I used z3 theorem prover to assess LLM output, which is a pretty decent SAT solver. I considered the LLM output successful if it determines the formula is SAT or UNSAT correctly, and for SAT case it needs to provide a valid assignment. Testing the assignment is easy, given an assignment you can add a single variable clause to the formula. If the resulting formula is still SAT, that means the assignment is valid otherwise it means that the assignment contradicts with the formula, and it is invalid.
Последние новости。关于这个话题，一键获取谷歌浏览器下载提供了深入分析
# early profiling data，详情可参考搜狗输入法2026
Concept art for the project.
I then added a few more personal preferences and suggested tools from my previous failures working with agents in Python: use uv and .venv instead of the base Python installation, use polars instead of pandas for data manipulation, only store secrets/API keys/passwords in .env while ensuring .env is in .gitignore, etc. Most of these constraints don’t tell the agent what to do, but how to do it. In general, adding a rule to my AGENTS.md whenever I encounter a fundamental behavior I don’t like has been very effective. For example, agents love using unnecessary emoji which I hate, so I added a rule: