  In this paper, we present Cross Language Agent -- Simultaneous
Interpretation, CLASI, a high-quality and human-like Simultaneous Speech
Translation (SiST) System. Inspired by professional human interpreters, we
utilize a novel data-driven read-write strategy to balance the translation
quality and latency. To address the challenge of translating in-domain
terminologies, CLASI employs a multi-modal retrieving module to obtain relevant
information to augment the translation. Supported by LLMs, our approach can
generate error-tolerated translation by considering the input audio, historical
context, and retrieved information. Experimental results show that our system
outperforms other systems by significant margins. Aligned with professional
human interpreters, we evaluate CLASI with a better human evaluation metric,
valid information proportion (VIP), which measures the amount of information
that can be successfully conveyed to the listeners. In the real-world
scenarios, where the speeches are often disfluent, informal, and unclear, CLASI
achieves VIP of 81.3% and 78.0% for Chinese-to-English and English-to-Chinese
translation directions, respectively. In contrast, state-of-the-art commercial
or open-source systems only achieve 35.4% and 41.6%. On the extremely hard
dataset, where other systems achieve under 13% VIP, CLASI can still achieve 70%
VIP.
