论文

研究表明，机器学习在公共部门的成功，将更少依赖模型准确率的突破，而更多依赖机构能否构建出透明、可复现、可问责且受公民信任的数据基础设施。It is shown that the success of machine learning in the public sector will depend less on breakthroughs in model accuracy and more on the ability of institutions to engineer transparent, reproducible, and accountable data infrastructures that citizens can trust.

介绍 Gemini for Google (GfG)，一款面向 Google 内部软件工程生态的 Gemini 专用适配版本，涵盖从构建万亿 token 的专有数据集到采用可缓解灾难性遗忘的中段训练策略的完整过程。G Gemini for Google (GfG)}, an adaptation of Gemini specialized for Google's internal software engineering ecosystem, is introduced, from curating a trillion-token proprietary dataset to implementing a mid-training strategy that mitigates catastrophic forgetting.

本文贡献了一组在架构层面具有重要意义的 MLOps 集成与部署指南（共 25 条），分为五类，并阐述其对整体系统架构的影响。This work contributes a collection of 25 architecturally significant MLOps guidelines for model integration and deployment, organized into five categories, and describes their impact on the overall system architecture.

对八款主流开源 MLOps 框架的实践使用与功能增强需求进行调查，结果显示 MLOps 框架很少被直接开箱即用，也较少集成进 GitHub Workflows，开发者更多通过其 API 在项目中实现自定义功能。Investigating the practical use and desired feature enhancements of eight popular open-source MLOps frameworks indicates that users mainly ask for enhancements to core features of the frameworks, but also better API exposure and CI/CD integration.

对聚焦MLOps工具的学术文献进行系统综述，揭示其功能、范围及其旨在解决的挑战，并突出真实MLOps pipeline中各工具间互操作性的重要性。A systematic review of the academic literature focused on MLOps tools is conducted to reveal their function, scope, and the challenges they are designed to address and highlight the importance of interoperability across MLOps tools in real-world MLOps pipelines.

实验表明，DataEvolver 显著提升了数据质量，相比在原始数据上训练，下游 LLM 性能平均提升 10%，凸显了 LLM 与数据迭代协同演化的新机遇。Experiments show that DataEvolver substantially improves data quality and achieves an average 10\% gain in downstream LLM performance compared with training on original data, highlighting new opportunities for the iterative co-evolution of LLMs and data.

本文提出 MatryoshkaLoRA，一种受 Matryoshka 启发、面向 LoRA 的通用训练框架，通过在已有 LoRA adapter 之间插入一个固定的、经精心设计的对角矩阵来按比例缩放其子秩，从而学习到准确的层次化低秩表示。MatryoshkaLoRA is proposed, a general, Matryoshka-inspired training framework for LoRA that learns accurate hierarchical low-rank representations by inserting a fixed, carefully crafted diagonal matrix between the existing LoRA adapters to scale their sub-ranks accordingly.

这些结果表明，Kueue、DAS 与 GAIE 等互补组件构成了一个高性能的协同平台，证明了 Kubernetes 能够作为承载高要求 GenAI 工作负载的统一底座。These findings illustrate that these complementary components (Kueue, DAS, and GAIE) form a cohesive, high-performance platform, proving Kubernetes' capability to serve as a unified foundation for demanding GenAI workloads.

本文为两部分组成的专题论文，介绍物理信息神经网络——一类在训练求解监督学习任务时遵循由一般非线性偏微分方程所描述的物理定律的网络；并展示如何利用这些网络推断偏微分方程的解，以及获得对所有输入坐标和自由参数完全可微的物理信息代理模型。This two part treatise introduces physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations and demonstrates how these networks can be used to infer solutions topartial differential equations, and obtain physics-informed surrogate models that are fully differentiable with respect to all input coordinates and free parameters.

一个能够将任意自然语言任务轻松映射为人类可读 prompt 形式的系统，并在覆盖多种任务的多任务混合数据上对预训练 encoder-decoder 模型进行微调。A system for easily mapping any natural language tasks into a human-readable prompted form and fine-tune a pretrained encoder-decoder model on this multitask mixture covering a wide variety of tasks.

对现有图神经网络模型进行了详细综述，系统性地归纳了其应用，并提出了四个有待解决的未来研究方向A detailed review over existing graph neural network models is provided, systematically categorize the applications, and four open problems for future research are proposed.

本文提出一种权重裁剪的替代方案：对 critic 相对于其输入的梯度范数施加惩罚。其性能优于标准 WGAN，能以几乎无需调参的方式稳定训练多种 GAN 架构。This work proposes an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input, which performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning.