Today, we’re introducing [schema]: a harness reaching 99% RHAE with Opus 4.8 + Fable 5 and 95.35% with GPT-5.6 Sol on ARC-AGI-3 Public set.
[schema] makes an LLM think like a physicist. 🧵
What makes long-horizon planning so interesting to me is the trap: a short-term plan can look promising yet make the overall task impossible!
With Agima, imagined futures and real observations share the same visual interface. The agent can use a generated future image as its
We trained an imaginative agent! ✨
Agima: an omni agent that plans by imagining futures.
𝗦𝗙𝗧: Directly generalizes to harder tasks! 𝟮%→𝟰𝟬%, beats GPT-5.6 Sol (𝟮𝟬%).
𝗥𝗟: New ways to imagine emerge! Imagines a second plan only if the first one fails (𝟯𝟮%→𝟭𝟬𝟬%).
1/ RL for improving task success has made LLMs remarkably capable. But what happens when the model fails? Turns out, training for graceful failure can be surprisingly tricky!
Very excited to share our new paper: Training Language Models to Fail Gracefully
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Long-horizon video generation AND persistent visual memory? 🧠
That’s what MosaiChunk brings to autoregressive video models. Code, data, and checkpoints are out today!
Check it out: mosaichunk.github.io
Now autoregressive video models can remember. 🧠
Introducing MosaiChunk: persistent visual memory for long-horizon autoregressive video generation🧩.
Instead of remembering everything, it remembers the right things. ✍️
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Now autoregressive video models can remember. 🧠
Introducing MosaiChunk: persistent visual memory for long-horizon autoregressive video generation🧩.
Instead of remembering everything, it remembers the right things. ✍️
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