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.
On Friday afternoon, Donald Trump posted on Truth Social, accusing Anthropic, the AI company behind Claude, of attempting to "STRONG-ARM" the Pentagon and directing federal agencies to "IMMEDIATELY CEASE" use of its products. At issue is Anthropic CEO Dario Amodei's refusal of an updated agreement with the US military agreeing to "any lawful use" of Anthropic's technology, as Defense Secretary Pete Hegseth mandated in a January memo, to the frustration of many tech workers across the industry.
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British Airways’ owner, International Airlines Group, has announced a sharp rise in annual profits to almost £4bn despite a slight fall in passenger numbers in 2025.