🔹 Title: ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks

🔹 Publication Date: Published on Aug 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.08240
• PDF: https://arxiv.org/pdf/2508.08240
• Project Page: https://kaijwang.github.io/odyssey.github.io/

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Learnable SMPLify: A Neural Solution for Optimization-Free Human Pose Inverse Kinematics

🔹 Publication Date: Published on Aug 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13562
• PDF: https://arxiv.org/pdf/2508.13562
• Github: https://github.com/Charrrrrlie/Learnable-SMPLify

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15868
• PDF: https://arxiv.org/pdf/2508.15868

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Sketch3DVE: Sketch-based 3D-Aware Scene Video Editing

🔹 Publication Date: Published on Aug 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.13797
• PDF: https://arxiv.org/pdf/2508.13797
• Github: https://github.com/IGLICT/Sketch3DVE

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: RotaTouille: Rotation Equivariant Deep Learning for Contours

🔹 Publication Date: Published on Aug 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16359
• PDF: https://arxiv.org/pdf/2508.16359
• Github: https://github.com/odinhg/rotation-equivariant-contour-learning

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Hop, Skip, and Overthink: Diagnosing Why Reasoning Models Fumble during Multi-Hop Analysis

🔹 Publication Date: Published on Aug 6

🔹 Abstract: Research investigates reasoning failures in language models for multi-hop question answering, introducing a framework to categorize errors and improve model fidelity. AI-generated summary The emergence of reasoning models and their integration into practical AI chat bots has led to breakthroughs in solving advanced math, deep search, and extractive question answering problems that requires a complex and multi-step thought process. Yet, a complete understanding of why these models hallucinate more than general purpose language models is missing. In this investigative study, we systematicallyexplore reasoning failures of contemporary language models on multi-hop question answering tasks. We introduce a novel, nuanced error categorization framework that examines failures across three critical dimensions: the diversity and uniqueness of source documents involved ("hops"), completeness in capturing relevant information ("coverage"), and cognitive inefficiency ("overthinking"). Through rigorous hu-man annotation, supported by complementary automated metrics, our exploration uncovers intricate error patterns often hidden by accuracy-centric evaluations. This investigative approach provides deeper insights into the cognitive limitations of current models and offers actionable guidance toward enhancing reasoning fidelity , transparency , and robustness in future language modeling efforts.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.04699

• PDF: https://arxiv.org/pdf/2508.04699

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent LLMs

🔹 Publication Date: Published on Aug 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17188
• PDF: https://arxiv.org/pdf/2508.17188

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

🔹 Publication Date: Published on Aug 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18265
• PDF: https://arxiv.org/pdf/2508.18265
• Github: https://github.com/OpenGVLab/InternVL

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: UQ: Assessing Language Models on Unsolved Questions

🔹 Publication Date: Published on Aug 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17580
• PDF: https://arxiv.org/pdf/2508.17580
• Project Page: https://huggingface.co/datasets/uq-project/uq

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/uq-project/uq

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: ST-Raptor: LLM-Powered Semi-Structured Table Question Answering

🔹 Publication Date: Published on Aug 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18190
• PDF: https://arxiv.org/pdf/2508.18190
• Github: https://github.com/weAIDB/ST-Raptor

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: SpotEdit: Evaluating Visually-Guided Image Editing Methods

🔹 Publication Date: Published on Aug 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18159
• PDF: https://arxiv.org/pdf/2508.18159
• Github: https://github.com/SaraGhazanfari/SpotEdit

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Neither Valid nor Reliable? Investigating the Use of LLMs as Judges

🔹 Publication Date: Published on Aug 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18076
• PDF: https://arxiv.org/pdf/2508.18076

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning

🔹 Publication Date: Published on Aug 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16949
• PDF: https://arxiv.org/pdf/2508.16949

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Visual-CoG: Stage-Aware Reinforcement Learning with Chain of Guidance for Text-to-Image Generation

🔹 Publication Date: Published on Aug 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18032
• PDF: https://arxiv.org/pdf/2508.18032

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation

🔹 Publication Date: Published on Aug 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17472
• PDF: https://arxiv.org/pdf/2508.17472

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling

🔹 Publication Date: Published on Aug 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16790
• PDF: https://arxiv.org/pdf/2508.16790

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Explain Before You Answer: A Survey on Compositional Visual Reasoning

🔹 Publication Date: Published on Aug 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17298
• PDF: https://arxiv.org/pdf/2508.17298
• Project Page: https://github.com/pokerme7777/Compositional-Visual-Reasoning-Survey
• Github: https://github.com/pokerme7777/Compositional-Visual-Reasoning-Survey

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: Agent Lightning: Train ANY AI Agents with Reinforcement Learning

🔹 Publication Date: Published on Aug 5

🔹 Abstract: Agent Lightning is a flexible RL framework for training LLMs in various agents, using a hierarchical RL algorithm and decoupling execution from training to handle complex interactions. AI-generated summary We present Agent Lightning, a flexible and extensible framework that enables Reinforcement Learning (RL)-based training of Large Language Models (LLMs) for any AI agent. Unlike existing methods that tightly couple RL training with agent or rely on sequence concatenation with masking, Agent Lightning achieves complete decoupling between agent execution and training, allowing seamless integration with existing agents developed via diverse ways (e.g., using frameworks like LangChain, OpenAI Agents SDK, AutoGen, and building from scratch) with almost ZERO code modifications. By formulating agent execution as Markov decision process , we define an unified data interface and propose a hierarchical RL algorithm , LightningRL, which contains a credit assignment module, allowing us to decompose trajectories generated by ANY agents into training transition. This enables RL to handle complex interaction logic, such as multi-agent scenarios and dynamic workflows. For the system design, we introduce a Training-Agent Disaggregation architecture , and brings agent observability frameworks into agent runtime, providing a standardized agent finetuning interface. Experiments across text-to-SQL , retrieval-augmented generation, and math tool-use tasks demonstrate stable, continuous improvements, showcasing the framework's potential for real-world agent training and deployment.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03680

• PDF: https://arxiv.org/pdf/2508.03680

• Project Page: https://www.microsoft.com/en-us/research/project/agent-lightning/

• Github: https://github.com/microsoft/agent-lightning

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: MV-RAG: Retrieval Augmented Multiview Diffusion

🔹 Publication Date: Published on Aug 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16577
• PDF: https://arxiv.org/pdf/2508.16577
• Project Page: https://yosefdayani.github.io/MV-RAG/
• Github: https://github.com/yosefdayani/MV-RAG

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment

🔹 Publication Date: Published on Aug 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17290
• PDF: https://arxiv.org/pdf/2508.17290

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

🔹 Title: German4All - A Dataset and Model for Readability-Controlled Paraphrasing in German

🔹 Publication Date: Published on Aug 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17973
• PDF: https://arxiv.org/pdf/2508.17973

🔹 Datasets citing this paper:
No datasets found

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT