[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-hzwer--WritingAIPaper":3,"tool-hzwer--WritingAIPaper":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",150037,2,"2026-04-10T23:33:47",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":72,"owner_avatar_url":73,"owner_bio":74,"owner_company":75,"owner_location":74,"owner_email":74,"owner_twitter":72,"owner_website":76,"owner_url":77,"languages":74,"stars":78,"forks":79,"last_commit_at":80,"license":74,"difficulty_score":81,"env_os":82,"env_gpu":83,"env_ram":83,"env_deps":84,"category_tags":87,"github_topics":88,"view_count":32,"oss_zip_url":74,"oss_zip_packed_at":74,"status":17,"created_at":92,"updated_at":93,"faqs":94,"releases":95},6516,"hzwer\u002FWritingAIPaper","WritingAIPaper","Writing AI Conference Papers: A Handbook for Beginners","WritingAIPaper 是一本专为人工智能领域初学者打造的会议论文写作指南。它旨在解决科研新手在面临截稿压力时，因缺乏经验而不知如何下笔、难以提炼核心贡献或容易陷入常见写作误区的痛点。无论是刚完成实验却对着空白文档发愁的研究生，还是希望规范学术表达的研究人员，都能从中获得实用帮助。\n\n该指南将复杂的论文创作过程拆解为“从零构建”与“细节打磨”两大板块。在构建阶段，它指导用户如何从实验结果中提炼出“洞察”、“性能提升”或“新能力”这三类核心贡献，并搭建清晰的论文框架；在细节阶段，则聚焦于提升文章的可读性与逻辑流畅度。其独特亮点在于不仅提供了具体的写作策略，还强调了同行评审的重要性，鼓励社区共同参与完善这份手册。通过结合真实场景的引导与结构化的建议，WritingAIPaper 帮助初学者跨越学术写作的门槛，更自信地分享研究成果。","# Writing AI Conference Papers: A Handbook for Beginners\n\n**We believe that what this article needs the most is peer review, and we warmly welcome valuable suggestions in any form.**\n\nAuthor: [hzwer](https:\u002F\u002Fgithub.com\u002Fhzwer), [DingXiaoH](https:\u002F\u002Fgithub.com\u002FDingXiaoH)\n\n知乎 [1](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F593195527)-[2](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F639732057)-[3](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F627032371)｜[跃问中翻](https:\u002F\u002Fyuewen.cn\u002Fshare\u002F145749938443137024?utm_source=share&utm_content=web_linkcopy&version=2) | [豆包总结](https:\u002F\u002Fwww.doubao.com\u002Fthread\u002Fw750d882cf0af6419) | [公众号](https:\u002F\u002Fmp.weixin.qq.com\u002Fs\u002FMjeBZDV6xapuA_L6ODpVcA)\n\n**Abstract.** *Crafting a research manuscript can pose significant challenges for novices, particularly when time is scarce before the deadline and the authors lack experience in academic submissions. An ill-prepared manuscript can be a source of distress for both collaborators and readers, frequently leading to rejection or necessitating substantial revisions. In this article, we'll share some tips for beginners who want to write AI conference papers. Our goal is for this article to be a guide for beginners, making it easier to share academic achievements.*\n\n## Introduction\n\n> **Background.** The GPU cluster has been running for half a year, and you feel that the results are already significant. You realize that the deadline for an upcoming conference is less than a month away, yet you have only written some course assignment reports. How far in advance should the first draft be completed to avoid missing the deadline? What distinguishes a good research paper from a bad one? What should be done before starting to write? These questions plague you like a nightmare, leaving you staring at the blank Overleaf homepage. Luckily, this article is written for you. \n\nIn this article, we will discuss the aspects related to writing conference papers, with a focus on common pitfalls, catering to novices. Our article mainly consists of two parts: **[completing a paper](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper?tab=readme-ov-file#build-a-paper-from-scratch)** and **[refining its details](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper?tab=readme-ov-file#readability-improvement)**. We aim to provide practical guidance that will enable novices to navigate the complexities of academic writing and contribute to the field with clarity and confidence. Sincerely, we recommend the [Resource List of Writing Tips](https:\u002F\u002Fvision.sjtu.edu.cn\u002Fwriting.html) curated by Chao Ma.\n\n## Build a Paper from Scratch\n\nThis section outlines writing an AI paper from scratch, covering core idea, whole framework, introduction and related work.\n\n### Find the Core Idea\nYou may have interesting findings and experimental results, but you're unsure how to define the core theme. *The key contribution of most published papers falls into exactly one out of the following three categories (from [Nowozion](https:\u002F\u002Fwww.nowozin.net\u002Fsebastian\u002Fblog\u002Ften-tips-for-writing-cs-papers-part-1.html)):*\n\n*Insight: you have an explanation for something that is already there.*\n\n*Performance: you can do something better.*\n\n*Capability: you can do something that could not be done before.*\n\nIdentify the core advantages of your work and emphasize them early in the paper. This way, readers can read the remaining parts with expectations. You can further expand the overall novelty from other aspects as well. **Novelty seems to be elusive, what is it?** Key research topics, efficient solutions, and innovative technical contributions are the primary elements that contribute to a paper's novelty. For instance, numerous early influential works of deep learning emerged from foundational model research due to their potential to impact the entire field. The RAFT\u002FNeRF methods have attracted a large number of researchers due to their outstanding performance, and they involve a lot of engineering handling beyond their core ideas. Techniques such as \"Batch Normalization\" and \"Residual Learning\" are esteemed for their effectiveness. By emphasizing the novelty of your work, you'll be able to discern which aspects are worth the effort and which are inconsequential details.\n\n> A little squiggle of paint by Picasso can be as beautiful as an intricate painting by Rembrandt. —— [Novelty in Science](https:\u002F\u002Fmedium.com\u002F@black_51980\u002Fnovelty-in-science-8f1fd1a0a143) (highly recommended for readers)\n\n***Take-away: Clearly understand the increment over previous methods and find one or two core ideas.