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Source-validation accuracy is a poor proxy for out-of-distribution reliability; re-ranking checkpoints by aggregated source reliability metrics significantly boosts target probability quality without needing target data.
LASP is introduced, a Language-Augmented Semantic Priors framework that leverages large language models to infer structured, solver-usable B-spline priors from procedural modeling histories, establishing a new paradigm of language-guided geometric optimization in modern CAD systems.
This work introduces Chunk-Aligned Semantic Distillation (CASD), which derives semantic targets for entire action chunks from the current observation, robot state, and task instruction, and freezes the generator to train a policy conditioned on its predictions.
