[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-93544":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":12,"openIssues":13,"contributorsCount":13,"subscribersCount":13,"size":13,"stars1d":14,"stars7d":14,"stars30d":14,"stars90d":13,"forks30d":13,"starsTrendScore":15,"compositeScore":16,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":17,"fork":17,"defaultBranch":18,"hasWiki":19,"hasPages":17,"topics":20,"createdAt":9,"pushedAt":9,"updatedAt":21,"readmeContent":22,"aiSummary":9,"trendingCount":13,"starSnapshotCount":13,"syncStatus":23,"lastSyncTime":24,"discoverSource":25},93544,"awesome-papers-awesome","LanceZPF\u002Fawesome-papers-awesome","LanceZPF","The unified gateway to high-quality Awesome XXX Paper repositories.",null,"Python",157,1,0,25,75,72.9,false,"main",true,[],"2026-07-22 04:02:09","\u003C!-- GENERATED FILE: edit data\u002Fcurated_repositories.json and run scripts\u002Fcollect_github.py. -->\n\n\u003Ca id=\"readme-top\">\u003C\u002Fa>\n\n\u003Cdiv align=\"center\">\n  \u003Ch1>Awesome Papers Awesome\u003C\u002Fh1>\n  \u003Cp>\u003Cstrong>One index for the indexes: discover the best GitHub paper collections across computer science.\u003C\u002Fstrong>\u003C\u002Fp>\n  \u003Cp>以 ACM CCS 为主干、将人工智能设为独立一级领域的计算机科学论文合集导航。\u003C\u002Fp>\n  \u003Cp>\n    \u003Ca href=\"https:\u002F\u002Fawesome.re\">\u003Cimg src=\"https:\u002F\u002Fawesome.re\u002Fbadge-flat2.svg\" alt=\"Awesome\">\u003C\u002Fa>\n    \u003Ca href=\"TAXONOMY.md\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Ftaxonomy-ACM%20CCS%202012-343a40?style=flat-square\" alt=\"ACM CCS 2012 taxonomy\">\u003C\u002Fa>\n  \u003C\u002Fp>\n  \u003Cp>\u003Cstrong>260 verified repositories · 390 category placements · 95 topics · 14 fields\u003C\u002Fstrong>\u003C\u002Fp>\n  \u003Cp>\u003Cstrong>Bilingual \u002F 双语\u003C\u002Fstrong> · \u003Ca href=\"README.en.md\">English\u003C\u002Fa> · \u003Ca href=\"README.zh-CN.md\">简体中文\u003C\u002Fa>\u003C\u002Fp>\n\u003C\u002Fdiv>\n\nA curated meta-index of repositories that maintain bibliographies, reading lists, survey collections, and paper notes. It uses an [ACM Computing Classification System-aligned hierarchy](TAXONOMY.md), with artificial intelligence as an independent first-level field and machine learning as one of its topics; every link points to a collection, not an individual paper.\n\n> **Snapshot:** GitHub stars checked on 2026-07-16. Stars help discovery; they are not quality scores. Repository descriptions preserve their upstream language.\n\n**Popular shortcuts:** [Artificial intelligence](#ccs-14) → [Machine learning](#ccs-14-02) · [Systems](#ccs-03) · [Data management](#ccs-08-01) · [Security](#ccs-09) · [HCI](#ccs-10-01) · [Computer graphics](#ccs-11-05)\n\n\u003Ca id=\"section-contents\">\u003C\u002Fa>\n## Contents\n\n- [How this list works](#section-how-it-works)\n- [Browse by field](#section-browse)\n- [Contributing](#section-contributing)\n- [Data and maintenance](#section-data)\n\n\u003Ca id=\"section-how-it-works\">\u003C\u002Fa>\n## How this list works\n\n- **Collections, not single papers.** A repository must contain a sustained paper list, bibliography, or substantial paper section; one-paper implementations and generic tool lists are out of scope.\n- **Evidence before popularity.** Every entry is manually reviewed for paper-list evidence, then ranked by its current GitHub stars. Strong niche collections are welcome even when their star count is modest.\n- **Specific beats broad.** Topic-focused collections are preferred. A repository may be cross-listed only when it is genuinely useful in more than one field.\n- **Healthy repositories only.** Forks and archived repositories are excluded, and near-identical repository names are capped to reduce mirrors and clones.