EASEL is a benchmark evaluating multimodal agents’ ability to perform dexterous visual tool use through reference-guided visual reconstruction tasks, accompanied by EASEL-Data, a 440,000-sample curriculum dataset, and EASEL-9B, a fine-tuned model showing improved performance. Evaluating 25 models reveals significant challenges maintaining closed-loop stability and precision in visual tool manipulation.
