makingAGI's profile picture. CEO of Sapient Intelligence. Exploring the path to AGI through brain-inspired AI. 🧠🤖 #AGI #NeuroAI

Guan Wang

@makingAGI

CEO of Sapient Intelligence. Exploring the path to AGI through brain-inspired AI. 🧠🤖 #AGI #NeuroAI

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🚀Introducing Hierarchical Reasoning Model🧠🤖 Inspired by brain's hierarchical processing, HRM delivers unprecedented reasoning power on complex tasks like ARC-AGI and expert-level Sudoku using just 1k examples, no pretraining or CoT! Unlock next AI breakthrough with…

makingAGI's tweet image. 🚀Introducing Hierarchical Reasoning Model🧠🤖

Inspired by brain's hierarchical processing, HRM delivers unprecedented reasoning power on complex tasks like ARC-AGI and expert-level Sudoku using just 1k examples, no pretraining or CoT!

Unlock next AI breakthrough with…

Hierarchical reasoning works well on large language models!🎉

makingAGI's tweet image. Hierarchical reasoning works well on large language models!🎉

Guan Wang 已轉發

🔥It’s official-Sapient HRM Discord Community is now live! This is a place to discuss, connect, and collaborate as we shape HRM’s future together. We will be sharing our latest work, releases, and tips, as well as hosting Q&A sessions💬💬 Hop on this journey with us as we push…

Sapient_Int's tweet image. 🔥It’s official-Sapient HRM Discord Community is now live!

This is a place to discuss, connect, and collaborate as we shape HRM’s future together. We will be sharing our latest work, releases, and tips, as well as hosting Q&A sessions💬💬

Hop on this journey with us as we push…

Thanks to @arcprize for reproducing and verifying the results! ARC-AGI-1: public 41% pass@2 - semi private 32% pass@2 ARC-AGI-2: public 4% pass@2 - semi private 2% pass@2 Due to differences in testing environments, a certain amount of variance in results is acceptable.…

makingAGI's tweet image. Thanks to @arcprize for reproducing and verifying the results!

ARC-AGI-1: public 41% pass@2 - semi private 32% pass@2
ARC-AGI-2: public 4% pass@2 - semi private 2% pass@2

Due to differences in testing environments, a certain amount of variance in results is acceptable.…
makingAGI's tweet image. Thanks to @arcprize for reproducing and verifying the results!

ARC-AGI-1: public 41% pass@2 - semi private 32% pass@2
ARC-AGI-2: public 4% pass@2 - semi private 2% pass@2

Due to differences in testing environments, a certain amount of variance in results is acceptable.…

❤️ Thanks for highlighting our HRM paper! Apologies for any confusion—we're working on clarifying this thoroughly. Stay tuned for updates!

Ok, so this paragraph in isolation looks pretty bad, but based on the code, THEY DIDN'T TRAIN ON THE TEST SET. In fact, THEY DIDN'T PRETRAIN AT ALL. And that's the point of the paper! 1/



Thanks for featuring us!😃

My story on HRM with comments from @makingAGI, CEO of Sapient Intelligence x.com/VentureBeat/st…



🌟Exactly my thoughts on the next-gen of AI reasoning. Leveraging insights from neuroscience, our Hierarchical Reasoning Model offers practical, efficient scaling of depth. Not every problem can be solved faster with more processors; sometimes, the key is adding depth.

Some problems can’t be rushed—they can only be done step by step, no matter how many people or processors you throw at them. We’ve scaled AI by making everything bigger and more parallel: Our models are parallel. Our scaling is parallel. Our GPUs are parallel. But what if the…



Guan Wang 已轉發

Our co-founder William Chen is going to share more about the open-sourced Hierarchical Reasoning Model (HRM) at #FortuneAISingapore @FortuneMagazine tomorrow, under the panel theme "Beyond Human: AGI And The Future We’re Building"! We are excited about the practical path towards…

Sapient_Int's tweet image. Our co-founder William Chen is going to share more about the open-sourced Hierarchical Reasoning Model (HRM) at #FortuneAISingapore @FortuneMagazine tomorrow, under the panel theme "Beyond Human: AGI And The Future We’re Building"! We are excited about the practical path towards…

Will Sudoku become the MNIST for reasoning? Simple rules, clear structure, unique solutions—yet surprisingly challenging for modern LLMs, often requiring explicit trial-and-error to solve. huggingface.co/datasets/sapie…


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