We've done this, and it works. Our setup is to have some agents that synthesize Prolog and other types of symbolic and/or probabilistic models. We then use thes

We've done this, and it works. Our setup is to have some agents that synthesize Prolog and other types of symbolic and/or probabilistic models. We then use these models to increase our confidence in LLM reasoning and iterate if there is some mismatch. Making synthesis work reliably on a massive set of queries is tricky, though. Imagine a medical doctor or a lawyer. At the end of the day, their entire reasoning process can be abstracted into some probabilistic logic program which they synthesize on-the-fly using prior knowledge, access to their domain-specific literature, and observed case evidence. There is a growing body of publications exploring various aspects of synthesis, e.g. references included in [1] are a good starting point. [1] https://proceedings.neurips.cc/paper_files/paper/2024/file/820c61a0cd419163ccbd2c33b268816e-Paper-Conference.pdf https://proceedings.neurips.cc/paper_files/paper/2024/file/8...