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🚨 The biggest challenge for interactive world models isn't image quality. It's drift Small prediction errors compound over time until the generated world falls apart. That's exactly what LingBot-World 2.0 (LingBot-World-Infinity) sets out to solve. A few things that stood out to me 🧵👇

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Md Riyazuddin

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51,174 просмотров • 7 дней назад •via X (Twitter)

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One of the biggest challenges LingBot-World 2.0 (Infinity) is trying to address isn't image quality. It's keeping an interactive world coherent over long periods. Once a model starts conditioning on its own predictions, small errors accumulate, eventually leading to visual drift. Here's how it works:
2:04

One of the biggest challenges LingBot-World 2.0 (Infinity) is trying to address isn't image quality. It's keeping an interactive world coherent over long periods. Once a model starts conditioning on its own predictions, small errors accumulate, eventually leading to visual drift. Here's how it works:

Markandey Sharma

72,478 просмотров • 8 дней назад

LingBot-World 2.0 (Infinity) is out on Hugging Face interactive world model with: Hour-long generation with zero quality drift Rich actions & events: attack, cast spells, shoot, summon storms Agentic world: a Director Agent drives real-time world evolution 720p/60fps. Playable like a game
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LingBot-World 2.0 (Infinity) is out on Hugging Face interactive world model with: Hour-long generation with zero quality drift Rich actions & events: attack, cast spells, shoot, summon storms Agentic world: a Director Agent drives real-time world evolution 720p/60fps. Playable like a game

AK

28,721 просмотров • 9 дней назад

Most AI world models impress you for 20 seconds. The harder problem is making them stay coherent. That's what stood out to me in Robbyant's LingBot-World-Infinity. Instead of chasing another resolution headline, the paper focuses on reducing autoregressive drift the issue where small prediction errors compound until the world slowly falls apart. Their stress test is a single uninterrupted 60-minute rollout across 20 different scenarios with no visible quality decay. If you want to see the interactive system yourself, it's live on Reactor: 🎮 I also like that they don't pretend it's perfect. The paper openly says it doesn't have true long-term memory. Leave an area and come back later, and it regenerates something plausible instead of recalling the exact same place. That's a refreshingly honest limitation. Open weights make it even more interesting to dig into. Robbyant For anyone who wants to explore the research: 💻 GitHub: 🤗 Model Weights:
1:04

Most AI world models impress you for 20 seconds. The harder problem is making them stay coherent. That's what stood out to me in Robbyant's LingBot-World-Infinity. Instead of chasing another resolution headline, the paper focuses on reducing autoregressive drift the issue where small prediction errors compound until the world slowly falls apart. Their stress test is a single uninterrupted 60-minute rollout across 20 different scenarios with no visible quality decay. If you want to see the interactive system yourself, it's live on Reactor: 🎮 I also like that they don't pretend it's perfect. The paper openly says it doesn't have true long-term memory. Leave an area and come back later, and it regenerates something plausible instead of recalling the exact same place. That's a refreshingly honest limitation. Open weights make it even more interesting to dig into. Robbyant For anyone who wants to explore the research: 💻 GitHub: 🤗 Model Weights:

Shruti Codes

15,066 просмотров • 7 дней назад

I've been reading through the LingBot-World 2.0 paper today, and I think one detail deserves more attention than the hour-long demo itself. Most interactive world models can generate impressive moments, but keeping an explorable world coherent over long sessions is a much harder problem errors gradually accumulate. LingBot-World 2.0 tackles that challenge by upgrading both world prediction and interaction, sustaining hour-level generation at stable 720p and 60 fps while remaining responsive in real time. The paper argues this comes from its causal training strategy, rather than simply extending generation length. Robbyant
0:48

I've been reading through the LingBot-World 2.0 paper today, and I think one detail deserves more attention than the hour-long demo itself. Most interactive world models can generate impressive moments, but keeping an explorable world coherent over long sessions is a much harder problem errors gradually accumulate. LingBot-World 2.0 tackles that challenge by upgrading both world prediction and interaction, sustaining hour-level generation at stable 720p and 60 fps while remaining responsive in real time. The paper argues this comes from its causal training strategy, rather than simply extending generation length. Robbyant

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113,396 просмотров • 6 дней назад

Robbyant just open-sourced LingBot-VLA 2.0 and it's much more than a bigger Vision-Language-Action model. Behind the 60,000-hour pretraining dataset run are several clever engineering ideas that push robots closer to reliable real-world deployment. Here are 6 things that stood out to me 👇
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Robbyant just open-sourced LingBot-VLA 2.0 and it's much more than a bigger Vision-Language-Action model. Behind the 60,000-hour pretraining dataset run are several clever engineering ideas that push robots closer to reliable real-world deployment. Here are 6 things that stood out to me 👇

Md Riyazuddin

54,251 просмотров • 8 дней назад

Most world models fall apart after a few seconds. Common failure modes include texture smearing, warped geometry, and scenes that no longer look real. LingBot-World 2.0 from Robbyant seems to hold 720p at 60 fps for a full hour of interaction. That’s impressive. Here is what makes that possible.
1:12

Most world models fall apart after a few seconds. Common failure modes include texture smearing, warped geometry, and scenes that no longer look real. LingBot-World 2.0 from Robbyant seems to hold 720p at 60 fps for a full hour of interaction. That’s impressive. Here is what makes that possible.

