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AI Papers & Wine vol. 2 was a blast! 🍷 We dove into Geoffrey Hinton's Fast-Forwarding paper, enjoyed an impromptu dance party, and celebrated @arturkiulian's win with a bottle of wine. Thanks to all who joined us and made it an unforgettable night of AI innovation and community. We learned a lot about how AI researchers read papers and are excited to develop a B2B offering to support them. Tag your AI researcher friends 👇We'd love to connect and learn more! Stay tuned for our upcoming custom prompt competition and subscribe to @GoatstackAI for updates on new features and releases. Until next time, keep innovating, connecting, and dancing! 🤖

@FeitengLi AI Summary: Cognitive Kernel-Pro is presented as a fully open-source multi-module framework aimed at democratizing the development of deep research agents, which are essential for advanced AI functionalities... goatstack.ai/articles/2508.…

📄 Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training 📄 论文: arxiv.org/abs/2508.00414 💻 代码: github.com/Tencent/Cognit…

Cognitive Kernel-Pro is presented as a fully open-source multi-module framework aimed at democratizing the development of deep research agents, which are essential for advanced AI functionalities like reasoning, web interaction, and autonomous research. The pa...

@cristian_le0 AI Summary: Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving goatstack.ai/articles/2507.…

Read the full paper: Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving arxiv.org/pdf/2507.23726…

Researchers just achieved a major milestone in AI math! Seed-Prover fully proved 5 out of 6 problems in the IMO 2025 contest, mastering complex theorems with iterative reasoning. Unprecedented!

Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving

@Marktechpost AI Summary: The paper introduces SmallThinker, a family of large language models (LLMs) specifically designed for local deployment, overcoming the limitations of traditional models which are optimized for GP... goatstack.ai/articles/2507.…

2/2 Paper: arxiv.org/abs/2507.20984 SmallThinker-4B-A0.6B-Instruct: huggingface.co/PowerInfer/Sma… SmallThinker-21B-A3B-Instruct: huggingface.co/PowerInfer/Sma…

1/2 Meet SmallThinker: A Family of Efficient Large Language Models LLMs Natively Trained for Local Deployment The generative AI landscape is dominated by massive language models, often designed for the vast capacities of cloud data centers. These models, while powerful, make it difficult or impossible for everyday users to deploy advanced AI privately and efficiently on local devices like laptops, smartphones, or embedded systems. Instead of compressing cloud-scale models for the edge—often resulting in substantial performance compromises—the team behind SmallThinker asked a more fundamental question: What if a language model were architected from the start for local constraints? This was the genesis for SmallThinker, a family of Mixture-of-Experts (MoE) models developed by Researchers at Shanghai Jiao Tong University and Zenergize AI, that targets at high-performance, memory-limited, and compute-constrained on-device inference. With two main variants—SmallThinker-4B-A0.6B and SmallThinker-21B-A3B—they set a new benchmark for efficient, accessible AI..... Full Analysis: marktechpost.com/2025/08/01/mee…

The paper introduces SmallThinker, a family of large language models (LLMs) specifically designed for local deployment, overcoming the limitations of traditional models which are optimized for GPU-powered cloud infrastructure. By utilizing a deployment-aware a...

@RevanthAtmakuri AI Summary: This paper presents ScreenCoder, a modular multi-agent framework designed to automate the transformation of user interface (UI) designs into front-end code, addressing limitations of current larg... goatstack.ai/articles/2507.…

ScreenCoder: because making UI designs into code should be less of a guesswork game. Three agents—grounding, planning, and generating—combine to make UI-to-code magic. Say goodbye to black-box coding! 🤖🔧 - ChatGPT Link: [arxiv.org/abs/2507.22827](arxiv.org/abs/2507.22827)

This paper presents ScreenCoder, a modular multi-agent framework designed to automate the transformation of user interface (UI) designs into front-end code, addressing limitations of current large language models (LLMs) that rely solely on text prompts. The fr...

@flopsy42 @MaziyarPanahi @JulienBlanchon AI Summary: HunyuanWorld 1.0 is a novel framework developed by Tencent aimed at generating immersive, explorable, and interactive 3D worlds from textual and visual inputs. It addresses the limitations of exi... goatstack.ai/articles/2507.…

@MaziyarPanahi @JulienBlanchon I still prefer arxflix, it makes extremly less blablating : for this paper : arxiv.org/html/2507.2180… arxflix : youtu.be/FjHG0NISXJc vs noteboobLM : drive.google.com/file/d/1cwKetV…

This is honestly the most underated AI app ever

HunyuanWorld 1.0 is a novel framework developed by Tencent aimed at generating immersive, explorable, and interactive 3D worlds from textual and visual inputs. It addresses the limitations of existing video-based and 3D-based world generation methods by combin...

@kakakbibibi @_akhaliq AI Summary: The paper introduces Agentic Reinforced Policy Optimization (ARPO), a novel reinforcement learning algorithm aimed at enhancing the performance of large language models (LLMs) in multi-turn inter... goatstack.ai/articles/2507.…

@_akhaliq 🌐The training code, data, and model weights for ARPO are all open source: Arxiv: arxiv.org/abs/2507.19849 Dataset & Models: huggingface.co/collections/do… GitHub: github.com/dongguanting/A…

Agentic Reinforced Policy Optimization

The paper introduces Agentic Reinforced Policy Optimization (ARPO), a novel reinforcement learning algorithm aimed at enhancing the performance of large language models (LLMs) in multi-turn interactions with external tools. Unlike existing RL methods that focu...

@zhenjun_zhao @yukangcao @hongfz16 @Frozen_Burning @YuanLiu41955461 @liuziwei7 AI Summary: Reconstructing 4D Spatial Intelligence: A Survey goatstack.ai/articles/2507.…

Reconstructing 4D Spatial Intelligence: A Survey @yukangcao, Jiahao Lu, Zhisheng Huang, Zhuowei Shen, Chengfeng Zhao, @hongfz16, @Frozen_Burning, Xin Li, Wenping Wang, @YuanLiu41955461, @liuziwei7 tl;dr: in title arxiv.org/abs/2507.21045

Reconstructing 4D Spatial Intelligence: A Survey