\n\n\u003Ca id=\"section-browse\">\u003C\u002Fa>\n## Browse by field\n\n| # | Field | Topics | Links |\n|---:|---|---:|---:|\n| 1 | [General and reference · 通用与参考（辅助）](#ccs-01) | 2 | 11 |\n| 2 | [Artificial intelligence · 人工智能](#ccs-14) | 13 | 99 |\n| 3 | [Hardware · 硬件](#ccs-02) | 10 | 31 |\n| 4 | [Computer systems organization · 计算机系统组织](#ccs-03) | 4 | 14 |\n| 5 | [Networks · 网络](#ccs-04) | 8 | 25 |\n| 6 | [Software and its engineering · 软件及其工程](#ccs-05) | 3 | 13 |\n| 7 | [Theory of computation · 计算理论](#ccs-06) | 8 | 24 |\n| 8 | [Mathematics of computing · 计算数学](#ccs-07) | 6 | 20 |\n| 9 | [Information systems · 信息系统](#ccs-08) | 5 | 25 |\n| 10 | [Security and privacy · 安全与隐私](#ccs-09) | 10 | 34 |\n| 11 | [Human-centered computing · 以人为中心的计算](#ccs-10) | 6 | 18 |\n| 12 | [Computing methodologies · 计算方法](#ccs-11) | 6 | 25 |\n| 13 | [Applied computing · 应用计算](#ccs-12) | 11 | 41 |\n| 14 | [Social and professional topics · 社会与专业议题](#ccs-13) | 3 | 10 |\n\n_Links count category placements; a repository can appear in more than one field when appropriate._\n\n\u003Ca id=\"ccs-01\">\u003C\u002Fa>\n## 1. General and reference · 通用与参考（辅助）\n\n**Topics:** [Document types · 文献类型](#ccs-01-01) · [Cross-computing tools and techniques · 跨计算工具与技术](#ccs-01-02)\n\n\u003Ca id=\"ccs-01-01\">\u003C\u002Fa>\n### 1.1 Document types · 文献类型 (8 collections)\n\n- [mli\u002Fpaper-reading](https:\u002F\u002Fgithub.com\u002Fmli\u002Fpaper-reading) — 深度学习经典、新论文逐段精读。 _★ 33,576_\n- [arXivTimes\u002FarXivTimes](https:\u002F\u002Fgithub.com\u002FarXivTimes\u002FarXivTimes) — repository to research & share the machine learning articles. _★ 3,899_\n- [eugeneyan\u002Fml-surveys](https:\u002F\u002Fgithub.com\u002Feugeneyan\u002Fml-surveys) — Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc. _★ 2,900_\n- [AakashKumarNain\u002Fannotated_research_papers](https:\u002F\u002Fgithub.com\u002FAakashKumarNain\u002Fannotated_research_papers) — This repo contains annotated research papers that I found really good and useful. _★ 2,790_\n- [aleju\u002Fpapers](https:\u002F\u002Fgithub.com\u002Faleju\u002Fpapers) — Summaries of machine learning papers. _★ 2,501_\n- [huggingface\u002Fawesome-papers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fawesome-papers) — Papers & presentation materials from Hugging Face's internal science day. _★ 2,052_\n- [NiuTrans\u002FABigSurvey](https:\u002F\u002Fgithub.com\u002FNiuTrans\u002FABigSurvey) — A collection of 1000+ survey papers on Natural Language Processing (NLP) and Machine Learning (ML). _★ 2,030_\n- [Machine-Learning-Tokyo\u002Fpapers-with-annotations](https:\u002F\u002Fgithub.com\u002FMachine-Learning-Tokyo\u002Fpapers-with-annotations) — Research papers with annotations, illustrations and explanations. _★ 826_\n\n\u003Ca id=\"ccs-01-02\">\u003C\u002Fa>\n### 1.2 Cross-computing tools and techniques · 跨计算工具与技术 (3 collections)\n\n- [leipzig\u002Fawesome-reproducible-research](https:\u002F\u002Fgithub.com\u002Fleipzig\u002Fawesome-reproducible-research) — A curated list of reproducible research case studies, projects, tutorials, and media. _★ 391_\n- [faroit\u002Freproducible-audio-research](https:\u002F\u002Fgithub.com\u002Ffaroit\u002Freproducible-audio-research) — List of Reproducible Audio Research Papers. _★ 79_\n- [XuYuanchi\u002FAwesome-Reproducible-Analysis-of-Single-Cell-Papers](https:\u002F\u002Fgithub.com\u002FXuYuanchi\u002FAwesome-Reproducible-Analysis-of-Single-Cell-Papers) — A curated list of awesome reproducible papers in single-cell omics. _★ 11_\n\n\u003Cp align=\"right\">\u003Ca href=\"#readme-top\">Back to top ↑\u003C\u002Fa>\u003C\u002Fp>\n\n\u003Ca id=\"ccs-11-03\">\u003C\u002Fa>\n\u003Ca id=\"ccs-14\">\u003C\u002Fa>\n## 2. