When LLM search Agents repeatedly scrape web pages but still give wrong answers, the problem often isn't search, but that they refuse to proactively clarify when the user query is ambiguous. Tencent Hunyuan and Tsinghua University jointly release DiscoBench in arXiv:2606.27669, using 211 samples, 463 ambiguity instances, and a "user simulator" to systematically reveal the failure modes of mainstream large models in multi-turn deep-search scenarios. Experiments cover mainstream systems like Gemini-3.1-Pro, Doubao-Seed-2.0-Pro, DeepSeek-V4-Pro, Claude-Opus-4.7. The core conclusion is that proactive clarification is more effective than repeated retrieval, and searching more is even worse than just guessing directly.