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Turns out, nobody's explicitly RL-training LLM agents when to *stop* in multi-agent systems, despite its critical role in efficiency and cost.
Evaluating web coding LLMs with real-world fidelity reveals that even state-of-the-art models still struggle with aesthetics and framework-specific nuances.
