It seems to be doing more than that.
Like other researchers, I’m using the ChatGPT API (model gpt-4o) as the LLM because it offers strong reasoning, stable structured outputs, and affordable per-call pricing. I’m also most familiar with PHP, so I’m using it as the interface layer that connects the LLM to the game. The last missing piece was enabling two-way communication between PHP and the emulator:
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俄罗斯度假胜地副长官实施夜间宵禁 20:48
Benchmarks are structured as standardized tasks. Each assignment resides under tasks/my-task/ and contains task.toml for configuration details like time limits, instruction.md representing the agent's directive, a tests/ folder with test.sh initialization that records results to /logs/reward.txt, and test.py for validation using either predefined checks or AI-based assessment. An environment/Dockerfile specifies the operational container, while a files/ directory contains reference materials integrated into the container. Evaluations record performance metrics between 0.0 and 1.0 to assessment logs. The supervisory AI continuously improves this metric.