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Evaluating web coding LLMs with real-world fidelity reveals that even state-of-the-art models still struggle with aesthetics and framework-specific nuances.
LLM-generated explanations often fail to help users identify incorrect answers, and simply scaling models or applying post-training doesn't fix the problem.
Ditch the deep thought: this new agentic search framework slashes reasoning steps by 70% while boosting accuracy by prioritizing parallel evidence gathering.
