MUX packs multiple reasoning steps into a single token for faster chain‑of‑thought
MUX trains latent tokens to encode a lossless superposition of a span of reasoning subwords, letting models convey several intermediate steps in one token. This cuts the chain‑of‑thought bottleneck and improves performance across 32 tasks, beating prior latent‑reasoning baselines. It also enables parallel search.