***\n\nWhen readers read papers, they seek novel insights. A good paper should have strong points that are easy to remember. You should refine your central ideas until you're confident that people will be eager to learn about them and share them widely. It is particularly worth noting that some ideas may be great, but if they lack originality, it may not be advisable to describe them in detail in the paper. When writing a paper, the focus should be on providing novel, unique, and valuable insights to attract readers' attention and inspire their interest.\n\nDon't underestimate the novelty of your own work. Delve deep to uncover the underlying principles. If the ResNet paper were rewritten as: \"We designed a model using a large number of $3\\times3$ convolutions (inspired by VGGNet) and parallel shortcuts (simplified from GoogleNet) **based on** the former two\", then it will also become a paper without novelty. The story told by the ResNet paper is to propose a problem, abstract the underlying principles, propose its own solutions and specific implementations, and verify them experimentally. This might not totally reflect [their research process](https:\u002F\u002Fwww.zhihu.com\u002Fquestion\u002F406913672\u002Fanswer\u002F1339549216), but it effectively showcases their discoveries.\n\n***Take-away: Discovering new phenomena and sharing new ideas matter more than performance gains.***\n\nA good number of excellent papers often show strong results in experiments. This can make people think that good results are all that matters in a paper. But actually, experimental results are just proof of new discoveries. Small improvements in results don't always mean new knowledge. When you write a paper, think first about what new things readers can learn from your work—not just about showing off better results than others.\n\nIn addition, as Kaiming He points out, researchers should focus on the future rather than solely on past \"state-of-the-art.\" By applying Occam's Razor—seeking simple yet effective solutions—and validating research in real scenarios while forecasting experimental outcomes and future needs, researchers can reduce \"overfitting\" in their studies. Rather than meticulously refining experimental designs and metrics solely for immediate validation, it's more valuable to consider the long-term correctness and relevance of the work during the paper-writing process. The enduring, correct discoveries sustain their influence over time. By stripping away convoluted techniques used merely for paper publication and identifying straightforward, effective solutions, researchers increase the likelihood of their work generalizing to future scenarios.\n\n### Construct the Framework\n***Take-away: Abstract - Introduction - Main body, which are gradually unfolded. Each part is self-complete.***\n\nThe typical structure of a paper includes 1. an abstract, 2. an introduction, and 3. the main body, which encompasses sections such as related work, methodology, experiments, discussion, conclusion, and references. We can break down this structure into three levels. At each level, you should aim to convey a comprehensive research narrative. Each level serves as an expansion of the preceding one. With this understanding, let's explore how to effectively present a research story. For those who are starting out, it's advisable to focus on completing the main body of the paper first.\n\n***Take-away: Consider the target audience, introduce the valuable findings rather than the tortuous research process.***\n\nWhile adhering to the core ideas, start outlining the content you intend to present in your paper. Begin by creating a simple slide to demonstrate your research approach and achievements to your peers, colleagues, or mentors, in order to assess their understanding. It may be beneficial to intentionally seek feedback from researchers unfamiliar with your work to identify potential gaps in comprehension. Unlike the experimental process, it is advisable to emphasize valuable novelties and avoid presenting incomplete or complex aspects of your research. Researchers all understand the pain of research, but the bitter portrayal is only suitable for the postscript of the project. Continuously review and refine your presentation from the reader's perspective until it is easily understandable.\n\nIt may also be necessary to supplement your work with additional experiments if you feel that your experimental rationale lacks rigor. At the same time, it is advisable to conduct thorough literature research, ideally identifying several papers with highly relevant topics. Consider these as potential competitors to your paper and examine them for areas of improvement. Reflect on which aspects would captivate the community and accentuate them, while minimizing the inclusion of clichéd content. \n\n***Take-away: Around the contribution statements, conduct a solid analysis in the result section.***\n\nMany readers will initially assess the method's effectiveness by examining the results before deciding to read the entire paper. They will look to see if your contribution aligns with the experimental findings. Even with strong confidence in your method's efficacy, you will likely need additional comparative and ablation experiments. It's important to create more tables and visuals, selecting the most significant aspects to present. Honesty and objectivity are crucial; overstating claims is particularly undesirable. If concerned about overclaiming, discussing with peers is advisable.\n\n### Write an Introduction\nWith the above materials, you can start trying to write the introduction. Regarding the structure of the introduction, we directly quote from the textbook (from [Elena](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC3178846\u002F)):\n\n**Move 1. Establish a research territory**\n\na. Show that the general research area is important, central, interesting, and problematic in some way;\n\n**Move 2. Find a niche**\n\na. Indicate a gap in the previous research, or extend previous knowledge in some way.