Boundary-Resolved Answer Relays in Latent Reasoning

How does answer information pass from continuous latent reasoning to discrete generation? We test whether the contextual cache of a naturally generated answer prefix relays information from native latent states. We introduce Boundary-Resolved Answer Transport and Sensitivity, a protocol that crosses latent and prefix caches, recomputes the terminal reader, and measures donor-answer transfer separately from destructive sensitivity. In CODI-GPT-2, native latent values repeatedly write answer information into the contextual values of “The answer is,” which terminal attention heads combine to produce an answer. On 64 new arithmetic pairs, the fixed setup yields 61 donor-answer transfers and 64 recipient recoveries under unrestricted greedy generation. All three prefix positions are required by the native-patching criterion, but redistributing their mean value difference at matched readout dose enables single-position control. In two prospectively selected Llama-based checkpoints, the same bidirectional test supports relay sufficiency in SIM-CoT, whereas Zen-E falls below the sufficiency threshold. A separate fresh-input study shows that Zen-E's patched prefix can recover the original donor answer with latent support from different-answer problems, although latent content still influences the output. Complementary boundary studies show persistent latent-answer transport and transport–sensitivity dissociation. Together, these findings support an operator-defined latent-to-prefix relay and show why answer-transport claims must specify the intervention, upstream state, and decoding boundary.