latent-computation

This paper introduces Hidden Decoding, a sequence-length scaling method for LLMs that adds internal computation per token by expanding each token into multiple streams with independent embeddings, using Stream-Factorized Attention to keep costs low. Experiments on models up to 617B parameters show consistent improvements over baselines, demonstrating a practical fixed-backbone scaling path.