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LLM workflows can be optimized without human labels by treating agent assignment and task decomposition as a bidding process, slashing optimization costs.
Achieving unprecedented consistency in cross-representation learning, CoCoEvolve outperforms existing methods by leveraging one-to-one correspondences without extra annotations.
CURV transforms chart question answering by embedding dynamic visual grounding into a structured learning curriculum, leading to unprecedented improvements in reasoning accuracy.
