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This paper studies empirical scaling properties for text conditioning in visual generation, showing that converged diffusion loss scales with structured language in prompts, and introduces methods to improve diffusability and promptability.
This research examines how deep Transformers with bidirectional masking achieve implicit deductive reasoning comparable to explicit chain-of-thought methods. The study demonstrates that algorithmically aligned models can scale reasoning capabilities across diverse graph topologies and problem widths.