\n\n**Move 3. Occupy the niche**\n\na. Outline purposes or state the nature of the present research;\n\nb. List research questions or hypotheses;\n\nc. Announce principle findings;\n\nd. State the value of the present research;\n\ne. Indicate the structure of the research paper.\n\n**Additional suggestions:**\n\na. Knuth: *Keep the reader upper-most in your mind;*\n\nb. Cut to the chase and don't write too much irrelevant to the paper's topic. The novel and interesting aspects of the paper should appear as early as possible;\n\nc. Dedicate more space to describing original and novel ideas;\n\nd. Respect the work of predecessors, and affirm historical contributions before pointing out shortcomings;\n\ne. Consider using a \"page one figure\" to highlight the most important aspects of the paper and catch the reader's attention.\n\nAs we mentioned earlier, the main body is actually an extended version of the introduction. Typically, supplementing the introduction with additional experimental details forms the main body of the paper.\n\n### Describe the Related Work\n***Take-away: The mediocre approach is to describe the correct history, while the better approach is to focus on how different methods relate to what you do.***\n\nThe part may not be included in the context of the introduction. A paper typically has a separate section dedicated to discussing related work, including some background work and competing work. Find three or four topics that are most relevant to your paper, and outline the historical evolution under each topic. Do not list all the negative aspects of other techniques, but rather explain how you improve upon them. You can first write a literature review that is independent of your work. When classifying and ordering the previous methods (for example, refer to a certain method as a pioneer), it is important to pay attention to the correctness of discussion. If you are not confident enough, check the \"related work\" sections of the papers you referenced. Finally, rewrite this section from a more appropriate angle to reflect the unique aspects of your paper. \n\n## Readability Improvement\n> \"Writing endures through the ages, its merits and faults known only to the author's heart. (文章千古事，得失寸心知）\" —— [Du Fu](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FDu_Fu)\n\n> \"The best writing instruction ever: Good writing is bad writing rewritten. I got it from Stephen King. Where Stephen King got it, I don't know. I'm giving it to you. You tell your students.\" —— [Robert Weiss](https:\u002F\u002Frobweiss.faculty.biostat.ucla.edu\u002Fwriting_advice_2)\n\n***Take-away: The more prominent part, the more time you invest in it.***\n\nNext, we will mainly discuss the polishing of the details. At present, AI assistants like [ChatGPT](https:\u002F\u002Fchatgpt.com) and [Claude](https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fclaude-3-5-sonnet) can easily help authors address the basic issues in English writing. We also recommend that authors in the Chinese region use [跃问](https:\u002F\u002Fyuewen.cn\u002Fchats\u002Fnew) or [豆包](https:\u002F\u002Fwww.doubao.com\u002Fchat\u002F). You can have the AI generate multiple versions and choose the most suitable one. When utilizing these tools, remember that prioritize clarity over style.\n\nLet's move on to discuss the issues that are not easy to handle automatically. We will measure the readability of papers using the following concepts: logical strength, defensibility, confusion time, and information density. Based on these concepts, some practical suggestions and techniques are described to improve the readability.\n\n### Enhance Logical Strength\n***Take-away: Do not misuse or abuse connectives.***\n\nIn academic writing, logical coherence is more crucial than elegant vocabulary. Logical coherence is rooted in the logic itself, not in the connectives. We should view connectives as enhancements that smooth language, rather than using them to artificially construct sentence logic. Misalignment between connectives and actual logic can be confusing and greatly diminish readability. Here are a few specific examples:\n\n> We argue that problem A is critical. To this end, we propose method B.\n\n\"To this end\" refers to which end? In fact, the previous context only presents a viewpoint without specifying any actions or goals, so the use of this connective is inherently incorrect. Connectives must be grammatically correct.\n\n> The system comprises three modules. First of all, Module A is .... Second, Module B is .... Last but not least, Module C is ....\n\nHere, several connectives impose a certain order on these three things that originally have no order relationship. We should not use connectives to create logical relationships. It would be better to introduce the three modules separately.\n\n### Consider Defensibility\nWhen we write, we should think about how readers might find fault with every sentence we write. If they believe something that seems wrong, they might doubt the whole paper. To enhance the paper's trustworthiness, we need to minimize the likelihood of being challenged.\n\n***Take-away: Make statements based on references and facts.***\n\nWhen we write \"Problem A is a pain point in this field and has not been solved yet,\" we should consider that the reader may ask, \"Why is this a pain point? How serious are the consequences? Does this consequence have a significant impact on the final performance?\" This requires the addition of appropriate references.\n\n> It is reported that problem A results in ... [1,2,3] and ... [4,5], which are critical to ... because ... [6, 7, 8].\n\nWhen discussing the results of a paper, it is even more necessary to be rigorous:\n\n> The performance improves, which is attributed to the fact that XXX...