Machine Learning
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Gamma-World Models Many Agents at Once, Not Just Two Players
Gamma-World is an NVIDIA research effort in generative world modeling — models that learn to simulate an environment’s dynamics so agents can plan and act inside an imagined version of the world. Its specific target is the part most world models dodge: scenes with many interacting agents, not just two. ## Beyond the two-player case… Continue reading
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MUSE-Autoskill Lets Agents Write, Store, and Grade Their Own Skills
MUSE-Autoskill is a framework for self-evolving agents — agents that don’t just run a fixed toolset but build, store, and refine their own skills as they work. Its structure is three coupled pieces: skill creation, memory management, and evaluation. ## Closing the self-improvement loop Most agents are static after deployment: they fail the same way… Continue reading
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