На видео показано, как фигурант закладывает взрывчатку под автомобиль. После этого снято, как его задерживают правоохранители, а потом приводят на допрос к следователю. На допросе мужчина ответил, что знает, за что его задержали, и свою вину он признает.
Глава пояснил, что недостоверные данные касаются удара ВСУ по автосервису в Сеймском округе. Тогда не удалось спасти 25-летнего сотрудника ремонтного предприятия.
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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.
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ICYMI, Anthropic recently sunsetted Claude Opus 3, the first of its models to be retired since outlining new preservation plans. Part of this process is conducting "retirement interviews" with the outgoing models, allowing them to offer "perspective" on their situation, and Opus 3 apparently used this opportunity to request an outlet for publishing its own essays. Specifically, the model said it wanted to share its own "musings, insights or creative works," because doesn’t everyone these days?