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Alibaba's Qwen3 is a new-generation large language model featuring a hybrid reasoning mode (thinking vs. non-thinking), support for 119 languages, and optimized agent capabilities. It includes specialized models like Qwen3-Embedding and Qwen3-Reranker for text representation and retrieval tasks, all open-sourced under Apache 2.0 for commercial use. 阿里巴巴推出的新一代大型语言模型Qwen3，具备混合推理模式（思考与非思考）、支持119种语言、优化Agent能力，并包含Qwen3-Embedding和Qwen3-Reranker等专用模型，全部采用Apache 2.0协议开源，可免费商用。

The Qwen3 series, released by Alibaba Cloud's Tongyi Qianwen team, features eight models ranging from 0.6B to 235B parameters, utilizing both MoE (Mixture of Experts) and Dense architectures. It supports 128K token context length and 119 languages, with innovative thinking/non-thinking modes for optimized task performance. The series balances high performance in coding, mathematics, and general tasks with efficient inference, making it suitable for diverse applications from edge devices to enterprise solutions. (阿里云通义千问团队发布的Qwen3系列包含八款模型，参数规模从0.6B到235B，采用MoE和密集架构。支持128K token上下文长度和119种语言，首创思考/非思考模式优化任务性能。该系列在编码、数学和通用任务上表现卓越，同时实现高效推理，适用于从边缘设备到企业级应用的多种场景。)

Qwen3 introduces a unified framework integrating thinking and non-thinking modes for dynamic reasoning, with multilingual support expanded to 119 languages and state-of-the-art performance across benchmarks. (Qwen3通过集成思维模式和非思维模式的统一框架实现动态推理，将多语言支持扩展至119种语言，并在多个基准测试中达到最先进性能。)

This content provides a comprehensive guide on writing academic resumes for research project applications, covering three main components: chronological academic history, relevant academic affiliations, and a structured presentation of academic accumulation and contributions. It emphasizes neutral, factual presentation, chronological organization, and the importance of summarizing contributions rather than simply listing achievements. The guide includes practical examples from National Social Science Fund applications to illustrate best practices. (本文提供了撰写研究项目申请学术简历的完整指南，涵盖三个主要部分：按时间顺序的学术经历、相关学术兼职、以及学术积累与贡献的结构化呈现。强调中性、事实性的表述，按时间顺序组织内容，以及总结贡献而非简单罗列成果的重要性。指南包含国家社科基金申请的实际案例以说明最佳实践。)

English Summary. This article provides a comprehensive overview of quality assessment standards for LLM-generated content in academic papers, focusing on technical evaluation criteria, ethical considerations, and practical implementation guidelines for researchers and editors. (中文摘要翻译：本文全面概述了学术论文中LLM生成内容的质量评估标准，重点关注技术评估标准、伦理考量以及研究人员和编辑的实际实施指南。)

A joint study reveals that poisoning large language models requires only a fixed number of malicious documents (as few as 250), regardless of model size or training data volume, challenging previous assumptions about attack feasibility. (一项联合研究表明，无论模型规模或训练数据量如何，仅需固定数量的恶意文档（少至250份）即可对大语言模型进行数据投毒攻击，这挑战了先前关于攻击可行性的假设。)

This article critiques the 'inevitabilist' framing of AI and LLMs as an unavoidable future, arguing instead for conscious choice in shaping technology's role. It warns against letting powerful narratives from tech leaders dictate our response, urging readers to define and fight for the future they want. (本文批判了将AI和LLM视为不可避免未来的'必然主义'框架，主张在塑造技术角色时进行有意识的选择。它警告不要让科技领袖的强大叙事决定我们的反应，敦促读者定义并争取他们想要的未来。)

English Summary: This article explains GPTZero's technical approach to detecting AI-generated text, focusing on its use of perplexity and burstiness metrics to distinguish human writing from AI output, while also comparing it with other detection tools like OpenAI's classifier and Originality.AI. (中文摘要翻译：本文详细解析了GPTZero检测AI生成文本的技术原理，重点介绍了其利用困惑度和突发性指标来区分人类写作与AI输出的方法，同时对比了OpenAI分类器、Originality.AI等其他检测工具。)

Blockchain technology, with its decentralized, immutable, and transparent nature, offers a promising solution to enhance data security, trust, and efficiency across various industries, though it faces challenges like scalability and regulatory compliance. (区块链技术凭借其去中心化、不可篡改和透明的特性，为提升各行业数据安全、信任和效率提供了有前景的解决方案，但也面临可扩展性和监管合规等挑战。)