Researchers from Meta's FAIR and UC San Diego are exploring ways to enhance large language models (LLMs) by allowing them to reason in "latent space," which is the hidden layer before generating language. Their COCONUT model encodes logical processes as "latent thoughts," enabling simultaneous reasoning and efficient backtracking, particularly in complex logical problems. While traditional models struggle with intricate conditions, this approach shows promise for improving accuracy and generalization in reasoning tasks.
