Aether AI (Causal Intelligence)

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Aether AI (Causal Intelligence)

Aether AI (Causal Intelligence)

@AetherLab_AI

Causal World Models for Real-World Intelligence

Katılım Nisan 2026
82 Takip Edilen938 Takipçiler
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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
Welcome to Aether AI! ✨ We're building the next generation of intelligence: Causal World Models. Forget correlations. We're focused on AI that truly understands underlying mechanisms, reasons about interventions, and operates reliably in the real world. This isn't just about bigger models; it's about fundamentally smarter, safer #PhysicalAI and #EmbodiedAI. Our work is led by our founder, Prof. Biwei Huang @huang_biwei, whose vision drives us to redefine what's possible in AI. Follow us for technical insights, team milestones, behind-the-scenes glimpses, and our vision for a smarter, more reliable future. Join our journey as we redefine intelligence itself! Learn more: aetherlabs.ai #AetherAI #CausalAI #AIResearch #FutureOfAI
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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
Machines’ understanding of humans is not just a perception problem. For robots, it becomes a question of action: what people are trying to do, how intention shows up in movement, and what changes in the physical world after action is taken. This is why causal world models matter for physical AI. Our founder @huang_biwei will join Forum 1: Machine Understanding & Human–Robot Coexistence at the Humanity & AGI Summit 2026 on July 12 at Stanford. Aether AI will also have a booth at the summit. Stop by if you’ll be there. #CausalAI #EmbodiedAI #Robotics #WorldModels
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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
World models don’t need to predict every pixel. They need the right state. TC-WM extracts a compact, task-centric latent from frozen visual features with a single linear projection, then plans in it: sharper world-model rollouts and more precise control, surpassing DINO-WM on every manipulation task. Research from UC San Diego, led by Prof. Biwei Huang (Aether’s founder), with Minghao Fu, Fan Feng, and Nicklas Hansen. This is the direction we care about: world models that are compact, physically grounded, and built for control. -Technical blog: aetherlabs.ai/articles/task-… -Paper: arxiv.org/abs/2605.25620 -Project: minghaofu.com/tc-wm #WorldModels #Robotics #RobotLearning #EmbodiedAI #AI #PhysicalAI #AetherAI
Biwei Huang@huang_biwei

1/ Most world models focus on generating high-fidelity futures: realistic videos, longer rollouts with expressive visual foundation models. For control, we need more than visual fidelity. Instead, we aim for task-centric representations that reflect physical dynamics and task-relevant information, while still leveraging the scalability of visual foundation models. Introducing TC-WM – a task-centric world model that learns grounded dynamics in a compact latent space while keeping the scalability of video foundation models. - Technical blog: aetherlabs.ai/articles/task-… - Paper: arxiv.org/abs/2605.25620 - Project: minghaofu.com/tc-wm - Code: github.com/MinghaoFu/TC-WM By Minghao Fu, Fan Feng, Nicklas Hansen, and Biwei Huang. (UC San Diego) #WorldModels #RobotLearning #Robotics #EmbodiedAI #PhysicalAI #AI

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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
A great discussion at the Robotics & World Models Reading Club on where today’s world models begin to fall short in robotics. @huang_biwei shared her perspective on moving from prediction toward models that can support intervention, adaptation, and reasoning when environments change. Thanks to the @saturdayrobotic community for the thoughtful questions and discussion. #AetherAI #Robotics #WorldModels #PhysicalAI
Biwei Huang@huang_biwei

Thank you @saturdayrobotic for having me at the Robotics & World Models Reading Club. I shared why I think today’s LLMs, video generators, and JEPA-style models are important, but still not enough for robotics. A causal world model should not only predict what may happen next. It should also help us understand what causes what, and how the world changes when actions are taken. This is why I think causal feature learning, causal structure learning, and causal dynamics are all needed. #Robotics #WorldModels #CausalAI

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Wesley@Ambani_Wessley·
@AetherLab_AI @huang_biwei Congrats on the $20M seed! Causal World Models are exactly what AI needs to move beyond correlations to real intelligence. Excited for your progress, Prof. Huang!
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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
Aether AI has raised $20M in seed funding, backed by a group of leading global investors with deep expertise in artificial intelligence and frontier technologies. Founded by Prof. Biwei Huang（@huang_biwei）. We're building Causal World Models for Real World Intelligence. 🔗aetherlabs.ai
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Partnerly
Partnerly@Partnerly_us·
@AetherLab_AI @huang_biwei Congratulations @AetherLab_AI! 🎉 Most AI systems learn correlations. Causal World Models learn why — that's the leap from autocomplete to real intelligence. Excited to see where this goes. The $20M is well placed.
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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
Our founder @huang_biwei joined @Yuancheng for a conversation on world models, robotics, and the direction behind Aether AI. The discussion covers video generation, VLA, WAM, and why robotics requires models that can reason about actions, changes, and physical consequences in the real world. This is closely connected to the work we are doing at Aether AI as we build causal world models for Physical AI. We are also growing our team. Learn more at aetherlabs.ai #AetherAI #Robotics #CausalAI #PhysicalAI #EmbodiedAI #AIResearch
Biwei Huang@huang_biwei

I enjoyed joining @Yuancheng to talk about world models and robotics. The term “world model” is being used in many ways today, from video generation to VLA and WAM. In robotics, the question becomes more concrete. A model needs to understand not only what may happen next, but also how actions change the physical world. We discussed these directions, their limitations, and why I believe causal world models are an important path to explore.

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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
Last night @huang_biwei joined @GeekPark founder Jack Zhang to make the case for causal world models. The short version: scaling correlation hits a wall in the physical world. Causal structure — knowing why, not just what next — changes the economics of robotics. Our benchmarks vs conventional world models: 25–50% higher success rates, 5–10× fewer samples. Same data, deeper structure. One brain. Many robots. That's the bet. Full conversation → [weixin.qq.com/sph/AuNNpbY1ew]
GeekPark@GeekParkHQ

Why Hasn't Embodied AI Had Its GPT Moment? Last night on #GeekParkLive, Jack Zhang sat down with Biwei Huang @huang_biwei, founder of @AetherLab_AI. The question on the table: can next-token prediction ever really understand why things happen? And does that matter for physical AI? A few things from the conversation worth sitting with:

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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
Our founder @huang_biwei is doing something different tomorrow night! She'll be in conversation with Zhang Peng, Founder and President of @thegeekpark. Don't miss her unfiltered discussion on why causal world model is the next AI paradigm. Tomorrow, 9 PM Beijing time(06:00 AM PDT). #CausalAI #AetherAI #GeekPark
Biwei Huang@huang_biwei

Tomorrow night I'm doing something different. Usually I give structured talks — slides, data, the comfort of a prepared script. Tomorrow on @thegeekpark, I'll just be having a conversation with Zhang Peng, Founder and President of GeekPark. Live. Unfiltered. We'll talk about causal AI, why I started Aether AI, what we are working on, and why causal world model is the next AI paradigm. 9PM Beijing time ( 06:00 AM PDT). See you there. #CausalAI #AetherAI #GeekPark

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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
Aether AI ranked 2nd in the ManipArena Challenge at #CVPR2026! Proud of Junbo Huang and the team for this result. ManipArena was a tough real-robot manipulation challenge with 20 reasoning-intensive tasks. Big thanks to the organizers for putting this together. #ManipArena #Robotics #EmbodiedAI
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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
Prof. Biwei Huang shares Aether AI’s core perspective from @CVPR: Physical AI needs Causal World Models — systems that reason about interventions, counterfactuals, and causal structure, not just statistical patterns. #CausalAI #PhysicalAI #CVPR2026 #AetherAI
Biwei Huang@huang_biwei

1/4 At @CVPR, I presented a central question: Why do frontier AI models—from GPT to Sora—still struggle with the physical world? My answer: they learn statistical correlation, not causality. Physical AI needs Causal World Models. #CausalAI #PhysicalAI #CVPR2026 #AetherAI

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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
The standard response to robot failure is more data. Robot drops the cup? Collect more grasping examples. Robot gets stuck? Add more scenarios to the training set. Models underperform? Scale them up. This works, until it doesn't. Every new environment requires new data. Every new task requires new training. Every new failure requires human intervention. The model gets bigger. The brittleness doesn't always go away. The physical world creates too many variations to cover with examples alone. Liquid level can change. Friction can change. Contact point, force angle, and numerous underlying conditions can all change and combine. More data expands coverage. But coverage alone is not an efficient way to expose the mechanism behind success and failure. The transition in robotics may not simply be from small models to large models. It may be from systems that imitate more trajectories to systems that model underlying mechanisms and adapt decisions as environments change. That's one of the problems we're working on at Aether AI.
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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
🍻 Causal World Model Happy Hour #CVPR | @AetherLab_AI We're hosting a small (~30p), closed-door happy hour near the #CVPR venue — food, drinks, and the kind of unhurried conversation about world models, causality, embodied AI, and the decision brain of Physical AI that doesn't happen on the show floor. This is a invitation-only evening. We're hand-inviting a group of researchers and engineers whose work we follow — and we've opened a limited number of additional seats to apply. Because the room is intentionally small, every RSVP is reviewed. ✨ What we're building Aether AI is building causal world models — a new class of AI systems that understand mechanisms, reason under intervention, and operate reliably in real-world systems. We believe the next leap in AI won't come from simply scaling up to ever-larger models, but from paradigm-level innovation. ✨ Logistics 📅 Friday, June 5, 2026 · 18:00 – 21:00 📍 Walking distance from the CVPR venue, Denver. Exact location shared with approved guests by email. 🍸 Food and drinks will be provided. 🎟️ By approval only. Capacity is limited. You can also find us at Booth #715 any time during the Exhibition! 🎟️ Registration link: luma.com/wi0rtib8 🌐 aetherlabs.ai · 📩 contact@aetherlab-ai.com #WorldModel #PhysicalAI #EmbodiedAI #Causality #Robotics #AIforScience #AIHiring #BayAreaJobs #AIResearch #cvpr2026
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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
5/ Physics does not adapt to models. Models must adapt to physics. That is why Physical AI needs models of mechanisms, interventions, and consequences. And why we founded #AetherAI: to build causal world models that help agents act reliably, even when environments, constraints, and consequences change.
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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
4/ This is the core difference between imitating a past correlation and modeling the mechanism behind it. A correlation-based system may learn from experience: In scenes that look like this, grasping the handle usually lifts the cup cleanly. But things could be different in new scenes. If the hidden factors change — the cup is fuller, the surface is slipperier, the grasp angle is slightly different — the same action may no longer produce the same result. Instead, a causal model asks: if I apply this force, at this angle, under these physical conditions, what changes next?
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Aether AI (Causal Intelligence)
Aether AI (Causal Intelligence)@AetherLab_AI·
1/ ChatGPT makes a mistake. You correct it with words. A robot makes a mistake. It changes the world. A glass falls. Liquid spills. The next attempt starts from a different state. The cost is not just a wrong output. It is a wrong action with real consequences.
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Keşfet

@huang_biwei @saturdayrobotic @Ambani_Wessley @Partnerly_us @Yuancheng @GeekPark @thegeekpark @CVPR