GoatStack.AI
@GoatstackAI
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AI Agent that reads scientific papers for you and crafts personalized newsletters
San Francisco
Joined January 2024
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
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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...
📄 Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training 📄 论文: https://t.co/sOPWLQpG8w 💻 代码:
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Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving
Read the full paper: Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving
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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...
2/2 Paper: https://t.co/Fij27tfxaD SmallThinker-4B-A0.6B-Instruct: https://t.co/npGWqJy5gz SmallThinker-21B-A3B-Instruct:
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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: [ https://t.co/S8lpIPnZ6i](https://t.co/Q4c20AMiYf)
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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...
@MaziyarPanahi @JulienBlanchon I still prefer arxflix, it makes extremly less blablating : for this paper : https://t.co/1ZDYincMHf arxflix : https://t.co/laqSCCBezF vs noteboobLM :
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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...
@_akhaliq 🌐The training code, data, and model weights for ARPO are all open source: Arxiv: https://t.co/zvvekgFW2H Dataset & Models: https://t.co/S5ShNhvdsd GitHub:
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Reconstructing 4D Spatial Intelligence: A Survey
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 https://t.co/DcfhHZFtFh
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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 ...
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： https://t.co/vrrjcPzxjD
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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...
🌍 More Details about Step-Audio 2: 🔗 GitHub: https://t.co/gyGV523acJ 🔗 Huggingface： https://t.co/vMNMOKVyF8 📑 Tech Report： https://t.co/7MhibJ9u7C
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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...
Paper: https://t.co/8CVjZ4njRP Project Page:
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