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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.

Gaby Hinsliff may be right to link the current lack of starter jobs to recent increases in minimum wage and national insurance costs for employers (Do you remember your first crappy job? Today’s young people would wish for half your luck, 20 February). But there’s more to it.

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Yesterday, Axios reported that Hegseth gave Anthropic a deadline of 5:01 PM on Friday to agree to the Pentagon's terms. At the same time, the DoD requested an assessment of its reliance on Claude, an initial step toward potentially labelling Anthropic as a "supply chain risk" — a designation usually reserved for firms from adversaries like China and "never before applied to an American company," Anthropic wrote.

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Вооруженные силы (ВС) Афганистана нанесли удар по «ядерному объекту» в Пакистане и военной базе в районе Абботтабада в провинции Хайбер-Пахтунхва. Об этом сообщает телеканал Ariana News на своей странице в социальной сети X со ссылкой на местные источники.

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