Source: Computational Materials Science, Volume 266
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，这一点在heLLoword翻译官方下载中也有详细论述
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.，这一点在服务器推荐中也有详细论述
至于基于 NPU 和桌面平台的 LiteRT-LM 运行时，相关工作已经在进行中。一旦 Google 开放 iOS 的公共 API（预计在 2026 年初），我们将添加全面支持。桌面平台也在我们的计划之中——敬请期待，即将推出。