This week
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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
This is genuinely novel: turning empirical exploration into formal proofs is a hard problem, and AWM does it by building models iteratively and letting category theory guide abstraction. For builders of reasoning systems, this shows a path past the memorization wall: agents that can prove what's actually impossible in a domain, not just what they've seen. The constraint is complexity; it works on game puzzles now.
Hour-long autonomy on physical robots is a meaningful threshold. World Action Models—learned predictive models of environment dynamics—are a real technical challenge that affects whether agents can plan and act reliably over extended horizons. If Dyna's approach is working, this signals progress on a bottleneck that matters for real deployment. Read the episode to understand their model architecture.