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Researchers from Los Alamos National Laboratory and NVIDIA present an expert-curated benchmark of 250 questions on inertial confinement fusion to evaluate LLMs' scientific fluency, physical reasoning, and quantitative analysis. They compare frontier open/closed models with Prospero, a domain-specialized model, finding that specialization improves scores but critical physics errors persist, and recommend assessing existing capabilities and retrieval benefits before investing in further specialization.
提出了 Differentiable Diffusion Conditioning (D2C)，通过使冻结条件扩散模型的确定性反向去噪链对条件信号可微，将其转化为基于梯度的端到端优化器，用于离线模型优化，在激光脉冲优化、数据中心调度及 Design-Bench 任务上取得最佳结果。