Neural Esperanto: Learning to Communicate Across Frozen Language Models

Can different language models exchange symbols, add information, and pass the result to an unfamiliar receiver? We introduce Neural Esperanto, a shared communication interface for frozen language models. Sending and receiving adapters generate discrete symbols one at a time, with each symbol read back before the next is produced. Training uses a reference code derived from English. Qwen sends facts to Mistral, which adds its own facts and sends a message to Phi. Phi learns from Qwen/Llama messages only and answers questions revealed after both messages are complete. On 1,152 new spatial worlds with unseen entity–distance assignments, two runs achieve 97.05% and 97.14% accuracy, compared with 97.40% for a similarly trained text-based chain. Both runs stay within the predefined two-percentage-point margin of text. Even with access to all test labels, a predictor that only counts symbols reaches only 34.95% and 33.33%; replacing messages makes answers follow the replacement facts. Controlled comparisons show that training the receiving adapter improves both reading incoming messages and generating new ones. These results show that frozen models can communicate using a learned interface derived from English, while developing independent meanings and grammar remains an open challenge.