continuous-reasoning

MUX proposes a method for lossless continuous reasoning by distilling discrete reasoning steps into multiplexed latent tokens that encode a superposition of subwords, achieving higher bandwidth and enabling parallel exploration in language model reasoning tasks.

Proposes Asymmetric Mutual Variational Learning (AMVL) to resolve train-inference mismatch in multimodal continuous reasoning by using bidirectional calibration to prevent answer leakage and improve latent-space stability, achieving significant gains on the BLINK benchmark.