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Diffusion models can lose diversity when only later timesteps are updated during reward optimization, a finding that challenges prior assumptions and motivates a new training paradigm.
The authors', a persistent language field for structured Gaussian scenes that requires persistent semantic ownership, conserved evidence, and a hierarchy that balances stability, detail, and representation cost, is introduced.
iARCS transforms 3D scene generation by ensuring that synthetic environments meet essential functional constraints while maintaining diversity and realism.
