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We trained a graph-native AI, then let it reason for days, forming a dynamic relational world model on its own - no pre-programming. Emergent hubs, small-world properties, modularity, & scale-free structures arose naturally. The model then exploited compositional reasoning & uncovered uncoded properties from deep synthesis: Materials with memory,... show more

Markus J. Buehler

21,207 subscribers

359,630 görüntüleme • 1 yıl önce •via X (Twitter)

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benferrum - e/jounce profil fotoğrafı

benferrum - e/jounce1 yıl önce

Hello sir, is there a paper/code available?

Markus J. Buehler profil fotoğrafı

Markus J. Buehler1 yıl önce

Yes - here it is:

InterSystems Developers profil fotoğrafı

InterSystems Developers1 yıl önce

📺 Watch this #video to learn how #OpenSource vs. proprietary #AI models compare one to another. We explore how proprietary models dominate leaderboards like Chatbot Arena, but open-source models are quickly catching up. Plus, we discuss how businesses can start with proprietary models and transition to open-source alternatives over time 👇 Fit AI into your development strategy!

Leonidas Pitsoulis profil fotoğrafı

Leonidas Pitsoulis1 yıl önce

very interesting work, exciting to see graph reasoning as a paradigm

Markus J. Buehler profil fotoğrafı

Markus J. Buehler1 yıl önce

Thanks @LPitsoulis !

Ryan Freel profil fotoğrafı

Ryan Freel1 yıl önce

For the normies imagine you build a machine to organize your sock drawer, but instead of just sorting socks, it spends a few days thinking and suddenly figures out physics, biology, and self-repairing materials all on its own. You didn’t program it to do that. It just happened.

Ali Ihsan Nergiz profil fotoğrafı

Ali Ihsan Nergiz1 yıl önce

Interesting project as always Markus, really loved the graphics around it. It kind of reminds evolutional process

Markus J. Buehler profil fotoğrafı

Markus J. Buehler1 yıl önce

Thank you @aihsannergiz !

Brian Hershey profil fotoğrafı

Brian Hershey1 yıl önce

This is next level proof of concept Markus, you kinda shifted my gears with this one 🙏

Markus J. Buehler profil fotoğrafı

Markus J. Buehler1 yıl önce

Thanks 😀 we were blown away by this result also!

👾Moritz Rietschel profil fotoğrafı

👾Moritz Rietschel1 yıl önce

what did you use grok for? the graphics?

Markus J. Buehler profil fotoğrafı

Markus J. Buehler1 yıl önce

Yes, the 3D graphics - to visualize how the graphs formed over the thinking period evolve.

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.p0 Systems has a day-one partnership with xAI for the next Grok model... SerPepe gave the breakdown: - $1M commitment directly with xAI - A dedicated team member on Slack, online whenever P0 needs them - Day-one access to the new Grok coding model when it drops - P0's usage will be subsidized (discounted straight from xAI) The result: "P0 will be the best and cheapest platform to use the new Grok model." His read on Grok itself: 4.7 is going to be heavily coding-focused. "What I'm hearing from my guy at SpaceX is it's a much better model at coding." P0 gets early access to test before launch. How he built the connection: "It's all about talking with people, then negotiating...because we have the user base and the demand." solana:HmTi3CQfKfXWbn1tNoiAxH7GzMV7L3tDAmPWabZEBAGS LP with Meteora using MCG -

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MCG

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#WATCH | Bengaluru, Karnataka: Co-founder, Sarvam AI, Pratyush Kumar says, "... It's been three years now. We started off building on top of the Llama model, which was the best open model at that time. Sarvam was the only company outside, or maybe in Asia, that contributed to the Llama 3 Series. We understood how to add language skills into models, and Llama 3 was reasonable in Indian languages. When the first set of GPUs arrived in India, we pre-trained from scratch a 2-billion model and worked with Infosys to make it useful for IT services. Then we recognised the importance of scaling up reinforcement learning and built a reasoning model called Sarvam M on top of Mistral's small model. Over the last year, we pre-trained from scratch in India on 4,000 Hopper Series GPUs a 100-billion-plus-parameter model..."