\n\nThe evidence should be presented prominently;\n\n> The improvement may be explained by the fact that XXX...\n \nSome indirect evidence such as visualizations can be shown.\n\nBe as objective as possible and avoid exaggerating.\n\n### Shorten Confusion Time\n\"Confusion time\" is the sum of the time readers spend on each \"hmm, what's this?\" to \"oh, I get it\" moment during the reading process. The shorter the total confusion time of a paper, the higher the readability, and the more peaceful the reader will be.\n\n***Take-away: Explain a concept as close as possible when it is proposed.***\n\nIt is recommended to directly explain the essence of a component after giving its name; for example, \"We propose XXX, which is implemented with a two-layer multilayer perceptron (MLP).\" If a concept is not easy to explain, it can be supplemented by referring to literature.\n\n***Take-away: Resolve relative pronoun ambiguity.***\n\nIf it is not possible to make a long sentence completely unambiguous, it should be broken down into short sentences. A large proportion of the readership is not native speakers, and fancy sentence structures do not earn extra points.\n\n***Take-away: Frequently use topic sentences, preferably at the beginning of paragraphs.***\n\nThe reader may not be able to quickly understand all the details, at which point the main information can be quickly obtained by the reader through the topic sentences, to avoid affecting the overall reading experience.\n\n### Increase Information Density\n\"Information density\" refers to the efficiency with which text provides effective information to readers. Low information density may cause readers to lose focus and question the expertise.\n\n***Take-away: Get to the point as soon as possible.***\n\nThe beginning of each section may talk about the history. Try not to be lengthy. \"Do not write irrelevant content, nor should you write about things that most readers are already familiar with.\" Discussing the development of human writing skills, would certainly deter the vast majority of our readers.\n\n***Take-away: Both text and charts should be appropriately detailed or concise.***\n\nUse an appropriate layout that balances text and visuals. Avoid common pitfalls like featuring a large chart with only a few key points highlighted. Or a very long passage describing the experimental details and hyper-parameters, which should really be placed in the appendix.\n\n***Take-away: Important explanations and elucidations should be as close to the charts as possible.***\n\nThe ideal situation is that each chart can be understood independently of the main text. In the caption, try to clearly state the theme and key conclusions. If there are abbreviations in the chart, it is best to have an explanation. \n\nIf you want to emphasize a certain result in Table 5, it is best for the sentence analyzing that result to be on the same page as Table 5, and it is best to have the words \"Table 5\" before and after that sentence. This is because readers may not carefully read the text you write, but first look at the charts and then look for text related to the content of the charts. When they see a striking result in Table 5 and become curious, they may use the search function in the PDF reader to search for \"Table 5\". \n\nDo not expect readers to figure out for themselves from a complex table who should be compared with whom to draw conclusions. We should put the content we want to compare. If such a table is difficult to design, it is worth repeating a certain result (usually a baseline that needs to be compared with several groups of results) several times, even if it means sacrificing the elegance. No one will reject your paper because the tables are not elegant, but it is very annoying if the table is not clear.\n\n### Detail Checklist\nFirst and foremost, avoid making mistakes. Prioritize the rigor of the paper before considering its aesthetics. The following is a checklist that can help authors improve their writing:\n\n- [ ] Go through the charts to ensure the story is complete. Strive to improve the quality of the charts and make them self-explanatory.\n- [ ] Check for any inconsistencies in symbols, abbreviations, and references.\n- [ ] Whether the level of detail in the text and charts is appropriate?\n- [ ] Place important information in prominent positions.\n- [ ] Can the text and legends in the figure be larger?\n- [ ] Can the understanding speed of tables be improved by using methods such as column division, bolding text, and deleting redundancy?\n- [ ] Can the reproducibility be improved? For example, by providing details and key code in the appendix.\n\nWe will list more minor items in the [Appendix](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper?tab=readme-ov-file#appendix).\n## Conclusion\n***Take-away: Good luck!***\n\nAs this manuscript stands without the benefit of peer review, it undoubtedly contains numerous imperfections. The concepts presented herein are primarily derived from widely shared community knowledge, which we have endeavored to synthesize and simplify for the benefit of newcomers to the community. Our goal is to provide a concise yet comprehensive guide that can ease the learning curve for those embarking on the journey of writing AI conference papers. If this document serves as a beacon of clarity and direction for any reader, we would consider our efforts successful. *Leaving a star will be a great encouragement to us.*\n\n## Appendix\nIn the Appendix, several topics are covered:\n\n[Checklist for Last Few Hours](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper\u002Ftree\u002Fmain?tab=readme-ov-file#checklist-for-last-few-hours): It provides a checklist to ensure the paper is in order before submission.