Daichi Yashima

Rigel achieves over 10-point improvements in image and video captioning evaluation by aligning automatic metrics with human judgment without relying on large vocabulary sets.

Achieving 33× faster robot flow generation while improving success rates in manipulation tasks could redefine efficiency benchmarks in robotic motion planning.

ELSA outperforms existing metrics by aligning audio evaluations with human judgments at the level of individual acoustic events, transforming TTA assessment.

Turns out, your MLLM judge is probably biased: MLLMs tend to prefer their own generations and those from similar architectures, potentially skewing benchmark results.