The Signal

Everything that matters in AI, with our take.

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Latent SpaceArticle3d ago

Dyna-2: General Purpose Physical Agents with Hour-Long Autonomy — Jason Ma, Dyna Robotics

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.

agents robotics world-models dyna

arXiv cs.AIPaper9d ago

RoboJEPA: Scaling Robotic Latent World Models

A reliable power law for world model scaling is rare and valuable. The fact that imagination error predicts real-robot performance means you can tune models in simulation and trust the results will transfer. If you're investing in robotic learning systems, this gives you a principled way to estimate how much compute to allocate and validates the approach broadly rather than for one embodiment.

research robotics scaling-laws world-models

arXiv cs.AIPaper9d ago

Long-WAM: Scaling the Context of World-Action Models

The key finding is that context length matters far more when pretraining preserves causal structure. If you're building robot control systems with video models, this tells you to prioritize autoregressive pretraining and invest in longer context windows rather than architectural novelty. The scaling law they demonstrate is reliable enough to predict performance at untested scales.

research robotics world-models