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LLMs struggle with structured 2D tasks when inputs are serialized into 1D, revealing a surprising performance gap compared to vision-augmented models that directly process the 2D layout.
Unlock more diverse and effective LLM outputs by explicitly rewarding semantic novelty during decoding with Exploratory Sampling.