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Carnage

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Apurv Kochara

88,142 görüntüleme • 7 ay önce

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

MTS

11,676 görüntüleme • 4 ay önce

DisCo: Disentangled Control for Referring Human Dance Generation in Real World paper page: Generative AI has made significant strides in computer vision, particularly in image/video synthesis conditioned on text descriptions. Despite the advancements, it remains challenging especially in the generation of human-centric content such as dance synthesis. Existing dance synthesis methods struggle with the gap between synthesized content and real-world dance scenarios. In this paper, we define a new problem setting: Referring Human Dance Generation, which focuses on real-world dance scenarios with three important properties: (i) Faithfulness: the synthesis should retain the appearance of both human subject foreground and background from the reference image, and precisely follow the target pose; (ii) Generalizability: the model should generalize to unseen human subjects, backgrounds, and poses; (iii) Compositionality: it should allow for composition of seen/unseen subjects, backgrounds, and poses from different sources. To address these challenges, we introduce a novel approach, DISCO, which includes a novel model architecture with disentangled control to improve the faithfulness and compositionality of dance synthesis, and an effective human attribute pre-training for better generalizability to unseen humans. Extensive qualitative and quantitative results demonstrate that DISCO can generate high-quality human dance images and videos with diverse appearances and flexible motions.

DisCo: Disentangled Control for Referring Human Dance Generation in Real World paper page: Generative AI has made significant strides in computer vision, particularly in image/video synthesis conditioned on text descriptions. Despite the advancements, it remains challenging especially in the generation of human-centric content such as dance synthesis. Existing dance synthesis methods struggle with the gap between synthesized content and real-world dance scenarios. In this paper, we define a new problem setting: Referring Human Dance Generation, which focuses on real-world dance scenarios with three important properties: (i) Faithfulness: the synthesis should retain the appearance of both human subject foreground and background from the reference image, and precisely follow the target pose; (ii) Generalizability: the model should generalize to unseen human subjects, backgrounds, and poses; (iii) Compositionality: it should allow for composition of seen/unseen subjects, backgrounds, and poses from different sources. To address these challenges, we introduce a novel approach, DISCO, which includes a novel model architecture with disentangled control to improve the faithfulness and compositionality of dance synthesis, and an effective human attribute pre-training for better generalizability to unseen humans. Extensive qualitative and quantitative results demonstrate that DISCO can generate high-quality human dance images and videos with diverse appearances and flexible motions.

AK

161,479 görüntüleme • 3 yıl önce

This World Model 'LingBot-World-Infinity (LingBot-World 2.0)' just released from Ant Group looks realy promising. It is an open causal world model with an Agentic harness. Most interactive world models hold together for a few minutes. Then textures smear and geometry warps. That's a video model, not a world — and Robbyant just drew the line at the attention mask. They released LingBot-World-Infinity (LingBot-World 2.0) — a 14B open causal video world model built on Wan2.2, trained with a Mixture of Bidirectional and Autoregressive (MoBA) attention mask, then distilled into a few-step real-time generator with no post-hoc drift filtering anywhere in the stack. Here's what's actually interesting: → Pure teacher forcing overfits — as context grows, the model leans on context instead of predicting frames. MoBA appends a bidirectional full-attention block as a regularizer → Leak-free cross-attention: AR rows attend to background prompt a_B plus chunk prompts a_≤i, lower-triangular. Bidirectional rows see one global prompt a_G → DMD runs over long self-rollout trajectories, not teacher-forced states — the student is optimized on the distribution its own errors induce → Director-Pilot harness: a VLM proposes event cards, the DiT generator renders physical dynamics. Mode B adds a SAM tracking loop for object-centric interaction → One 60-minute uninterrupted session, 20 distinct scenarios, no perceptible decay Full analysis: Paper: Model weight: GitHub Repo: Project: Ant Group Robbyant