\n\n[AI Paper Production and Publication](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper?tab=readme-ov-file#ai-paper-production-and-publication): It outlines the process of paper submission, review, and publication in AI conferences.\n\n[Common Negative Review Comments](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper\u002Ftree\u002Fmain?tab=readme-ov-file#common-negative-review-comments): It lists common criticisms reviewers might have and suggestions for revision.\n\n[If the Paper Is Not Accepted](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper\u002Ftree\u002Fmain?tab=readme-ov-file#if-the-paper-is-not-accepted): It offers advice on dealing with rejection and improving the paper for future submissions.\n\n[AI Conference List](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper\u002Ftree\u002Fmain?tab=readme-ov-file#ai-conference-list): Some information of notable AI conferences that you might find useful.\n\n### Checklist for the Last Few Hours\n- [ ] Check that various numbers are not copied incorrectly.\n- [ ] Search for question marks to check for latex errors.\n- [ ] Make sure that all charts are mentioned in the main text and that the order of mentions matches the order in which the charts appear.\n- [ ] The caption is very noticeable. Avoid grammatical errors, and it is recommended to use a period at the end.\n- [ ] Vectorize the charts. \n- [ ] Check that all formulas are complete, they are easily overlooked in the editing process.\n- [ ] Go through all the subtitles and unify the capitalization style.\n- [ ] Confirm that there are no figures outside the main body pages.\n- [ ] Check for anonymity. You may need to delete acknowledgments. If you have submitted code or a demo, you must also pay great attention to anonymity.\n- [ ] **Ensure the number of pages is correct to avoid being desk rejected.**\n\n### AI Paper Production and Publication\n\nThis section mainly introduces the process of producing papers and the review process. A conference paper usually runs about eight pages in a two-column layout, or over ten pages in a single-column layout, based on the conference's specifications. Authors prepare and submit their paper along with supplementary materials like code and demo videos by the given deadline.\n\nProvided there are no critical oversights, such as neglecting to anonymize the submission, substantial formatting issues, or surpassing the page limit — any of which could result in an immediate rejection (known as a \"desk reject\") — the paper proceeds to the review phase. Following approximately two months, authors receive feedback from typically three reviewers in the form of comments and an overall score for their paper. Many of these reviewers have published work in related domains and might be cited in the submitted paper. With the initial review outcomes, authors must craft a brief rebuttal, generally one page, to address queries or provide additional findings. Roughly half of the papers are withdrawn during this rebuttal phase. Reviewers then deliberate for a week or two (commonly on a private platform) based on the rebuttal, indicating whether their concerns have been alleviated and discussing the paper's merits. Usually, reviewers align on a positive or negative stance, though occasionally, the area chair decides.\n\nThe final acceptance outcome necessitates waiting for approximately another month, after which it will be revealed via the email system. Typically, acceptance rates range from one-sixth to one-quarter of submitted manuscripts. Authors then revise their work based on reviewer feedback before submitting the final, camera-ready version for publication. The majority of papers, however, face rejection and are returned to the authors. These authors may opt to resubmit following the previously mentioned process or decide to discontinue work on the paper. It is worth noting that most papers undergo an extensive period of refinement and revision, colloquially dubbed the \"Fibonacci Submission Approach.\" (Recommendation: [a Chinese lecture by Boxin Shi](https:\u002F\u002Fhub.baai.ac.cn\u002Fview\u002F8659)).\n\n### Common Negative Review Comments\nWe have listed some common negative reviews and suggested revisions (in italics).\n\n- Criticizing the author for being unprofessional: Important references are missing; the paper structure is messy, and some essential elements are lacking, such as not submitting supplementary video results for a video-related study; the experimental setup is significantly different from previous work. \n\n*Refer to the reference list of recent papers to fill in the gaps, and the configuration should be aligned.*\n\n- Questioning the validity: The reported results do not conform to common sense and are not credible; exaggerating one's own achievements or making some obviously incorrect assertions; there are flaws in the experimental setup or argumentation. \n\n*Conduct more experiments, refine the expression, and strive for rigor.*\n\n- Not respecting previous work: Not citing the latest results, conducting low-benchmark experiments; excessively demeaning the work of predecessors; confusing one's own work with the contributions of predecessors. \n\n*Compare more with existing work tables, conduct more paper research, and if you say others have done a poor job, provide evidence.*\n\n- Lack of novelty: The story is not well written, the logic is not clear, or most of it is known knowledge; feeling that the work is incremental and does not contribute much. In other words, the effect is not impressive.\n\n*Discuss with some peers, and highlight the strengths.*\n\n- Poor paper presentation quality: Many grammatical errors, poor writing, poor English level; difficult to understand, lacking some details. \n\n*Use AI tools or [Grammarly](https:\u002F\u002Fwww.grammarly.com) to revise, and ask friends for help to read.*\n\n- Disagreement on the approach: Not approving of the experimental design or not believing in this technical route.