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A controlled evaluation framework is introduced that disentangles resolution-related performance degradation from task difficulty through semantics-preserving transformations and proposes two simple metrics: Area Under the Scaling Curve (AUSC), which quantifies scaling robustness independent of baseline accuracy, and P...
This work introduces a human-aligned evaluation framework for text-to-SVG generation, and develops two complementary evaluators: CLIP scorers adapted to vector graphics and then aligned to human preferences, for fast large-scale evaluation and a VLM judge trained with supervised fine-tuning and reward-shaped reinforcem...
This work proposes a novel test-time alignment approach that leverages trajectory-guided structured sampling for dynamic inference-time refinement, achieving better alignment with visual grounding and ensuring logical consistency, and demonstrates that this approach significantly improves accuracy without incurring pro...
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