Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.
I’ve been planning for some time to send a server to a datacenter to be free to announce my own IPs via BGP. The choice of OS running on this server is important, and I think that with Bootc + OSTree, I have a solution that suits me perfectly (because if I ever lock up the machine during an update, a simple reboot will restore it to a consistent state).
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"In China, the people who make motors are getting together with the people who make hand hardware and basically creating bespoke motors that can fit within joints and fingers. It's probably going to work as an effective hand," he says.
Флорида Пантерз