论文

提出首个 MuseCP 评估框架，涵盖四类音乐 facet，使用细粒度且量身定制的指标来捕捉音乐属性的细微变化，并希望为开发更有效、更可靠、具备强大 MuseCP 能力的音乐编辑策略提供实践指导。The first MuseCP evaluation framework is introduced that covers four categories of music facets with fine-grained and well-tailored metrics to capture nuanced changes in music attributes and hopes it can offer practical guidance for developing more effective and reliable music editing strategies with strong MuseCP capability.

现代 LLM Agent 的改进通常依赖于人工修改 prompt、工具或工作流，而围绕模型的可执行支架——harness——在部署后一般被视为固定不变的产物。本文研究一种替代方案：harness 是任务特定的且持续可进化的，每个任务族维护各自的 harness，通过固定的任务注入接缝在不同迭代间热替换，并依据环境反馈进行改写。我们提出分层自改进（HSI），在该框架中，一个冻结的 LLM M 在三层Modern LLM agents are often improved by modifying prompts, tools, or workflows manually, while the executable scaffold surrounding the model---the harness---is typically treated as a fixed artifact after deployment. This work studies an alternative where the harness is task-specific and continuously evolvable: each task family maintains its own harness, which is hot-swapped across iterations through a fixed task-injection seam and rewritten using environment feedback. We introduce Hierarchical Self-Improvement (HSI), a framework in which a single frozen LLM M operates across three hierarchical

应在真实类别分布下使用跨网络评估来判断部署就绪度，而非仅依赖域内准确率。Deployment readiness should be assessed using cross-network evaluation under realistic class distributions, rather than within-domain accuracy alone, to suggest deployment readiness should be assessed using cross-network evaluation under realistic class distributions, rather than within-domain accuracy alone.

分析表明，借助 LLM，美国约 15% 的工作任务可在保持同等质量的前提下显著提速完成，意味着 LLM 驱动的软件将对底层模型经济影响的规模化产生实质性作用。The analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality, implying that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models.

结果表明，通过结合基础 LLM 改进（PaLM 2）、医学领域微调以及包括新颖集成精化方法在内的提示策略，医学问答正快速接近医生水平的表现。Results highlight rapid progress towards physician-level performance in medical question answering by leveraging a combination of base LLM improvements (PaLM 2), medical domain finetuning, and prompting strategies including a novel ensemble refinement approach.

本文提出一种结构化协议，通过对开源 LLM 评估与安全工具的分类驱动分析来自动化 AI 风险缓解，并给出一个可同时适用于开源与商用方案的分类驱动框架。This paper proposes a structured protocol to automate AI risk mitigation through a taxonomy-driven analysis of open-source LLM evaluation and security tools, and presents a taxonomy-driven framework applicable to open-source and proprietary solutions.

本文给出了 LLM routing 的统一形式化，将其刻画为由五个组件构成的序贯决策过程：context 编码器、模型编码器、评分函数、决策规则和学习信号，涵盖单轮、多轮和个性化 routing。This work presents a unified formulation of LLM routing as a sequential decision process characterized by five components: context encoders, model encoders, scoring functions, decision rules, and learning signals, covering single-turn, multi-turn, and personalized routing.