Yikang Shen

A high-signal researcher for the post-attention design space, especially if you care about the line of work trying to make linear-attention and Delta-rule models actually competitive in real language-model systems.

A good page to have because he is one of the recurring names in the recent MIT line of work on linear-attention alternatives, especially where hardware-efficient training meets practical long-context sequence modeling.

Worth surfacing because he leads the Gated Slot Attention paper, which is one of the clearer attempts to push the RWKV-adjacent efficient-sequence line toward stronger memory and retrieval behavior rather than stopping at architecture novelty.

A useful researcher to study for the line from classic neural NLP into today’s efficient large-model work, with papers that span early sentence models, character-aware language modeling, and current sequence-model efficiency research.

A useful person to study if you care about alignment proposals that try to make superhuman systems legible enough for humans to supervise in practice.

A good researcher to follow for the infrastructure side of frontier language models, especially mixture-of-experts scaling, instruction tuning, and the data systems that make very large models usable.