My naive thoughts on Formal Verification (FV) and LLM:
We could leverage existing FV tools for a given programming language by using an LLM to generate a translator that maps the language to the FV tool's input format. This would essentially require a finite number of "abstract interpretation" functions—one for each language construct. While the total number of constructs might be large (e.g., around 500), each function would be independent. A function would only need to reason about the abstract semantics of a single construct, assuming the others adhere to their respective semantics. We could then distribute these LLM-generated functions among a group of experts (e.g., 100 reviewers). Thanks to the modularity of the functions, reviewers could evaluate their assigned subset in parallel without bottlenecks. The end result would be a working FV tool for the target language.
That's a good post but we don't even need this post to tell of her incredible power of smashing great things. I wish to see her in the next Shehulk movie.
Obviously, openAI is a cheaper and better option for Apple to use AI in their devices for the time being. Once they have their own AI, or the AI hype subsides, they'll kick openAI.
Worth to mention that Changzhou is a city on the south side of Yangtze River, where no heat provided to public, meaning that cities on the north side could be way worse due to coal burning for heat supply.
We could leverage existing FV tools for a given programming language by using an LLM to generate a translator that maps the language to the FV tool's input format. This would essentially require a finite number of "abstract interpretation" functions—one for each language construct. While the total number of constructs might be large (e.g., around 500), each function would be independent. A function would only need to reason about the abstract semantics of a single construct, assuming the others adhere to their respective semantics. We could then distribute these LLM-generated functions among a group of experts (e.g., 100 reviewers). Thanks to the modularity of the functions, reviewers could evaluate their assigned subset in parallel without bottlenecks. The end result would be a working FV tool for the target language.