Daily LLM Papers
iMYorz让AI来聊一聊最新的大模型领域论文/报告。
| Publishes | Twice weekly | Language |  ZH-CN | Category | Technology |
|---|
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Latest Episodes
通义 DeepResearch：开源 AI 智能体的新纪元
阿里巴巴通义实验室在开发自主信息搜索和推理代理模型方面的进展，集中于三个相关的项目：Tongyi DeepResearch、WebDancer 和 WebSailor。这些研究的核心目标是构建能够执行复杂、多步骤任务的大型语言模型（LLMs），方法包括生成高质量、高不确定性的训练数据，例如通过构建知识图谱和模糊信息来合成问题（SailorFog-QA, CRAWLQA），以及使用如 ReAct 框架和强化学习（RL）等后训练方法。特别... more
Scaling Synthetic Data Creation with 1,000,000,000 Personas
Persona Hub 是一个包含 10亿个多样化角色（persona） 的集合，这些角色是从海量网络数据中自动整理出来的。这些角色约占世界总人口的13%。Persona Hub中的每个角色都被视为世界知识的分布式载体，与独特的知识、经验、兴趣、个性和职业相关联。从压缩的角度来看，Persona Hub（约10^10个token）可以被看作是将用于训练大型语言模型（LLM）的... more
REFRAG (REpresentation For RAG) 和 RASD (Retrieval-Augmented Speculative Decoding) 都是旨在提高大型语言模型 (LLM) 推理效率的方法，但它们关注的方面和实现机制有所不同：
• 核心目标和解决的问题：REFRAG：主要目标是解决RAG应用中长上下文输入带来的显著系统延迟和对键值 (KV) 缓存的大量内存需求，从而提高吞吐量并解决知识丰富与系统效率之间的基本权衡问题。它特别关注首个token生成时间 (TTFT... more
LongCat-Flash-Chat
LongCat-Flash 模型在架构设计、训练策略和推理部署方面引入了多项创新技术，使其在计算效率和智能体能力方面均表现出色。
模型架构创新和技术要点
LongCat-Flash 采用了一种新颖的 Mixture-of-Experts (MoE) 架构，其核心创新包括：
• 零计算专家 (Zero-computation Experts)：LongCat-Flash 引入了零计算专家机制，能够根据上下文需求动态分配计算预算。这意味着模型可以为重... more
本期论文：INTERN-S1: A SCIENTIFIC MULTIMODAL FOUNDATION MODEL
Intern-S1 在多个方面展现了显著的创新，这些创新主要体现在其模型架构、数据策略以及训练系统和算法优化上，旨在弥合开放源代码与闭源模型在科学理解和推理能力上的差距，并向通用人工智能（AGI）迈进。
以下是 Intern-S1 的主要创新点：
• 专业通用型多模态基础模型定位：Intern-S1 被设计为一个能够分析多种科学模态数据（如分子结构、时间序列信号等）的专业通... more
原文：A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models
该综述文章深入探讨了并行文本生成领域，旨在解决大型语言模型（LLMs）中固有的自回归（AR）生成速度瓶颈。文章系统地将现有技术分为基于自回归（AR-based）和非自回归（Non-AR-based）范式。基于自回归的方法通过草稿-验证、分解-填充和多令牌预测等策略加速生成，同时努力保持输出质量。非自回归方法则包... more
作者： Chengshuai Zhao、Zhen Tan、Pingchuan Ma、Dawei Li、Bohan Jiang、Yancheng Wang、Yingzhen Yang 和 Huan Liu (亚利桑那州立大学) 来源： arxiv.org
摘要
这篇研究论文《大语言模型链式思维推理是假象吗？一个数据分布视角》对大语言模型（LLM）中链式思维（CoT）推理的真实性提出了质疑。尽管CoT提示在提高LLM在各种任务中的性能方面表现出色，并常被认为是LLM进行类人推理过程的证据，但本文... more
Seed Diffusion Preview 技术报告
Seed Diffusion模型将扩散（Diffusion）方法应用于代码生成，并实现了显著的高速推理，其核心在于将连续域的扩散概念巧妙地适配到离散的文本数据上，并通过一系列优化策略来加速其迭代生成过程。
以下是其主要实现方式：
• 离散状态扩散（Discrete-state Diffusion）：挑战：传统的Diffusion模型天然适用于图像像素或音频频谱图等连续的高维数据，因为在这些数据上定义和操作高斯噪声是一个自然的过程。... more
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