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Chemical VLMs can achieve SOTA performance by prioritizing reasoning through fine-grained analysis of visual inputs, such as identifying functional groups, before generating answers.
Stop guessing why your GUI agent failed: GUIDE breaks down complex trajectories into interpretable subtasks, pinpointing errors and suggesting fixes with unprecedented accuracy.