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🚨🇨🇳HOW CHINA WENT FROM ONE OF THE POOREST COUNTRIES IN THE WORLD TO THE BIGGEST ECONOMY IN THE WORLD (Adjusted for PPP), AND LIFTED OVER 850 MILLION PEOPLE OUT OF POVERTY! ✅WHAT HAS CHINA DONE FOR LESS FORTUNE COUNTRIES? #China #US #Multipolar #World
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🚨🇨🇳HOW CHINA WENT FROM ONE OF THE POOREST COUNTRIES IN THE WORLD TO THE BIGGEST ECONOMY IN THE WORLD (Adjusted for PPP), AND LIFTED OVER 850 MILLION PEOPLE OUT OF POVERTY! ✅WHAT HAS CHINA DONE FOR LESS FORTUNE COUNTRIES? #China #US #Multipolar #World

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More jobs, more food, more hope: that's exactly what World Food Programme resilience programs are delivering around the world. Check out how World Food Programme_WAfrica is working with local communities to literally plant the seeds of a brighter future for families across the #Sahel!

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Labor has presided over the biggest collapse in living standards in the developed world. It's time the Prime Minister set out a plan to fix this mess.
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🤖Robots that think ahead and act in real time. LingBot-VA 2.0 — the first embodied-native foundation model. Not fine-tuned from a video generator. Built from scratch for the physical world. ✅ 93.6% success on bimanual tasks ⚡ 150 Hz single-GPU inference 🎯 20 demos to generalize. This is what happens when you stop adapting and start building natively. 🧵👇
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🤖Robots that think ahead and act in real time. LingBot-VA 2.0 — the first embodied-native foundation model. Not fine-tuned from a video generator. Built from scratch for the physical world. ✅ 93.6% success on bimanual tasks ⚡ 150 Hz single-GPU inference 🎯 20 demos to generalize. This is what happens when you stop adapting and start building natively. 🧵👇

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1/ Tencent releases HY World 1.5 (WorldPlay), an open-source real-time world model with 24fps A streaming video diffusion model that enables real-time, interactive world modeling with long-term geometric consistency In direct comparison to Genie 3, it's technically inferior, and there's no information on how long the consistency will last. However, Tencent has released a significant world model as open source for the first time, making it available to everyone. A few more examples, a thread 🧵
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1/ Tencent releases HY World 1.5 (WorldPlay), an open-source real-time world model with 24fps A streaming video diffusion model that enables real-time, interactive world modeling with long-term geometric consistency In direct comparison to Genie 3, it's technically inferior, and there's no information on how long the consistency will last. However, Tencent has released a significant world model as open source for the first time, making it available to everyone. A few more examples, a thread 🧵

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The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped. Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline. A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation. LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud. Numbers on LingBot-Depth 2.0 - Excels on glass, mirrors & transparent objects — where traditional depth cameras fail - Training data: 3M → 150M (50x scale-up) - 12 out of 16 first-place rankings on depth completion benchmarks - RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale. The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.
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The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped. Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline. A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation. LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud. Numbers on LingBot-Depth 2.0 - Excels on glass, mirrors & transparent objects — where traditional depth cameras fail - Training data: 3M → 150M (50x scale-up) - 12 out of 16 first-place rankings on depth completion benchmarks - RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale. The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.

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25,312 просмотров • 11 дней назад

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World models are moving beyond offline generation towards interactive, real-time experiences. Introducing ⚡FlashDreams⚡: an open-source high-performance inference and serving library built for autoregressive world models: 🔥 Up to 3.10× faster LingBot-World inference 🔥 Up to 2.12× faster Self-Forcing inference 🔥 Up to 1.40× faster Wan2.1 inference 🔥 8 integrated models 🔥 Multi-GPU, streaming, low-latency serving 🔥 Agentic skills that teach you how to use it FlashDreams is designed for a new generation of AI systems that continuously evolve over time while responding to user interactions. It powers applications across robotics, autonomous vehicle simulation, gaming, and virtual worlds. Github: Docs: Research page: Join the #flashdreams Discord channel at FlashDreams is also the runtime backbone behind NVIDIA OmniDreams ( 1/n #AI #WorldModels #FastInference #PhysicalAI #OpenSource #NVIDIA
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World models are moving beyond offline generation towards interactive, real-time experiences. Introducing ⚡FlashDreams⚡: an open-source high-performance inference and serving library built for autoregressive world models: 🔥 Up to 3.10× faster LingBot-World inference 🔥 Up to 2.12× faster Self-Forcing inference 🔥 Up to 1.40× faster Wan2.1 inference 🔥 8 integrated models 🔥 Multi-GPU, streaming, low-latency serving 🔥 Agentic skills that teach you how to use it FlashDreams is designed for a new generation of AI systems that continuously evolve over time while responding to user interactions. It powers applications across robotics, autonomous vehicle simulation, gaming, and virtual worlds. Github: Docs: Research page: Join the #flashdreams Discord channel at FlashDreams is also the runtime backbone behind NVIDIA OmniDreams ( 1/n #AI #WorldModels #FastInference #PhysicalAI #OpenSource #NVIDIA

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.Balaji says AI verification is much harder in the digital world than it is for the physical world: "AI for the physical world is very verifiable because the thing is, the digital world is fundamentally decentralized in a way the physical world isn't." "There's only one physical world. You can say: did the AI move this box from this pallet to that pallet? That is something where you can probably get it to 100% over time." "By contrast, in the digital world, there are all these people who live in their own constructed environments." "How do you know when you're done with your to-do list? That's harder. Those things are fuzzier."

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Introducing Modality Forcing, a recipe for post-training T2I models for SOTA RGB-Depth generation! Text-to-image (T2I) models learn rich representations of the spatial world. How do we build on this prior for high-quality depth generation? 🧵 [1/6]

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