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Turning published scientific figures back into executable Python remains a brittle frontier for top VLMs, with even frontier models routinely failing on multi-component layouts, precise axis semantics, and domain typography.
Early denoising stages in diffusion models are up to 2.58脳 more vulnerable to hardware noise, but a novel approach can mitigate this without retraining.
MatRank's innovative approach to pseudo-labeling reduces prediction error by effectively utilizing unlabeled data, achieving a remarkable NMAE of 0.896.
Accepting selective mismatches in autoregressive decoding can boost throughput by over 15% without any additional training or model adjustments.
