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LLM-based judges, widely used for automated evaluation, are riddled with diverse biases that can be significantly reduced through bias-aware training using RL and contrastive learning.
Cycle-consistent training unlocks robust layered image decomposition in diffusion models, even with complex interactions like shading and reflections.
By explicitly modeling specular effects with view-dependent opacity, this augmented Gaussian Splatting method leapfrogs NeRFs in rendering performance and parameter efficiency.
