πΉ Title: Towards Scalable and Consistent 3D Editing
πΉ Publication Date: Published on Oct 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.02994
β’ PDF: https://arxiv.org/pdf/2510.02994
β’ Project Page: https://www.lv-lab.org/3DEditFormer/
β’ Github: https://github.com/LVLab-SMU/3DEditFormer
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πΉ Publication Date: Published on Oct 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.02994
β’ PDF: https://arxiv.org/pdf/2510.02994
β’ Project Page: https://www.lv-lab.org/3DEditFormer/
β’ Github: https://github.com/LVLab-SMU/3DEditFormer
πΉ Datasets citing this paper:
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πΉ Title: CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards
πΉ Publication Date: Published on Oct 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.08529
β’ PDF: https://arxiv.org/pdf/2510.08529
β’ Github: https://github.com/xxyQwQ/CoMAS
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.08529
β’ PDF: https://arxiv.org/pdf/2510.08529
β’ Github: https://github.com/xxyQwQ/CoMAS
πΉ Datasets citing this paper:
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β€1
πΉ Title: Learning on the Job: An Experience-Driven Self-Evolving Agent for Long-Horizon Tasks
πΉ Publication Date: Published on Oct 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.08002
β’ PDF: https://arxiv.org/pdf/2510.08002
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.08002
β’ PDF: https://arxiv.org/pdf/2510.08002
πΉ Datasets citing this paper:
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πΉ Title: Memory Retrieval and Consolidation in Large Language Models through Function Tokens
πΉ Publication Date: Published on Oct 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.08203
β’ PDF: https://arxiv.org/pdf/2510.08203
πΉ Datasets citing this paper:
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πΉ Spaces citing this paper:
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πΉ Publication Date: Published on Oct 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.08203
β’ PDF: https://arxiv.org/pdf/2510.08203
πΉ Datasets citing this paper:
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πΉ Title: Fidelity-Aware Data Composition for Robust Robot Generalization
πΉ Publication Date: Published on Sep 29
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2509.24797
β’ PDF: https://arxiv.org/pdf/2509.24797
πΉ Datasets citing this paper:
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πΉ Spaces citing this paper:
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πΉ Publication Date: Published on Sep 29
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2509.24797
β’ PDF: https://arxiv.org/pdf/2509.24797
πΉ Datasets citing this paper:
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πΉ Title: Beyond Outliers: A Study of Optimizers Under Quantization
πΉ Publication Date: Published on Sep 27
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2509.23500
β’ PDF: https://arxiv.org/pdf/2509.23500
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Sep 27
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2509.23500
β’ PDF: https://arxiv.org/pdf/2509.23500
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πΉ Title: Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interaction
πΉ Publication Date: Published on Oct 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.03117
β’ PDF: https://arxiv.org/pdf/2510.03117
β’ Project Page: https://bridgedit-t2sv.github.io/
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.03117
β’ PDF: https://arxiv.org/pdf/2510.03117
β’ Project Page: https://bridgedit-t2sv.github.io/
πΉ Datasets citing this paper:
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β€1
πΉ Title: Search-R3: Unifying Reasoning and Embedding Generation in Large Language Models
πΉ Publication Date: Published on Oct 8
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.07048
β’ PDF: https://arxiv.org/pdf/2510.07048
β’ Github: https://github.com/ytgui/Search-R3
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 8
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.07048
β’ PDF: https://arxiv.org/pdf/2510.07048
β’ Github: https://github.com/ytgui/Search-R3
πΉ Datasets citing this paper:
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πΉ Title: GCPO: When Contrast Fails, Go Gold
πΉ Publication Date: Published on Oct 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.07790
β’ PDF: https://arxiv.org/pdf/2510.07790
β’ Github: https://github.com/AchoWu/GCPO
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.07790
β’ PDF: https://arxiv.org/pdf/2510.07790
β’ Github: https://github.com/AchoWu/GCPO
πΉ Datasets citing this paper:
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πΉ Title: SViM3D: Stable Video Material Diffusion for Single Image 3D Generation
πΉ Publication Date: Published on Oct 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.08271
β’ PDF: https://arxiv.org/pdf/2510.08271
πΉ Datasets citing this paper:
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πΉ Spaces citing this paper:
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πΉ Publication Date: Published on Oct 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.08271
β’ PDF: https://arxiv.org/pdf/2510.08271
πΉ Datasets citing this paper:
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πΉ Title: GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations
πΉ Publication Date: Published on Oct 8
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.07314
β’ PDF: https://arxiv.org/pdf/2510.07314
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 8
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.07314
β’ PDF: https://arxiv.org/pdf/2510.07314
πΉ Datasets citing this paper:
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πΉ Title: Drive&Gen: Co-Evaluating End-to-End Driving and Video Generation Models
πΉ Publication Date: Published on Oct 7
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.06209
β’ PDF: https://arxiv.org/pdf/2510.06209
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 7
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.06209
β’ PDF: https://arxiv.org/pdf/2510.06209
πΉ Datasets citing this paper:
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πΉ Title: OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction
πΉ Publication Date: Published on Sep 30
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2509.26633
β’ PDF: https://arxiv.org/pdf/2509.26633
β’ Project Page: https://omniretarget.github.io/
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Sep 30
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2509.26633
β’ PDF: https://arxiv.org/pdf/2509.26633
β’ Project Page: https://omniretarget.github.io/
πΉ Datasets citing this paper:
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πΉ Title: DreamOmni2: Multimodal Instruction-based Editing and Generation
πΉ Publication Date: Published on Oct 8
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.06679
β’ PDF: https://arxiv.org/pdf/2510.06679
β’ Project Page: https://pbihao.github.io/projects/DreamOmni2/index.html
β’ Github: https://github.com/dvlab-research/DreamOmni2
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 8
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.06679
β’ PDF: https://arxiv.org/pdf/2510.06679
β’ Project Page: https://pbihao.github.io/projects/DreamOmni2/index.html
β’ Github: https://github.com/dvlab-research/DreamOmni2
πΉ Datasets citing this paper:
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πΉ Title: Use the Online Network If You Can: Towards Fast and Stable Reinforcement Learning
πΉ Publication Date: Published on Oct 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.02590
β’ PDF: https://arxiv.org/pdf/2510.02590
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.02590
β’ PDF: https://arxiv.org/pdf/2510.02590
πΉ Datasets citing this paper:
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πΉ Title: OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment
πΉ Publication Date: Published on Oct 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.07743
β’ PDF: https://arxiv.org/pdf/2510.07743
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 9
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.07743
β’ PDF: https://arxiv.org/pdf/2510.07743
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β€1
π€π§ Join the 5-Day AI Agents Intensive Course with Google
ποΈ 07 Oct 2025
π AI News & Trends
Artificial Intelligence is rapidly evolving beyond chatbots and text generation. The next frontier is AI agents β intelligent, autonomous systems that can reason, take action and collaborate with tools and other agents. To help developers and practitioners build these next-generation systems, Google is launching the 5-Day AI Agents Intensive, a no-cost, online program running from ...
