斯坦福和耶鲁的研究者发现，Claude 3.7 Sonnet 在特定条件下会以 95.8% 的准确率「近乎逐字逐句」地输出《哈利波特》等受版权保护的作品——这不仅与 Anthropic 长期以来关于「模型只是学习了语言规律」的说法背道而驰，更让该公司对任何人的「蒸馏」指控显得缺乏底气。
18:57, 27 февраля 2026Культура，更多细节参见服务器推荐
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As a data scientist, I’ve been frustrated that there haven’t been any impactful new Python data science tools released in the past few years other than polars. Unsurprisingly, research into AI and LLMs has subsumed traditional DS research, where developments such as text embeddings have had extremely valuable gains for typical data science natural language processing tasks. The traditional machine learning algorithms are still valuable, but no one has invented Gradient Boosted Decision Trees 2: Electric Boogaloo. Additionally, as a data scientist in San Francisco I am legally required to use a MacBook, but there haven’t been data science utilities that actually use the GPU in an Apple Silicon MacBook as they don’t support its Metal API; data science tooling is exclusively in CUDA for NVIDIA GPUs. What if agents could now port these algorithms to a) run on Rust with Python bindings for its speed benefits and b) run on GPUs without complex dependencies?
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