LatentDx: Latent Multi-Agent Communication for Cross-Hospital Rare-Disease Diagnosis

Rare diseases affect approximately $300$ million patients across more than $7,000$ conditions, yet no single hospital encounters enough cases of any one condition for reliable diagnosis. Cross-hospital collaboration could help by allowing a diagnosing institution to use distributed, case-specific diagnostic evidence, but privacy regulations restrict the transmission of identifiable clinical text across institutional boundaries. This setting raises two challenges: existing medical agent systems often rely on textual evidence exchange, while raw latent states such as hidden states and KV caches may still reveal prompt-derived clinical content. We introduce LatentDx a latent multi-agent communication framework in which hospital agents keep private clinical records and retrieved cases local, and send compact latent representations to a host agent for rare-disease diagnosis. In the same-backbone setting, LatentDx-H transmits distilled KV blocks. In the cross-family setting, LetentDx-X transmits compact hidden-state sequences for projection into host input embeddings. On CrossRare-Bench, a self-built large-scale rare-disease benchmark with hospital-level partitions, LatentDx supports strong cross-hospital diagnostic performance, with evaluations in both settings also showing reduced reconstructable clinical content relative to raw-latent communication baselines. Code and data are available at https://anonymous.4open.science/r/LatentDx/.