\n\n*Conduct more experiments or cite similar expressions in relevant literature to support your argument, and try to win over other reviewers.*\n\n### If the Paper is Not Accepted\n> Review process is highly random. But there is one golden rule that withstands the test of time and randomness - **badly written papers get bad reviews. Period.** It doesn’t matter if the idea is good, result is good, citations are good. Not at all. Writing is critical — and this is ironic because engineers are the worst-trained writers among all disciplines in a university. You need to discipline yourself: leave time for writing, think deeply about writing, and write it over and over again till it’s as polished as you can think of. (Fei-Fei Li)\n\nThere are many papers that stayed on arXiv after being rejected and now have a huge impact [1](https:\u002F\u002Farxiv.org\u002Fabs\u002F1503.02531);[2](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.11692);[3](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.04861);[4](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.06160). [Many great works have been rejected and even got extremely negative comments.](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FMachineLearning\u002Fcomments\u002Fvywfx3\u002Fd_are_there_any_rejected_papers_that_ended_up\u002F) The review process is especially painful for novices, who may be putting all their eggs in one basket. The paper will be significantly improved throughout the process. If this process helps you produce a truly good paper, you can benefit from and be proud of it for many years to come. Remember, the paper is only the initial step or a small part of the overall work.\n\n### AI Conference List\nThe schedule can usually be found on [AI Conference Deadlines](https:\u002F\u002Faideadlin.es\u002F?sub=CV,ML,NLP). The acceptance rate can be found on [Conference-Acceptance-Rate](https:\u002F\u002Fgithub.com\u002Flixin4ever\u002FConference-Acceptance-Rate).\n\nNote: Acceptance rates and submission dates vary. Always check the official conference website for the most current information.\n\n| Conference Name | Typical Submission Month | Recent Acceptance Rate |\n| --- | --- | --- |\n| IJCAI | January | ~14% |\n| ICML | January | ~27% |\n| ICCV\u002FECCV | March | ~27% |\n| BMVC | April | ~26% |\n| ACMMM | April | ~26% |\n| NeurIPS | May | ~26% |\n| EMNLP | May | ~23% |\n| WACV | June and August | ~45% |\n| ACCV | July | ~33% |\n| AAAI | July | ~24% | \n| ICASSP | September | ~45% |\n| ICLR | September | ~31% |\n| NAACL | September | ~23% |\n| ICRA | September | ~45% | \n| AISTATS | October | ~28% |\n| CVPR | November | ~24% | \n| ACL | Rolling Review | ~23% |\n","# 撰写人工智能会议论文：初学者手册\n\n**我们认为，本文最需要的是同行评审，我们热忱欢迎任何形式的宝贵建议。**\n\n作者：[hzwer](https:\u002F\u002Fgithub.com\u002Fhzwer)、[DingXiaoH](https:\u002F\u002Fgithub.com\u002FDingXiaoH)\n\n知乎 [1](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F593195527)-[2](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F639732057)-[3](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F627032371)｜[跃问中翻](https:\u002F\u002Fyuewen.cn\u002Fshare\u002F145749938443137024?utm_source=share&utm_content=web_linkcopy&version=2) | [豆包总结](https:\u002F\u002Fwww.doubao.com\u002Fthread\u002Fw750d882cf0af6419) | [公众号](https:\u002F\u002Fmp.weixin.qq.com\u002Fs\u002FMjeBZDV6xapuA_L6ODpVcA)\n\n**摘要。** *对于新手而言，撰写研究论文可能面临诸多挑战，尤其是在截止日期临近且缺乏学术投稿经验的情况下。一篇准备不足的论文不仅会让合作者和读者感到困扰，还常常导致被拒或需要进行大量修改。在本文中，我们将为希望撰写人工智能会议论文的初学者分享一些实用技巧。我们的目标是让这篇文章成为一份面向初学者的指南，帮助大家更轻松地分享学术成果。*\n\n## 引言\n\n> **背景。** GPU集群已经运行了半年，你感觉成果已经相当显著。然而，你突然意识到，一场重要会议的投稿截止日期不到一个月了，而你至今只写过几份课程作业报告。到底应该提前多久完成初稿，才能避免错过截止日期？一篇优秀的研究论文与糟糕的论文究竟有何区别？在开始写作之前又该做些什么呢？这些问题像噩梦般萦绕心头，让你只能盯着空白的Overleaf页面发呆。幸运的是，本文正是为你而写。\n\n在本文中，我们将围绕会议论文的撰写展开讨论，重点针对初学者常遇到的误区。文章主要分为两个部分：**[从零构建论文](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper?tab=readme-ov-file#build-a-paper-from-scratch)** 和 **[细节打磨](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper?tab=readme-ov-file#readability-improvement)**。我们希望通过提供切实可行的指导，帮助初学者克服学术写作中的复杂性，以清晰、自信的方式为学术界贡献力量。在此，我们诚挚推荐由Chao Ma整理的[写作技巧资源清单](https:\u002F\u002Fvision.sjtu.edu.cn\u002Fwriting.html)。\n\n## 从零构建论文\n\n本节将详细介绍如何从零开始撰写一篇人工智能领域的论文，内容涵盖核心思想、整体框架、引言以及相关工作部分。\n\n### 找到核心思想\n你可能已经有了有趣的发现和实验结果，但却不确定如何提炼出论文的核心主题。*大多数已发表论文的关键贡献，恰好属于以下三类中的一类（摘自[Nowozion](https:\u002F\u002Fwww.nowozin.net\u002Fsebastian\u002Fblog\u002Ften-tips-for-writing-cs-papers-part-1.html)）：*\n\n*洞察：你对已存在的现象给出了新的解释。*\n*性能：你能把某件事做得更好。*\n*能力：你能做到以前无法实现的事情。*\n\n明确你工作的核心优势，并在论文的早期部分加以强调。这样，读者就能带着预期继续阅读后续内容。此外，你还可以从其他方面进一步拓展整体的新颖性。**新颖性似乎难以捉摸，它到底是什么？** 关键的研究课题、高效的解决方案以及创新性的技术贡献，是构成论文新颖性的主要要素。例如，深度学习领域许多早期具有影响力的成果，正是由于其潜在的全局性影响而诞生；RAFT和NeRF等方法则凭借卓越的性能吸引了大量研究者，它们的核心思想之外还包含大量的工程化处理。像“批归一化”和“残差学习”这样的技术，之所以备受推崇，正是因为其实用性和有效性。通过突出你工作的新颖性，你将能够分辨哪些方面值得深入探讨，哪些只是无关紧要的细节。\n\n> 毕加索笔下的一抹随意涂鸦，同样可以如伦勃朗的精妙画作般令人赞叹。—— [科学中的新颖性](https:\u002F\u002Fmedium.com\u002F@black_51980\u002Fnovelty-in-science-8f1fd1a0a143)（强烈推荐给读者）\n\n***要点：清晰理解你的方法相较于前人有哪些增量，并提炼出一到两个核心思想。***\n\n读者阅读论文时，往往是在寻找新颖的洞见。一篇优秀的论文应当具备鲜明且易于记忆的亮点。你需要不断打磨自己的核心观点，直到确信他人会迫不及待地想要了解并广泛分享这些内容。尤其需要注意的是，有些想法或许非常出色，但如果缺乏原创性，则未必适合在论文中详尽描述。撰写论文时，重点应放在提供新颖、独特且有价值的观点上，以吸引读者的注意并激发他们的兴趣。\n  \n不要低估自己工作的新颖性。深入挖掘其背后的原理。如果将ResNet论文改写成：“我们基于VGGNet和GoogleNet的设计思路，采用大量3×3卷积层并引入并行捷径连接”，那么这篇论文也将失去其新颖性。而ResNet论文真正讲述的故事，是提出问题、抽象出底层原理、给出自己的解决方案与具体实现，并通过实验加以验证。这或许并不完全反映他们的研究过程（参见[知乎链接](https:\u002F\u002Fwww.zhihu.com\u002Fquestion\u002F406913672\u002Fanswer\u002F1339549216)），但它有效地展示了他们的发现。\n\n***要点：发现新现象、分享新思路，比单纯提升性能更为重要。***\n\n许多优秀的论文往往在实验结果上表现出色。这容易让人误以为，好的结果就是论文的全部价值所在。然而事实上，实验结果只是对新发现的一种佐证。微小的结果改进并不一定意味着新知识的产生。撰写论文时，首先要思考读者能从你的工作中学到什么新东西，而不仅仅是炫耀比别人更好的结果。\n\n此外，何凯明指出，研究人员应当着眼于未来，而非仅仅追求过去的“最先进”水平。通过运用奥卡姆剃刀原则——寻求简单而有效的解决方案——并在真实场景中验证研究的同时，预测实验结果及未来需求，研究人员可以减少研究中的“过拟合”现象。与其一味为眼前的验证而精心设计实验方案和指标，不如在写作过程中更多地考虑工作的长期正确性和实际意义。那些经得起时间考验、真正正确的发现，才能持续发挥影响力。通过摒弃那些仅为发表论文而使用的复杂技巧，找到简洁有效的解决方案，研究人员便更有可能使自己的工作推广应用于未来的各种场景。\n\n### 构建框架\n***要点：摘要—引言—主体，层层递进。每个部分都应自成一体。***\n\n一篇典型的论文结构包括：1. 