When most benchmarks are still testing LLMs with high-school / Olympiad-level math, AdvancedMathBench, released by Intern Large Models (Shanghai AI Lab), pulls the difficulty straight up to "upper-year undergraduate (UGD) + doctoral qualifying exam (QE)" level — the core ProverBench collects 296 such hard problems, and the companion VerifierBench uses 888 model-generated proof trajectories to test "verification ability". The experimental results are rather harsh. On proof generation, even the currently strongest GPT-5.5-xhigh only scores 75.8 (UGD) and 66.1 (QE), meaning that even the top models are completely stumped by a quarter to a third of PhD-level math proofs. On proof verification, the strongest model's Balanced F1 is only 65.1, and True Negative is generally low across all models — models are far from being good at catching "wrong proofs" and easily let wrong ones through as right. The biggest contribution of this benchmark is not "yet another proof that LLMs can't do hard problems", but rather refining the evaluation granularity from "is the final answer right" to "is the proof process valid", using a verifier pipeline + expert annotation for fine-grained error localization. This is critical for agentic workflows (letting LLMs peer-review and edit each other) — if the verifier can't catch the holes, the credibility of the entire agent chain is empty talk. Compared with OpenAI's admission during the SWE-Bench Pro re-review that nearly 30% of problems are "bad problems", AdvancedMathBench chose a harder direction: the problems may be cleaner, but the judging standard is harsher. This may hint that LLM evaluation is moving from "climbing leaderboards" to the next stage of "process auditing".