Explore tweets tagged as #GoatStack
@bronzeagepapi AI Summary: This paper introduces Energy-Based Transformers (EBTs), a novel class of models that enable System 2 Thinking through unsupervised learning. EBTs assign energy values to input and candidate-predi.
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This paper introduces Energy-Based Transformers (EBTs), a novel class of models that enable System 2 Thinking through unsupervised learning. EBTs assign energy values to input and candidate-prediction pairs, utilizing gradient descent for optimization to enhan.
Energy-Based Transformers are Scalable Learners and Thinkers.
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The paper explores the potential of AI research agents to automate the design and training of machine learning models, specifically within the context of the MLE-bench benchmark where agents compete in Kaggle competitions. It formalizes these agents as search .
AI Research Agents are becoming proficient at machine learning tasks, but how can we help them search the space of candidate solutions and codebases? Read our new paper looking at MLE-Bench: #LLM #Agents #MLEBench
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FreeMorph introduces a novel tuning-free method for image morphing, capable of generating smooth transitions between semantically diverse input images without the need for fine-tuning pre-trained diffusion models. This method overcomes the limitations of exist.
🔥Tuning-free 2D image morphing🔥. Tired of complex training and strict semantic/layout demands? . Meet #FreeMorph #ICCV2025: tuning-free image morphing across diverse situations. -Project: -Paper: -Code:
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A Neural Lambda Calculus.Bridging the gap between neurosymbolic AI and the foundations of computing through Neural Lambda Calculus. #NeurosymbolicAI #LambdaCalculus #DeepNeuralNetworks #ComputationalModels #goatai #goatstack via @GoatstackAI.
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This paper discusses the evolution of multimodal reasoning, emphasizing a shift from viewing images as static contexts to utilizing them as dynamic cognitive tools within AI models. The authors introduce the 'Thinking with Images' paradigm, which is structured.
Excited to share our new survey on the reasoning paradigm shift from "Think with Text" to "Think with Image"! 🧠🖼️.Our work offers a roadmap for more powerful & aligned AI. 🚀.📜 Paper: ⭐ GitHub (400+🌟):
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@XiaotaoGu @Zai_org AI Summary: The GLM-4.1V-Thinking model, developed by Zhipu AI and Tsinghua University, is a vision-language model designed to enhance multimodal reasoning capabilities through a comprehensive training frame.
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The Kwai Keye-VL Technical Report presents an 8-billion-parameter multimodal foundation model designed to excel in short-video understanding while preserving general vision-language capabilities. It relies on a high-quality dataset of over 600 billion tokens a.
PROJECT: PAPER: CODE:
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This paper investigates whether Large Language Models (LLMs) exhibit strategic intelligence in competitive settings, utilizing the Iterated Prisoner’s Dilemma (IPD) as a model for decision-making. The authors conducted evolutionary IPD tournaments with canon.
Take a look! We used Gemini, Claude and GPT4. These LLMs are competitive against all the classic agents in PD literature.
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@kennethpayne01 AI Summary: This paper investigates whether Large Language Models (LLMs) exhibit strategic intelligence in competitive settings, utilizing the Iterated Prisoner’s Dilemma (IPD) as a model for decision-maki.
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@SuZhaochen0110 AI Summary: This paper discusses the evolution of multimodal reasoning, emphasizing a shift from viewing images as static contexts to utilizing them as dynamic cognitive tools within AI models. The authors i.
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@yukangcao AI Summary: FreeMorph introduces a novel tuning-free method for image morphing, capable of generating smooth transitions between semantically diverse input images without the need for fine-tuning pre-trained.
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"Indulging in delicious cuisine and earning rewards at the same time? What a way to spend the weekend. It's all possible with my Plutus card and GOAT Stack! 💳🍽️ @plutus #BestRewardsCard #PlutusPerks #GOATStack #DiningRewards
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@gm8xx8 AI Summary: The Kwai Keye-VL Technical Report presents an 8-billion-parameter multimodal foundation model designed to excel in short-video understanding while preserving general vision-language capabilities.
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