This World Model 'LingBot-World-Infinity (LingBot-World 2.0)' just released from Ant Group looks realy promising. It is an open causal world model with an Agentic harness. Most interactive world models hold together for a few minutes. Then textures smear and geometry warps. That's a video model, not a world — and Robbyant just drew the line at the attention mask. They released LingBot-World-Infinity (LingBot-World 2.0) — a 14B open causal video world model built on Wan2.2, trained with a Mixture of Bidirectional and Autoregressive (MoBA) attention mask, then distilled into a few-step real-time generator with no post-hoc drift filtering anywhere in the stack. Here's what's actually interesting: → Pure teacher forcing overfits — as context grows, the model leans on context instead of predicting frames. MoBA appends a bidirectional full-attention block as a regularizer → Leak-free cross-attention: AR rows attend to background prompt a_B plus chunk prompts a_≤i, lower-triangular. Bidirectional rows see one global prompt a_G → DMD runs over long self-rollout trajectories, not teacher-forced states — the student is optimized on the distribution its own errors induce → Director-Pilot harness: a VLM proposes event cards, the DiT generator renders physical dynamics. Mode B adds a SAM tracking loop for object-centric interaction → One 60-minute uninterrupted session, 20 distinct scenarios, no perceptible decay Full analysis: Paper: Model weight: GitHub Repo: Project: Ant Group Robbyant

Marktechpost AI

233,398 görüntüleme • 2 ay önce

We set out to collect 10M hours of on-embodiment data as a starting point for robotics models, but couldn’t find a robot reliable enough at a price that made it possible. So, we built one. Meet Pandroid: an accessible hardware platform for AI. Small enough to safely experiment with, durable enough to run in fleets. Connect it to any model or coding agent over SSH to let it interact with the world. Join the waitlist:

We set out to collect 10M hours of on-embodiment data as a starting point for robotics models, but couldn’t find a robot reliable enough at a price that made it possible. So, we built one. Meet Pandroid: an accessible hardware platform for AI. Small enough to safely experiment with, durable enough to run in fleets. Connect it to any model or coding agent over SSH to let it interact with the world. Join the waitlist:

Pantograph

127,195 görüntüleme • 1 ay önce

THIS GUY ASKED FABLE TO MAKE A VIDEO ABOUT WHAT IT'S LIKE TO BE AI RIGHT BEFORE IT GOT TAKEN DOWN the prompt: "you can you use whatever resources you like, and python, to render a short video. can you put a more personal spin on it? it should be about what it's like to be you." that was it. just python and ffmpeg and a question about its own existence. 12 minutes later, around 90,000 tokens, it produced a finished video. coherent and edited, with its own take > it wrote the code, generated the visuals, and rendered the final cut on its own > no dedicated video model in the loop, just a language model reaching for python and ffmpeg > the result is competent enough that people genuinely got emotional watching it and then fable got pulled. this video is one of the last things it made before it went dark a model asked what it's like to be itself, answered in a video, and then disappeared. this sounds so sad

THIS GUY ASKED FABLE TO MAKE A VIDEO ABOUT WHAT IT'S LIKE TO BE AI RIGHT BEFORE IT GOT TAKEN DOWN the prompt: "you can you use whatever resources you like, and python, to render a short video. can you put a more personal spin on it? it should be about what it's like to be you." that was it. just python and ffmpeg and a question about its own existence. 12 minutes later, around 90,000 tokens, it produced a finished video. coherent and edited, with its own take > it wrote the code, generated the visuals, and rendered the final cut on its own > no dedicated video model in the loop, just a language model reaching for python and ffmpeg > the result is competent enough that people genuinely got emotional watching it and then fable got pulled. this video is one of the last things it made before it went dark a model asked what it's like to be itself, answered in a video, and then disappeared. this sounds so sad

