBench/CMPhysBench%5D
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Title: TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling
πΉ Publication Date: Published on Aug 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.17445
β’ PDF: https://arxiv.org/pdf/2508.17445
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.17445
β’ PDF: https://arxiv.org/pdf/2508.17445
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Title: Wan-S2V: Audio-Driven Cinematic Video Generation
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18621
β’ PDF: https://arxiv.org/pdf/2508.18621
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18621
β’ PDF: https://arxiv.org/pdf/2508.18621
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Title: QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting
πΉ Publication Date: Published on Aug 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.16697
β’ PDF: https://arxiv.org/pdf/2508.16697
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.16697
β’ PDF: https://arxiv.org/pdf/2508.16697
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Title: ThinkDial: An Open Recipe for Controlling Reasoning Effort in Large Language Models
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2502.18080
β’ PDF: https://arxiv.org/pdf/2508.18773
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2502.18080
β’ PDF: https://arxiv.org/pdf/2508.18773
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
β€1
πΉ Title: Training Language Model Agents to Find Vulnerabilities with CTF-Dojo
πΉ Publication Date: Published on Aug 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.00910
β’ PDF: https://arxiv.org/pdf/2508.18370
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.00910
β’ PDF: https://arxiv.org/pdf/2508.18370
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
β€1
πΉ Title: FastMesh:Efficient Artistic Mesh Generation via Component Decoupling
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.19188
β’ PDF: https://arxiv.org/pdf/2508.19188
β’ Project Page: https://jhkim0759.github.io/projects/FastMesh/
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.19188
β’ PDF: https://arxiv.org/pdf/2508.19188
β’ Project Page: https://jhkim0759.github.io/projects/FastMesh/
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
β€1
πΉ Title: Evaluating, Synthesizing, and Enhancing for Customer Support Conversation
πΉ Publication Date: Published on Aug 6
πΉ Abstract: A structured framework and datasets for training customer service agents using well-defined support strategies improve the quality of customer support interactions and problem resolution. AI-generated summary Effective customer support requires not only accurate problem solving but also structured and empathetic communication aligned with professional standards. However, existing dialogue datasets often lack strategic guidance, and real-world service data is difficult to access and annotate. To address this, we introduce the task of Customer Support Conversation (CSC), aimed at training customer service agents to respond using well-defined support strategies. We propose a structured CSC framework grounded in COPC guidelines , defining five conversational stages and twelve strategies to guide high-quality interactions. Based on this, we construct CSConv , an evaluation dataset of 1,855 real-world customer-agent conversations rewritten using LLMs to reflect deliberate strategy use, and annotated accordingly. Additionally, we develop a role-playing approach that simulates strategy-rich conversations using LLM-powered roles aligned with the CSC framework, resulting in the training dataset RoleCS . Experiments show that fine-tuning strong LLMs on RoleCS significantly improves their ability to generate high-quality, strategy-aligned responses on CSConv . Human evaluations further confirm gains in problem resolution. All code and data will be made publicly available at https://github.com/aliyun/qwen-dianjin.
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.04423
β’ PDF: https://arxiv.org/pdf/2508.04423
β’ Github: https://github.com/aliyun/qwen-dianjin
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 6
πΉ Abstract: A structured framework and datasets for training customer service agents using well-defined support strategies improve the quality of customer support interactions and problem resolution. AI-generated summary Effective customer support requires not only accurate problem solving but also structured and empathetic communication aligned with professional standards. However, existing dialogue datasets often lack strategic guidance, and real-world service data is difficult to access and annotate. To address this, we introduce the task of Customer Support Conversation (CSC), aimed at training customer service agents to respond using well-defined support strategies. We propose a structured CSC framework grounded in COPC guidelines , defining five conversational stages and twelve strategies to guide high-quality interactions. Based on this, we construct CSConv , an evaluation dataset of 1,855 real-world customer-agent conversations rewritten using LLMs to reflect deliberate strategy use, and annotated accordingly. Additionally, we develop a role-playing approach that simulates strategy-rich conversations using LLM-powered roles aligned with the CSC framework, resulting in the training dataset RoleCS . Experiments show that fine-tuning strong LLMs on RoleCS significantly improves their ability to generate high-quality, strategy-aligned responses on CSConv . Human evaluations further confirm gains in problem resolution. All code and data will be made publicly available at https://github.com/aliyun/qwen-dianjin.
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.04423
β’ PDF: https://arxiv.org/pdf/2508.04423
β’ Github: https://github.com/aliyun/qwen-dianjin
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Title: Autoregressive Universal Video Segmentation Model
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.19242
β’ PDF: https://arxiv.org/pdf/2508.19242
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.19242
β’ PDF: https://arxiv.org/pdf/2508.19242
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Title: MovieCORE: COgnitive REasoning in Movies
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.19026
β’ PDF: https://arxiv.org/pdf/2508.19026
β’ Github: https://joslefaure.github.io/assets/html/moviecore.html
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.19026
β’ PDF: https://arxiv.org/pdf/2508.19026
β’ Github: https://joslefaure.github.io/assets/html/moviecore.html
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Title: ObjFiller-3D: Consistent Multi-view 3D Inpainting via Video Diffusion Models
πΉ Publication Date: Published on Aug 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18271
β’ PDF: https://arxiv.org/pdf/2508.18271
β’ Project Page: https://objfiller3d.github.io/
β’ Github: https://github.com/objfiller3d/ObjFiller-3D
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18271
β’ PDF: https://arxiv.org/pdf/2508.18271
β’ Project Page: https://objfiller3d.github.io/
β’ Github: https://github.com/objfiller3d/ObjFiller-3D
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
β€1
πΉ Title: Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18672
β’ PDF: https://arxiv.org/pdf/2508.18672
β’ Github: https://github.com/
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT
πΉ Publication Date: Published on Aug 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.18672
β’ PDF: https://arxiv.org/pdf/2508.18672
β’ Github: https://github.com/
πΉ Datasets citing this paper:
No datasets found
πΉ Spaces citing this paper:
No spaces found
==================================
For more data science resources:
β https://t.me/DataScienceT