Chubby♨️@kimmonismus · X48%的得分背后是系统设计对模型能力的碾压，失败模式「reviewer-pleasing bias」和死亡螺旋比分数更有价值，提醒我们架构创新才是落地的真杠杆。
DeepMind的AI co-mathematician在FrontierMath Tier 4研究级数学问题得分48%，而基础模型Gemini 3.1 Pro仅19%。提升源于多代理架构的智能编排，包括并行代理相互审查证明、编写代码和搜索文献，而非模型本身更智能。评估绕过标准框架，使用48小时每问题、无令牌限制的自有基础设施，因此得分不能直接与其他模型比较。案例中，数学家Marc Lackenby与AI合作解决Kourovka Notebook开放问题，AI提供证明策略，审查代理发现缺陷，人类专家填补空白，展示了高效人机协作。系统存在“reviewer-pleasing bias”和“death spirals”等失败模式。对于Erdős型猜想或千年问题，AI仍缺乏创造性直觉，但能压缩从想法到验证的时间，加速文献搜索和计算验证。论文强调范式转变：系统设计以对实际研究重要的方式复合模型能力，推动数学向数学家与AI代理协作的未来发展。
DeepMind's AI co-mathematician scored 48% on FrontierMath Tier 4-research-level math problems that professional mathematicians need weeks to solve.
The base model （Gemini 3.1 Pro） scores 19% alone. The entire jump comes from agentic scaffolding， parallel agents reviewing each other's proofs， writing code， searching literature. Not a smarter model， but smarter orchestration.
Important context the paper openly provides： they bypassed the standard evaluation harness. 48 hours per problem， no token limits， their own infrastructure （page 14）. So the 48% isn't directly comparable to other models on the leaderboard.
What's more interesting than the score is the case study： Marc Lackenby used the system to solve an open problem from the Kourovka Notebook. The AI found a proof strategy， its own reviewer agent identified a flaw， and Lackenby， as a domain expert， filled the gap. Neither could have done it alone at that speed.
The paper also names concrete failure modes： "reviewer-pleasing bias" （agents rewrite flawed arguments until the AI reviewer can no longer detect the error. And "death spirals"） infinite review loops that degrade into hallucinated reasoning.
For Erdős-type conjectures or millennium problems， these systems still can't generate the creative intuition that opens a proof path. What they compress： the time between having an idea and knowing whether it works. Literature search， counterexample hunting， computational verification， the exploratory grind.
The takeaway from this paper is less about the benchmark and more about a paradigm shift： system design now compounds model capability in ways that matter for actual research. Thats why its a really intersting paper.