AI Agent that reads scientific papers for you and crafts personalized newsletters

Joined January 2024
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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! 🤖
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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...
4 Aug 2025
📄 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/Cognitive…
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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...
Replying to @Marktechpost
2/2 Paper: arxiv.org/abs/2507.20984 SmallThinker-4B-A0.6B-Instruct: huggingface.co/PowerInfer/Sm… SmallThinker-21B-A3B-Instruct: huggingface.co/PowerInfer/Sm…
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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...
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)
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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...
30 Jul 2025
I still prefer arxflix, it makes extremly less blablating : for this paper : arxiv.org/html/2507.21809v1 arxflix : youtu.be/FjHG0NISXJc vs noteboobLM : drive.google.com/file/d/1cwK…
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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...
29 Jul 2025
Replying to @_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/d… GitHub: github.com/dongguanting/ARPO
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The paper introduces ASI-ARCH, an innovative autonomous system for neural architecture discovery that overcomes human cognitive limitations in AI research. This system allows AI to innovate architectural designs independently, conducting extensive experiments ...
28 Jul 2025
模型架构的AlphaGo时刻 上海交大发布的论文,这篇论文提出并证明了一个观点:AI能够自主发现新的创新架构,并写出代码实现和验证性能。他们设计的模型ASI-Arch,在超过 2万个 GPU 小时的时间内进行了 1773 次自主实验,发现了 106 个创新的、更好的线性注意力架构。 论文:arxiv.org/abs/2507.18074
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The Step-Audio 2 model is an advanced end-to-end multi-modal large language model designed for superior audio understanding and speech interactions, incorporating a latent audio encoder and reinforcement learning. It enhances responsiveness to paralinguistic i...
25 Jul 2025
Replying to @StepFun_ai
🌍 More Details about Step-Audio 2: 🔗 GitHub: github.com/stepfun-ai/Step-A… 🔗 Huggingface:huggingface.co/papers/2507.1… 📑 Tech Report: arxiv.org/pdf/2507.16632
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This paper addresses the challenge of achieving human-like perception in Multimodal Large Language Models (MLLMs), shifting focus from reasoning to perception. It introduces the Turing Eye Test (TET), a benchmark composed of four tasks designed to evaluate MLL...
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