英國超市將巧克力鎖進防盜盒阻止「訂單式」偷竊
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.
，详情可参考safew官方版本下载
为了获得最佳的响应速度和稳定性，特别是在国内网络环境下，我们需要对 Claude Code 进行本地化配置，并接入国内高性能的大模型 API（如智谱 AI 的 GLM-4）。
第七十一条 当事人提出证据证明裁决有下列情形之一的，可以向仲裁机构所在地的中级人民法院申请撤销裁决：