InnoComm: Learning Decision-Relevant Latent Messages for Multi-Agent Language Models

When evidence is distributed across large language model agents, a communication interface must preserve the information needed for receiver decisions under a real byte budget. We introduce InnoComm, a sender-local latent codec that maps locally contextualized evidence into 128-dimensional slots, serializes them as INT8 packets, and delivers them to a set-consistent language-model receiver. The codec is trained to improve receiver decisions through task supervision, frozen-base behavior matching, and packet-receiver co-adaptation rather than full activation reconstruction. On two annotation-derived candidate-decision tasks, the uncompressed interface exhibits stable content utility across three receiver seeds on Development, with a separate component confirmation on a held-out FEVEROUS population. In single-receiver-seed Development experiments, a fixed-reader four-slot packet compresses the full FP16 hidden-bank payload by approximately $1,054\times$ to a mean of 1,428.872 application-layer bytes per request while lowering NLL by 0.044406 relative to the full base. Under a tighter 146-byte-per-sender budget, co-adapting the shared codec and memory adapter reduces NLL by $0.042625$ (95% CI $[0.019817,0.063856]$) relative to a matched codec-only continuation, while preserving content effects, base utility, and tested permutation consistency. A strong adapted text interface remains ahead by $0.044275$ NLL at the same mean byte budget. Together, these results show that co-adapting the shared packet and receiver interface improves compact latent communication under an actual wire budget.