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BDH-CQ scores 29.5% on ARC-AGI 1 at ~$0.0007 per task with latent-space reasoning (@omarsar0)
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BDH-CQ reaches 29.5% on ARC-AGI 1 at about $0.0007 per task by reasoning recurrently in latent space instead of chain-of-thought; its authors report Transformer-like scaling to 600B parameters.
In plain words
- A new system reportedly scored 29.5% on a problem-solving test at about $0.0007 per task.
- It repeatedly works through problems using hidden internal information instead of writing out each reasoning step.
- The authors also report testing larger versions containing up to 600 billion adjustable values.
- This could help teams run artificial intelligence problem-solving more cheaply without requiring written reasoning trails.
Appeared in
- In LangChain's benchmark, only 7% of agent turns needed a frontier model
Aug 12, 2026 · from X
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