This paper focuses on the methods that explain what neurons learn during the training process.

While advanced end-to-end models have achieved notable success in improving predictive performance, their integration and expansion pose significant challenges.

Many WSAD methods attribute their improvements to novel network architectures or loss functions, based on the authors' understanding of

Sparse mixture-of-experts are domain generalizable learners.

Existing methods like ensemble still struggle to aggregate these models with the desired higher-order communications. In this work, we propose Cola, a novel paradigm that coordinates multiple VLMs for visual reasoning.

Short-horizon tasks require few steps: e.g., go to a tree and chop it down, dig a hole.