摘要，2. 引言，以及 3. 主体部分，其中主体通常包含相关工作、方法论、实验、讨论、结论和参考文献等章节。我们可以将这一结构划分为三个层次。在每一层中，都应力求呈现一个完整的研究叙事。每一层都是对前一层的进一步展开与深化。基于这一理解，接下来我们将探讨如何有效地讲述研究故事。对于初学者而言，建议优先完成论文的主体部分。\n\n***要点：明确目标读者，重点介绍有价值的发现，而非曲折的研究过程。***\n\n在紧扣核心思想的前提下，开始梳理拟在论文中呈现的内容。首先可以制作一张简单的幻灯片，向同行、同事或导师展示你的研究思路与成果，以评估他们是否能够理解。不妨主动征求那些不熟悉你研究领域的专家的意见，从而发现潜在的理解盲区。与实验过程不同，撰写论文时应突出有价值的新颖性，避免呈现尚不完善或过于复杂的研究细节。研究人员都深知科研的艰辛，但这种苦涩的描述更适合放在项目的后记中。始终从读者的角度出发，不断审视并优化你的表达，直到它变得通俗易懂为止。\n\n如果你觉得实验设计的严谨性不足，可能还需要补充一些额外的实验来完善你的工作。同时，建议进行充分的文献调研，最好能找到几篇主题高度相关的论文，将其视为你论文的潜在竞争对手，并仔细分析它们的优点与不足。思考哪些方面能够真正吸引学术界的关注，然后加以强化；而那些陈词滥调的内容则尽量减少甚至删除。\n\n***要点：围绕贡献陈述，在结果部分展开扎实的分析。***\n\n许多读者在决定是否阅读全文之前，会先查看结果部分，以初步判断方法的有效性。他们会确认你的贡献是否与实验结果相符。即使你对自己的方法充满信心，也往往需要通过更多的对比实验和消融实验来加以验证。因此，应多制作表格和图表，挑选最具代表性的内容进行展示。诚实客观至关重要，切忌夸大其词。如果担心过度宣称，不妨与同行交流讨论。\n\n### 撰写引言\n有了上述材料，你就可以尝试撰写引言了。关于引言的结构，我们直接引用教科书中的内容（来自 [Elena](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC3178846\u002F)）：\n\n**步骤 1：确立研究领域**\n\na. 阐明该研究领域的重要性、核心地位、趣味性及其存在的问题；\n\n**步骤 2：找到研究空白**\n\na. 指出前人研究中的不足之处，或在某些方面拓展已有知识；\n\n**步骤 3：填补空白**\n\na. 阐述研究目的或说明本研究的性质；  \nb. 列出研究问题或假设；  \nc. 宣布主要发现；  \nd. 说明本研究的价值；  \ne. 介绍论文的整体结构。\n\n**附加建议：**\n\na. Knuth：*时刻牢记读者的需求；*\n\nb. 直奔主题，避免过多无关内容。论文的新颖性和吸引力应尽早展现；\n\nc. 多花些笔墨描述原创性和新颖的想法；\n\nd. 尊重前人的工作，在指出其不足之处之前，先肯定他们的历史贡献；\n\ne. 可考虑使用“首页图”来突出论文最重要的内容，以吸引读者注意。\n\n正如我们之前提到的，主体部分实际上是引言的扩展版。通常，通过在引言中补充更多实验细节，便构成了论文的主体部分。\n\n### 描述相关工作\n***要点：平庸的做法是简单罗列历史脉络，而更好的做法则是聚焦于不同方法与你的工作的关联。***\n\n这部分内容不一定必须包含在引言中。一般情况下，论文会单独设立一节来讨论相关工作，其中包括背景研究和竞争性研究。选择三到四个与你的论文最相关的主题，分别梳理每个主题的历史演进过程。不要一味列举其他方法的缺点，而是要说明你是如何对其进行改进的。你可以先撰写一篇独立于自己工作的文献综述。在对前人方法进行分类和排序时（例如称某方法为先驱），务必注意论述的准确性。若把握不准，可参考你所引用的论文中的“相关工作”部分。最后，再从更贴合你论文特点的角度重新组织这一部分内容。\n\n## 提升可读性\n> “文章千古事，得失寸心知。”——[杜甫](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FDu_Fu)\n\n> “最好的写作指导就是：好文章不过是坏文章经过反复修改的结果。这句话出自斯蒂芬·金，至于他从哪里学来的，我就不知道了。现在我把这句话送给你，请转告你的学生。”——[罗伯特·魏斯](https:\u002F\u002Frobweiss.faculty.biostat.ucla.edu\u002Fwriting_advice_2)\n\n***要点：越是重要的部分，就越需要投入更多时间打磨。***\n\n接下来我们将主要讨论细节的润色。目前，像 [ChatGPT](https:\u002F\u002Fchatgpt.com) 和 [Claude](https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fclaude-3-5-sonnet) 这样的AI助手，已经能够轻松帮助作者解决英语写作中的基本问题。我们也推荐中文地区的作者使用 [跃问](https:\u002F\u002Fyuewen.cn\u002Fchats\u002Fnew) 或 [豆包](https:\u002F\u002Fwww.doubao.com\u002Fchat\u002F) 等工具。你可以让AI生成多个版本，然后从中挑选最合适的一个。在使用这些工具时，切记要以清晰度为先，而非过分追求文采。\n\n接下来我们将讨论一些难以通过自动化手段解决的问题。我们将从逻辑强度、论证合理性、信息密度以及读者理解所需的时间等几个维度来衡量论文的可读性。基于这些概念，下面将提供一些实用的建议和技巧，以进一步提升论文的可读性。\n\n### 提升逻辑性\n***要点：不要误用或滥用连接词。***\n\n在学术写作中，逻辑连贯性比华丽的词汇更为重要。逻辑连贯性源于内容本身的逻辑，而非依赖于连接词。我们应将连接词视为润色语言的辅助工具，而不是用来人为构建句子逻辑的手段。如果连接词与实际逻辑不符，不仅会令人困惑，还会大大降低文章的可读性。以下是一些具体示例：\n\n> 我们认为问题A至关重要。为此，我们提出了方法B。\n\n“为此”指的是什么？事实上，前文仅提出了一种观点，并未明确任何行动或目标，因此使用该连接词本身就不恰当。连接词必须符合语法规则。\n\n> 该系统由三个模块组成。首先，模块A是……其次，模块B是……最后但并非最不重要的是，模块C是……\n\n这里，多个连接词为原本并无先后顺序的三件事强加了某种顺序。我们不应借助连接词来制造逻辑关系，而是分别介绍这三个模块会更为合适。\n\n### 考虑论证的可靠性\n在写作时，我们应当设想读者可能会对每一句话提出质疑。如果他们认为某处存在明显错误，就可能对整篇论文产生怀疑。为了增强论文的可信度，我们需要尽量减少被质疑的可能性。\n\n***要点：基于参考文献和事实陈述观点。***\n\n当我们写道“问题A是本领域的痛点，至今仍未解决”时，应考虑到读者可能会问：“为什么这是痛点？其后果有多严重？这些后果是否会对最终性能产生重大影响？”这就需要补充相应的参考文献。\n\n> 据报道，问题A会导致……[1,2,3]以及……[4,5]，而这些因素对……至关重要，因为……[6,7,8]。\n\n在讨论论文结果时，更需严谨：\n\n> 性能有所提升，这归因于XXX……\n\n证据应清晰突出地呈现；\n\n> 这一提升或许可以解释为XXX……\n\n也可以展示一些间接证据，例如可视化图表。同时，务必保持客观，避免夸大其词。\n\n### 缩短读者的困惑时间\n“困惑时间”是指读者在阅读过程中，从“嗯，这是什么？”到“哦，我明白了”的每个瞬间所花费的时间总和。一篇论文的总困惑时间越短，其可读性就越高，读者也会感到更加轻松。\n\n***要点：在提出概念时，尽可能就近加以解释。***\n\n建议在给出某个组件的名称后，立即说明其本质；例如，“我们提出了XXX，它由一个两层的多层感知器（MLP）实现。”如果某个概念难以解释，可以通过引用文献来补充说明。\n\n***要点：消除关系代词的歧义。***\n\n如果无法使长句完全无歧义，则应将其拆分为若干短句。由于相当一部分读者并非母语使用者，过于复杂的句式并不会为其增分。\n\n***要点：频繁使用主题句，最好置于段落开头。***\n\n读者未必能迅速理解所有细节，此时通过主题句即可快速获取主要信息，从而避免影响整体阅读体验。\n\n### 提高信息密度\n“信息密度”是指文本向读者传递有效信息的效率。信息密度低可能导致读者注意力分散，并对作者的专业性产生质疑。\n\n***要点：尽快切入正题。***\n\n各部分的开头可以简要回顾相关背景，但不宜冗长。“不要撰写无关内容，也不要重复大多数读者已熟悉的内容。”例如，若大谈人类书写技能的发展历程，无疑会让绝大多数读者失去兴趣。\n\n***要点：文字与图表都应详略得当。***\n\n采用图文并茂、布局合理的排版方式。避免常见的误区，如只突出几个关键点的大图表，或过长的实验细节和超参数描述——这类内容更适合放在附录中。\n\n***要点：重要的解释说明应尽量靠近图表。***\n\n理想情况下，每张图表都应能在脱离正文的情况下独立理解。在图注中，应清晰阐明主题及关键结论。若图表中使用缩写，最好附上解释说明。\n\n若想强调表5中的某项结果，分析该结果的句子最好与表5同页出现，并且在句子前后明确标注“表5”。这是因为读者通常不会逐字细读全文，而是先浏览图表，再寻找与图表内容相关的文字。当他们在表5中看到引人注目的结果并产生好奇时，很可能会使用PDF阅读器的搜索功能查找“表5”。不要指望读者能够自行从复杂的表格中判断出哪些数据应该相互比较以得出结论。我们应该直接列出需要对比的内容。如果设计这样的表格较为困难，不妨多次重复某一关键结果（通常是需要与多组结果进行对比的基准），即使牺牲一定的美观性也在所不惜。没有人会因为表格不够美观而拒绝你的论文，但如果表格不够清晰，却会让人非常困扰。\n\n### 细节检查清单\n首要任务是避免犯错。在追求论文美观之前，应优先确保其严谨性。以下是一个帮助作者提升写作质量的检查清单：\n\n- [ ] 通览图表，确保故事完整。努力提升图表质量，使其具备自明性。\n- [ ] 检查符号、缩写和参考文献是否存在不一致之处。\n- [ ] 文字与图表的详略程度是否恰当？\n- [ ] 是否已将重要信息置于显眼位置？\n- [ ] 图中的文字和图例能否适当放大？\n- [ ] 是否可通过分列、加粗文字、删除冗余等方式提高表格的易读性？\n- [ ] 是否能提升可重复性？例如，在附录中提供详细步骤和关键代码。\n\n更多细节将在[附录](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper?tab=readme-ov-file#appendix)中列出。\n\n## 结论\n***要点：祝你好运！***\n\n在未经同行评审的情况下，本文难免存在诸多不足之处。文中所阐述的概念主要源自社区内广泛共享的知识，我们尽力将其整合并简化，以帮助新加入社区的成员更快上手。我们的目标是提供一份简明而全面的指南，帮助那些刚开始撰写人工智能会议论文的人们降低学习门槛。若本文能为任何读者带来清晰的思路与方向，我们将视为成功。*留下一颗星将是对我们极大的鼓励。*\n\n## 附录\n在附录中，涵盖了以下几个主题：\n\n[最后几小时检查清单](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper\u002Ftree\u002Fmain?tab=readme-ov-file#checklist-for-last-few-hours)：提供了一份检查清单，用于确保论文在提交前一切就绪。\n\n[AI论文的制作与发表](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper?tab=readme-ov-file#ai-paper-production-and-publication)：概述了人工智能会议中论文的投稿、评审及发表流程。\n\n[常见的负面评审意见](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper\u002Ftree\u002Fmain?tab=readme-ov-file#common-negative-review-comments)：列出了审稿人可能提出的常见批评以及改进建议。\n\n[如果论文未被接受](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper\u002Ftree\u002Fmain?tab=readme-ov-file#if-the-paper-is-not-accepted)：提供了应对拒稿的建议，并指导如何改进论文以备再次投稿。\n\n[AI会议列表](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper\u002Ftree\u002Fmain?tab=readme-ov-file#ai-conference-list)：列出了一些值得关注的人工智能会议的相关信息，供您参考。\n\n### 最后几小时检查清单\n- [ ] 检查各类数字是否抄写无误。\n- [ ] 搜索问号以排查 LaTeX 错误。\n- [ ] 确保所有图表均在正文中提及，且提及顺序与图表出现顺序一致。\n- [ ] 图表的标题应醒目易读，避免语法错误，建议在末尾加上句号。\n- [ ] 将图表矢量化处理。\n- [ ] 检查所有公式是否完整，编辑过程中容易被忽略。\n- [ ] 通读所有小标题，统一大小写格式。\n- [ ] 确认正文页数之外没有多余图表。\n- [ ] 检查匿名性要求，必要时需删除致谢部分；若提交了代码或演示视频，则更需注意保持匿名。