生态环境部党组提出，认真落实学习研讨、查摆问题、整改整治、建章立制、开门教育等工作安排，教育引导部系统各级党组织和全体党员干部坚持实事求是、求真务实，坚决有力贯彻落实党中央重大决策部署，为人民出政绩、以实干出政绩，为推动美丽中国建设取得新的重大进展提供有力保障。
Testing LLM reasoning abilities with SAT is not an original idea; there is a recent research that did a thorough testing with models such as GPT-4o and found that for hard enough problems, every model degrades to random guessing. But I couldn't find any research that used newer models like I used. It would be nice to see a more thorough testing done again with newer models.
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A Foreword on AGENTS.md#One aspect of agents I hadn’t researched but knew was necessary to getting good results from agents was the concept of the AGENTS.md file: a file which can control specific behaviors of the agents such as code formatting. If the file is present in the project root, the agent will automatically read the file and in theory obey all the rules within. This is analogous to system prompts for normal LLM calls and if you’ve been following my writing, I have an unhealthy addiction to highly nuanced system prompts with additional shenanigans such as ALL CAPS for increased adherence to more important rules (yes, that’s still effective). I could not find a good starting point for a Python-oriented AGENTS.md I liked, so I asked Opus 4.5 to make one:
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