Aether AI (Causal Intelligence) @AetherLab_AI
Towards Real-World Causal Intelligence Founded by @huang_biwei aetherlabs.ai Joined April 2026-
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The path to Aether AI began long before the company itself. Causal learning taught us to look beyond mere patterns and unlock the mechanisms that drive outcomes. Foundation models made it possible to do this at scale. We’re building the infrastructure for Causal Intelligence, with a horizon that includes physical AI, computer use, forecasting, and scientific discovery.
As our founder @huang_biwei shares, CRIS-0 brings our causal world model and causal agent together on a physical robot for the first time. Throughout the demo, one reasoning loop handles changing tasks and live disruptions, from a metal can on the plate to a hand in the door's path. Physical AI is our first proving ground, and the same architecture lays the foundation for generalizing to very different domains. x.com/huang_biwei/st…
I have spent more than a decade studying causality. For me, causality is about understanding the rules that govern how actions change the world, and reasoning about their consequences before acting. Scaling has made today's models remarkably capable at spotting patterns and
@svpino Thanks! Excited to keep building!
This is CRIS-0, Aether AI's first causal robotic intelligence system, operating in a real home. It reasons about how its actions will change the world, checks what actually happened, and adapts when reality doesn't go as expected.
@GithubProjects Thank you for the support!
@_jaydeepkarale Exactly. That's a key part of what we're building.
@Whats_AI Thank you! We’re really excited about this one
@VoidAsuka Thanks! Excited to share CRIS-0 with everyone
A failed step should not derail an entire task. Built on Aether AI's causal world model and causal agent, CRIS-0 is designed to make long-horizon tasks more reliable. It breaks complex tasks into stages with observable completion conditions, verifies outcomes before moving forward, and adapts when reality diverges from expectation. When something goes wrong, CRIS-0 can retry, roll back to an earlier stage, or revise its plan, rather than blindly continuing down the wrong path. Read the full piece on TechCrunch: techcrunch.com/sponsor/aether…
@lyndsyfonsecaX That’s the point! CRIS-0 takes a broad request and works out the next steps based on each object’s state.
@MayaLeClarke If the current step can be retried, it tries it again. If that’s not enough, it replans based on the updated task state.
Under the hood, CRIS-0 predicts how actions can change the state of the world, plans around those predictions, and checks what actually happens to inform its next move. Predict. Act. Verify. Adapt. Full write-up: aetherlabs.ai/articles/real-…
We asked CRIS-0 to tidy the living room. Some cans were empty. Others still had drinks in them. CRIS-0 kept track of their states, threw away the empties, and set the unfinished drinks aside.
@_lexers Forecasting, scientific discovery, and plenty more to explore!
Today's AI is remarkably good at recognizing patterns. But in the real world, patterns can only take us so far. When a robot acts, every action changes its environment. To operate reliably, it needs to understand not just what to do next, but what its actions will cause and how to adapt when reality doesn't go as expected. At Aether AI, we're building AI that reasons about cause and effect. We started with robotics, where causality is tangible and every action has a clear consequence. But the ideas we're developing extend far beyond the physical world. Check out our new brand film which tells the story!
One thing leads to another. Stay tuned! aetherlabs.ai
A domino falls. An avalanche begins. Different scales. Different phenomena. Yet beneath the surface, similar chains of cause and effect unfold. Look beyond the surface. Discover what drives it.
That’s a wrap on #IROS2026! Huge thanks to everyone who stopped by our booth for the sharp questions, thoughtful challenges, and deep conversations about Causal Intelligence. Plenty for us to unpack, and so many conversations we can't wait to continue. What a ride. Let’s keep the momentum going! @ieeeiros #IROS #CausalAI
Early errors can snowball in long-horizon robotic tasks. If a small deviation goes undetected, the robot may continue acting on an incorrect state estimate, causing later decisions to drift further from reality. We address this with our "causal brain", which predicts how actions should change task-relevant state variables over time. After each action, it compares the predicted transition with what actually happened. These discrepancies provide a continuous calibration signal, helping the system discover new causal factors and relationships, update outdated knowledge, and adapt as the physical world evolves. #CausalIntelligence
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