  This study introduces a groundbreaking approach to simultaneous
interpretation by directly leveraging the predictive capabilities of Large
Language Models (LLMs). We present a novel algorithm that generates real-time
translations by predicting speaker utterances and expanding multiple
possibilities in a tree-like structure. This method demonstrates unprecedented
flexibility and adaptability, potentially overcoming the structural differences
between languages more effectively than existing systems. Our theoretical
analysis, supported by illustrative examples, suggests that this approach could
lead to more natural and fluent translations with minimal latency. The primary
purpose of this paper is to share this innovative concept with the academic
community, stimulating further research and development in this field. We
discuss the theoretical foundations, potential advantages, and implementation
challenges of this technique, positioning it as a significant step towards
democratizing multilingual communication.