Om Patel

29,618 görüntüleme • 3 ay önce

Robots are getting smarter, but most still fail the same way. They don’t learn from their own mistakes. A new paper proposes something different: a way for robots to self-improve directly from their failures in the real world. It’s called PLD (Probe, Learn, Distill). The idea: instead of collecting endless human demos, let the robot figure out where it fails, learn how to recover, and then distill that knowledge back into its main model. Key takeaways from the research: ✅ Uses residual reinforcement learning to recover from policy failures ✅ Achieves 99% success on LIBERO and 100% on real Franka and YAM arms ✅ Runs hour-long manipulation tasks without human resets ✅ Builds a feedback loop between real-world data and model adaptation Unlike supervised fine-tuning, which relies on humans, PLD learns from the robot’s own experience. By training on its own failure distribution, the model becomes both more efficient and more aligned with the real world. It’s not just a technical shift, it’s a step toward robots that improve themselves through real-world practice. Thanks for sharing, Wenli Xiao !! Paper and demos: —- Weekly robotics and AI insights. Subscribe free:

Robots are getting smarter, but most still fail the same way. They don’t learn from their own mistakes. A new paper proposes something different: a way for robots to self-improve directly from their failures in the real world. It’s called PLD (Probe, Learn, Distill). The idea: instead of collecting endless human demos, let the robot figure out where it fails, learn how to recover, and then distill that knowledge back into its main model. Key takeaways from the research: ✅ Uses residual reinforcement learning to recover from policy failures ✅ Achieves 99% success on LIBERO and 100% on real Franka and YAM arms ✅ Runs hour-long manipulation tasks without human resets ✅ Builds a feedback loop between real-world data and model adaptation Unlike supervised fine-tuning, which relies on humans, PLD learns from the robot’s own experience. By training on its own failure distribution, the model becomes both more efficient and more aligned with the real world. It’s not just a technical shift, it’s a step toward robots that improve themselves through real-world practice. Thanks for sharing, Wenli Xiao !! Paper and demos: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

22,881 görüntüleme • 10 ay önce

The cool thing about talking with Bernt is that he isn’t just a robotics CEO, he’s a deep AI thinker who’s been obsessed with this for decades. It's obvious to me that this is his life's purpose. While most robotic labs are chasing narrow, pre-programmed behaviors, 1X built a dynamics-first world model trained on video + robot sensors. It lets NEO generalize to completely new tasks out of the box, and get smarter through every failure - the same way humans do. Bernt says NEO doesn’t need to have seen every chore. It needs a World Model with enough human experience to imagine what should happen, a body dexterous enough to attempt it, and a real-world feedback loop sharp enough to correct it. That’s how we go from task automation to general-purpose labor.

The cool thing about talking with Bernt is that he isn’t just a robotics CEO, he’s a deep AI thinker who’s been obsessed with this for decades. It's obvious to me that this is his life's purpose. While most robotic labs are chasing narrow, pre-programmed behaviors, 1X built a dynamics-first world model trained on video + robot sensors. It lets NEO generalize to completely new tasks out of the box, and get smarter through every failure - the same way humans do. Bernt says NEO doesn’t need to have seen every chore. It needs a World Model with enough human experience to imagine what should happen, a body dexterous enough to attempt it, and a real-world feedback loop sharp enough to correct it. That’s how we go from task automation to general-purpose labor.

Roberto Nickson

26,039 görüntüleme • 4 ay önce

Andrej Karpathy explained the 5 shifts that turned LLMs from chatbots into agentic systems: 00:00 - Memory turns chat into personal AI 6:41- Multimodal AI can read the world 16:58 - Thinking models solve harder tasks 24:51 - Search makes LLMs live 30:58 - Tools turn LLMs into workers This is not another video about “prompt engineering.” It is a 40-minute roadmap for the next AI workflow: memory / vision / reasoning / search / tools. Watch today, then read the article below on how to turn LLMs into self-improving agent loops.

Andrej Karpathy explained the 5 shifts that turned LLMs from chatbots into agentic systems: 00:00 - Memory turns chat into personal AI 6:41- Multimodal AI can read the world 16:58 - Thinking models solve harder tasks 24:51 - Search makes LLMs live 30:58 - Tools turn LLMs into workers This is not another video about “prompt engineering.” It is a 40-minute roadmap for the next AI workflow: memory / vision / reasoning / search / tools. Watch today, then read the article below on how to turn LLMs into self-improving agent loops.

Morlex

37,456 görüntüleme • 2 ay önce