这篇论文很有意思，告诉你Claude Fable 5在医学难题上不是不会答，而是经常拒绝回答。一旦它愿意回答，准确率反而是最高的。如果你想了解模型的安全边界和真实能力，值得看看。
这篇论文评估了Anthropic最先进的模型Claude Fable 5在8个生物医学基准（4个文本+4个多模态）上的表现，并与Claude前辈和GPT-5进行了对比。研究发现Fable 5的拒绝率在8.0%到99.4%之间，具体取决于基准（如MedQA、MedXpertQA MM、RareBench），这一模式在先前模型和GPT-5中均不存在。一旦排除被拒绝的问题，Fable 5在所有基准上的准确率都超过或持平其他模型。论文识别出两种不同的拒绝模式：一种集中在基础科学和机制内容，另一种在RareBench上针对先天代谢疾病几乎全部拒绝，而成人自身免疫疾病则没有。
Capabilities of Claude Fable 5 on Biomedical Challenge Problems
Frontier language models are increasingly evaluated on biomedical benchmarks, but two problems undermine most published evaluations: legacy benchmarks are near-saturated, and open-ended responses are graded by other language models. We evaluate Claude Fable 5, Anthropic's most capable publicly available model, across eight biomedical benchmarks, four text and four multimodal, using deterministic scoring against fixed answer keys throughout. We include two Claude predecessors and GPT-5 as baselines. Refusal is tracked as a distinct outcome in every result table. That decision produces the paper's central finding. Fable 5 refuses between 8.0% and 99.4% of questions depending on the benchmark, a pattern absent in both predecessors and in GPT-5. Once refused items are excluded from the denominator, Fable 5's accuracy exceeds or meets every other model on every benchmark in this study. We identify two distinguishable refusal patterns: one concentrating in basic-science and mechanism content across MedQA and MedXpertQA MM, confirmed independently on two benchmarks using each benchmark's own category labels; and a separate disease-domain pattern on RareBench, where inborn metabolic disease presentations are refused near-universally while adult-onset autoimmune presentations are not. The primary constraint on Fable 5's biomedical usefulness is willingness to engage, not capability once it does.