Yoon Kim

A simple triadic outer product expansion of linear attention's state dramatically boosts long-context modeling capacity without a proportional parameter increase.

This work proposes a two-agent framework that first identifies the firms for which each alternative data channel is likely to be informative and then predicts revenue using firm- and channel-specific context and evaluates the framework across four commercial alternative data channels.

A single neuron can recalibrate LLM investment biases, enabling precise control over decision-making without altering the model's architecture or prompts.

Turing-RL reveals that training user simulators for indistinguishability can dramatically improve their performance in simulating human interactions.

Nonuniform width allocation in transformers can lead to a 22% reduction in FLOPs while enhancing language modeling performance.

Halving the parameter count of LLMs without sacrificing performance is now possible with Hyperloop Transformers, thanks to looped layers and hyper-connected residual streams.