\n- [ ] **务必确认页数准确，以免被直接拒稿。**\n\n### AI论文的制作与发表\n\n本节主要介绍论文的制作流程及评审机制。根据会议的具体要求，一篇会议论文通常采用双栏排版时长约八页，单栏排版则超过十页。作者需在截止日期前准备好论文及相关补充材料（如代码、演示视频等）并按时提交。\n\n只要不存在重大疏漏——例如未进行匿名处理、格式严重不符或超出页数限制等可能导致直接拒稿的情况——论文便会进入评审阶段。大约两个月后，作者会收到三位审稿人的反馈，包括评语和综合评分。这些审稿人大多在相关领域发表过论文，甚至可能被作者引用。基于初步评审结果，作者需撰写约一页的简短回复，回应审稿人的疑问或补充新的发现。这一阶段大约有一半的论文会被撤回。随后，审稿人会在一周至两周内（通常在专用平台上）讨论回复内容，判断作者是否已消除其顾虑，并评估论文的价值。多数情况下，审稿人会达成一致意见，但有时也会由领域主席作出最终决定。\n\n最终的接收结果还需再等待约一个月，届时将通过电子邮件通知。一般来说，接收率约为提交稿件的六分之一至四分之一。作者需根据审稿意见修改论文，提交最终定稿以供出版。然而，大多数论文都会被拒稿并退回给作者。这些作者可以选择按照上述流程重新投稿，也可以选择放弃该论文。值得注意的是，许多论文会经历漫长的打磨与修改过程，业内俗称“斐波那契投稿法”。（推荐观看石博鑫的中文讲座：[链接](https:\u002F\u002Fhub.baai.ac.cn\u002Fview\u002F8659)）\n\n### 常见的负面评审意见\n我们列举了一些常见的负面评价及相应的改进建议（以斜体标注）。\n\n- 批评作者不够专业：缺少重要参考文献；论文结构混乱，缺乏关键要素，例如针对视频相关研究未提交补充视频结果；实验设置与已有工作差异过大。\n\n*参考近期论文的参考文献列表进行补充，并调整实验配置使其更具可比性。*\n\n- 质疑论文的有效性：报告的结果不符合常理，缺乏可信度；夸大自身成果或做出明显错误的断言；实验设计或论证存在缺陷。\n\n*增加实验次数，优化表达方式，力求严谨。*\n\n- 不尊重前人工作：未引用最新研究成果，实验基准过低；过度贬低前人工作；混淆自身工作与前人贡献。\n\n*多与现有工作对比，深入查阅文献；若指出他人工作不佳，务必提供证据。*\n\n- 缺乏创新性：故事叙述不连贯，逻辑不清，或大部分内容为已知知识；给人感觉工作只是微小改进，贡献不大。换句话说，效果并不突出。\n\n*与同行交流讨论，突出论文的独特优势。*\n\n- 论文质量较差：语法错误较多，写作水平欠佳，英语能力有限；表述晦涩难懂，细节缺失。\n\n*可借助 AI 工具或 Grammarly 进行修改，并请朋友帮忙阅读。*\n\n- 对研究方法持有异议：不认可实验设计，或对技术路线持怀疑态度。\n\n*可通过更多实验验证，或引用相关文献中的类似观点来支持自己的论点，争取说服其他审稿人。*\n\n### 如果论文未被接受\n> 审稿过程具有很强的随机性。但有一条经得起时间和随机性考验的黄金法则——**写作糟糕的论文必然会得到差评。就这么简单。** 无论想法多好、结果多好、引用多高，都无关紧要。写作至关重要——这听起来颇具讽刺意味，因为在大学的所有学科中，工程师往往是最不擅长写作的一群人。你需要严格要求自己：留出充足的时间进行写作，深入思考如何表达，并反复修改，直到文章达到你能想到的最完美状态。（李飞飞）\n\n有许多论文在被拒后仍保留在 arXiv 上，如今却产生了巨大影响[1](https:\u002F\u002Farxiv.org\u002Fabs\u002F1503.02531)；[2](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.11692)；[3](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.04861)；[4](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.06160)。【许多优秀的工作曾被拒，甚至收到过极为负面的评审意见。】(https:\u002F\u002Fwww.reddit.com\u002Fr\u002FMachineLearning\u002Fcomments\u002Fvywfx3\u002Fd_are_there_any_rejected_papers_that_ended_up\u002F) 对于初学者而言，审稿过程尤为煎熬，因为他们很可能把全部希望都寄托在一篇论文上。事实上，论文往往会在整个审稿过程中得到显著改进。如果这个过程帮助你产出了一篇真正优秀的论文，那么你不仅会从中受益，还能为此感到自豪多年。请记住，论文只是整个研究工作的初始步骤或其中一小部分。\n\n### 人工智能会议列表\n会议日程通常可以在 [AI Conference Deadlines](https:\u002F\u002Faideadlin.es\u002F?sub=CV,ML,NLP) 上找到。而各会议的接收率则可在 [Conference-Acceptance-Rate](https:\u002F\u002Fgithub.com\u002Flixin4ever\u002FConference-Acceptance-Rate) 上查阅。\n\n注意：接收率和投稿截止日期可能会有所变化，请务必以各会议官方网站上的最新信息为准。\n\n| 会议名称 | 典型投稿月份 | 近年接收率 |\n| --- | --- | --- |\n| IJCAI | 1月 | ~14% |\n| ICML | 1月 | ~27% |\n| ICCV\u002FECCV | 3月 | ~27% |\n| BMVC | 4月 | ~26% |\n| ACMMM | 4月 | ~26% |\n| NeurIPS | 5月 | ~26% |\n| EMNLP | 5月 | ~23% |\n| WACV | 6月和8月 | ~45% |\n| ACCV | 7月 | ~33% |\n| AAAI | 7月 | ~24% |\n| ICASSP | 9月 | ~45% |\n| ICLR | 9月 | ~31% |\n| NAACL | 9月 | ~23% |\n| ICRA | 9月 | ~45% |\n| AISTATS | 10月 | ~28% |\n| CVPR | 11月 | ~24% |\n| ACL | 滚动审稿 | ~23% |","# WritingAIPaper 快速上手指南\n\n**工具简介**：WritingAIPaper 并非一个可安装的软件包或代码库，而是一份由社区维护的**开源写作手册与指南**。它旨在帮助初学者掌握撰写 AI 会议论文的核心技巧，涵盖从核心思想提炼、框架搭建到细节润色的全过程。\n\n由于本工具本质为文档资源，无需进行环境配置或命令安装，请直接阅读以下使用指南。\n\n## 1. 环境准备\n\n本指南以在线文档形式存在，无特定的系统要求或前置依赖。\n\n*   **阅读设备**：任意可访问互联网的电脑、平板或手机。\n*   **推荐平台**：\n    *   **GitHub 原版**：适合查看最新更新及英文原版内容。\n    *   **国内镜像\u002F翻译版**（推荐国内用户）：\n        *   [知乎专栏系列](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F593195527)（中文深度解读）\n        *   [跃问中翻版](https:\u002F\u002Fyuewen.cn\u002Fshare\u002F145749938443137024)\n        *   [豆包总结版](https:\u002F\u002Fwww.doubao.com\u002Fthread\u002Fw750d882cf0af6419)\n*   **写作工具建议**：虽然指南本身不限工具，但文中示例多基于 **Overleaf** (LaTeX) 或主流 Markdown 编辑器。建议提前注册 Overleaf 账号以便实践。\n\n## 2. 安装步骤\n\n本工具**无需安装**。\n\n*   **在线阅读**：直接点击上述链接在浏览器中打开即可。\n*   **本地保存（可选）**：\n    若希望离线阅读或参与贡献，可通过 Git 克隆仓库：\n    ```bash\n    git clone https:\u002F\u002Fgithub.com\u002Fhzwer\u002FWritingAIPaper.git\n    ```\n    进入目录后，使用 Markdown 阅读器（如 VS Code + Markdown Preview Enhanced 插件）打开 `README.md` 及相关章节文件。\n\n## 3. 基本使用\n\n本指南的使用核心在于**遵循其提出的写作思维框架**。以下是基于手册内容的三个关键实践步骤：\n\n### 第一步：提炼核心思想 (Find the Core Idea)\n在动笔前，明确你的工作属于以下三类贡献中的哪一种，并以此贯穿全文：\n1.  **Insight (洞察)**：对已有现象提供了新的解释。\n2.  **Performance (性能)**：在某项任务上做得更好。\n3.  **Capability (能力)**：实现了以前无法完成的任务。\n\n> **操作建议**：用一句话概括你的核心增量（Increment），确保读者能迅速抓住重点。避免堆砌实验数据而忽视新思想的表达。\n\n### 第二步：构建论文框架 (Construct the Framework)\n按照“摘要 -> 引言 -> 正文”的三层递进结构组织内容，每一层都应是一个完整的故事：\n1.  **Abstract (摘要)**：浓缩整个故事。\n2.  **Introduction (引言)**：扩展摘要，明确研究动机、缺口（Niche）及本文贡献。\n3.  **Main Body (正文)**：包含相关工作、方法、实验、讨论等，是对引言的详细支撑。\n\n> **操作建议**：先制作几页简单的 PPT 向同行展示思路，收集反馈后再开始撰写正文。确保实验部分围绕贡献陈述进行扎实的分析（包括对比实验和消融实验）。\n\n### 第三步：撰写引言与相关工作 (Write Introduction & Related Work)\n*   **引言结构**：遵循 \"Move 1 (建立领域重要性) -> Move 2 (发现研究缺口) -> Move 3 (占据缺口\u002F提出本文方案)\" 的逻辑。\n*   **相关工作**：不要仅罗列历史，要重点阐述现有方法与你的工作之间的逻辑关系（是改进、对比还是互补）。\n\n> **操作建议**：尽早放入\"Page One Figure\"（首页图），直观展示核心方法或成果，吸引读者继续阅读。\n\n---\n*注：本指南强调同行评审（Peer Review）的重要性，建议在写作过程中积极寻求导师或同行的反馈。*","研究生小李在 GPU 集群上跑出了显著的实验结果，但距离顶会截稿仅剩三周，面对空白文档不知如何下笔。\n\n### 没有 WritingAIPaper 时\n- **核心贡献模糊**：无法从“性能提升”或“新能力”等维度精准提炼创新点，导致论文主题散乱，审稿人难以抓住重点。\n- **架构搭建困难**：缺乏从零构建论文的框架指导，在引言和相关工作部分花费大量时间试错，迟迟无法完成初稿。\n- **陷入焦虑循环**：因缺乏经验而担心格式规范与写作陷阱，反复修改细节却忽略整体逻辑，最终可能因准备不足被拒稿。\n\n### 使用 WritingAIPaper 后\n- **定位清晰明确**：依据工具提供的“洞察、性能、能力”三类贡献模型，迅速锁定核心优势并在开篇强调，让读者带着预期阅读。\n- **写作路径顺畅**：跟随“从零构建”指南，快速搭建起包含核心思想、整体框架及引言的标准结构，高效完成首版草稿。\n- **避坑信心倍增**：参考新手常见误区分析与润色建议，规避了学术写作中的典型陷阱，能够从容地专注于内容打磨而非格式担忧。\n\nWritingAIPaper 将新手从截稿前的迷茫与焦虑中解放出来，通过结构化指南帮助研究者清晰、自信地展示学术成果。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fhzwer_WritingAIPaper_f090cdc9.png","hzwer","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fhzwer_afcaad10.jpg",null,"@stepfun-ai","https:\u002F\u002Fscholar.google.com.hk\u002Fcitations?user=zJEkaG8AAAAJ&hl=zh-CN&oi=ao","https:\u002F\u002Fgithub.com\u002Fhzwer",3612,127,"2026-04-10T11:08:23",1,"","未说明",{"notes":85,"python":83,"dependencies":86},"该工具并非软件代码库，而是一份关于如何撰写人工智能会议论文的指南手册（Handbook）。README 内容主要提供写作建议、论文结构指导和核心思想提炼方法，不涉及任何运行环境、依赖库或硬件资源需求。",[],[15,14,13],[89,90,91],"ai","paper","writing","2026-03-27T02:49:30.150509","2026-04-11T14:58:04.061126",[],[]]