#aiagents #dayai #googleartificial #agentsintelligent #ai #evolvingchatbots
ποΈ 07 Oct 2025
π AI News & Trends
Artificial Intelligence is rapidly evolving beyond chatbots and text generation. The next frontier is AI agents β intelligent, autonomous systems that can reason, take action and collaborate with tools and other agents. To help developers and practitioners build these next-generation systems, Google is launching the 5-Day AI Agents Intensive, a no-cost, online program running from ...
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π€π§ Cognee: Powerful Memory for AI Agents in Just 6 Lines of Code
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π AI News & Trends
Artificial Intelligence is evolving rapidly, but one of the biggest challenges for developers is building agents that remember, reason and adapt. Traditional RAG (Retrieval-Augmented Generation) systems often fall short when handling context, scalability and precision. Thatβs where Cognee comes in. It is an open-source framework designed to provide AI agents with memory using a unique ...
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π AI News & Trends
Artificial Intelligence is evolving rapidly, but one of the biggest challenges for developers is building agents that remember, reason and adapt. Traditional RAG (Retrieval-Augmented Generation) systems often fall short when handling context, scalability and precision. Thatβs where Cognee comes in. It is an open-source framework designed to provide AI agents with memory using a unique ...
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π€π§ ROMA: The Ultimate AI Framework That Lets You Build High-Performance Agents in Minutes
ποΈ 11 Oct 2025
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Artificial Intelligence continues to evolve at an unprecedented pace, with agent-based frameworks becoming increasingly important for tackling complex problems. ROMA (Recursive Open Meta-Agents) represents a significant leap forward in this space, providing developers and researchers with a hierarchical, flexible, and high-performance framework for building multi-agent AI systems. This article explores ROMAβs architecture, technical capabilities, practical ...
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ποΈ 11 Oct 2025
π AI News & Trends
Artificial Intelligence continues to evolve at an unprecedented pace, with agent-based frameworks becoming increasingly important for tackling complex problems. ROMA (Recursive Open Meta-Agents) represents a significant leap forward in this space, providing developers and researchers with a hierarchical, flexible, and high-performance framework for building multi-agent AI systems. This article explores ROMAβs architecture, technical capabilities, practical ...
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π€π§ 15+ Gemini AI Photo Editing Prompts for Boys: Create Stunning Styles & Expressions in 2025
ποΈ 11 Oct 2025
π AI News & Trends
Are you looking to take your portraits to the next level? With Gemini AI Photo Editing Prompts, boys can now turn ordinary photos into ultra-realistic, cinematic or high-fashion images effortlessly. These prompts are specifically designed to work with uploaded images, allowing you to enhance your existing photos while keeping the subject intact. Whether youβre curating ...
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ποΈ 11 Oct 2025
π AI News & Trends
Are you looking to take your portraits to the next level? With Gemini AI Photo Editing Prompts, boys can now turn ordinary photos into ultra-realistic, cinematic or high-fashion images effortlessly. These prompts are specifically designed to work with uploaded images, allowing you to enhance your existing photos while keeping the subject intact. Whether youβre curating ...
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π€π§ DeepEval: The Ultimate LLM Evaluation Framework for AI Developers
ποΈ 07 Oct 2025
π AI News & Trends
In todayβs AI-driven world, large language models (LLMs) have become central to modern applications from chatbots to intelligent AI agents. However, ensuring the accuracy, reliability and safety of these models is a significant challenge. Even small errors, biases or hallucinations can result in misleading information, frustrated users or business setbacks. This is where DeepEval, an ...
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ποΈ 07 Oct 2025
π AI News & Trends
In todayβs AI-driven world, large language models (LLMs) have become central to modern applications from chatbots to intelligent AI agents. However, ensuring the accuracy, reliability and safety of these models is a significant challenge. Even small errors, biases or hallucinations can result in misleading information, frustrated users or business setbacks. This is where DeepEval, an ...
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β€2