论文

RTP-LLM 是一个面向工业级 LLM 部署的高性能推理引擎，已在 Alibaba Group 成功部署，服务超过 1 亿用户，通过集成设计解决根本性瓶颈。RTP-LLM is presented, a high-performance inference engine for industrial-scale LLM deployment, successfully deployed across Alibaba Group serving over 100 million users, and addresses fundamental bottlenecks through integrated design.

本文引入一个均值修正项，有效抑制近似误差，即使在极端稀疏度下也能将性能下降保持在可控范围；并使用 PackGQA 内存访问、warp specialization 和 pingpong 流水线重新设计稀疏注意力算子。This paper introduces a mean correction term that effectively suppresses the approximation error, keeping performance degradation manageable even at extreme sparsity levels, and redesigns the sparse attention operator with PackGQA memory access, warp specialization, and pingpong pipelining.

目标是提供无需数周超参搜索即可部署的方案，直接从原始未压缩模型蒸馏 4-bit 学生模型，并以开源权重形式发布为 Hypernova-60B。The aim is a recipe deployable without a multi-week hyper-parameter search, which distills the 4-bit student directly from the original, uncompressed model, and is released open-weight as Hypernova-60B.

推出 DeepSeek-V4.1-Flash 模型，这是一个具有 552B 骨干参数、支持最长一百万 token 上下文的多模态 Mixture-of-Experts 模型，显著提升了 agent 工作负载的成本效率，并突破了 KV cache 压缩的极限。The DeepSeek-V4.1-Flash model, a multimodal Mixture-of-Experts model with 552B backbone parameters and support for contexts of up to one million tokens, is introduced, substantially improving cost efficiency for agentic workloads and pushing the limits of KV cache compression.

政治话语日益呈现更强的敌对感是普遍观感，但稳健证据仍稀缺。本文研究 1997 至 2026 年丹麦议会的归责行为，结合专门构建的分类器 BlameBERT（F1: 0.80）与多层统计建模。该分类器采用面向低至中等资源语言的标注高效流程。结果显示出一条香蕉形轨迹：归责水平约在 2016 年前下降，随后在近年（2019–2026）进入显著且持续的上升阶段。执政地位显著Political discourse is widely perceived to be growing more hostile, yet robust evidence remains scarce. This study examines blame attribution in the Danish Parliament from 1997 to 2026, combining a purpose-built classifier, BlameBERT (F1: 0.80), with multilevel statistical modeling. The classifier is constructed using an annotation-efficient pipeline for blame attribution in low-to-mid resource languages. The results reveal a banana-shaped trajectory, with blame declining until around 2016 before entering a significant and sustained increase in recent years (2019-2026). Government status consi

GigaWorld-Policy-0.5 在保留未来视觉动力学训练收益的同时提升了机器人控制的推理效率，并引入 Mixture-of-Transformers 架构，将视觉动力学建模与动作生成分离到专门的专家模块中。GigaWorld-Policy-0.5 preserves the training benefits of future visual dynamics while improving inference efficiency for robot control, and introduces a Mixture-of-Transformers architecture that separates visual dynamics modeling and action generation into specialized experts.

Meshy T2在几何保真度上达到SOTA，端到端图像到网格生成中位耗时6秒，比自回归基线快一个数量级以上。Meshy T2 achieves state-of-the-art geometric fidelity and completes end-to-end image-to-mesh generation within a median of 6 seconds, over an order of magnitude faster than autoregressive baselines.

提出 MultiMedQA 基准，整合六个现有医学问答数据集（涵盖专业医学、研究与消费者查询）及一个全新的在线医学问题搜索数据集，并提出针对模型答案的人工评估框架，揭示了 LLM 在医学领域的潜在应用价值。MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, is presented and a human evaluation framework for model answers is proposed, suggesting the potential utility of LLMs in medicine.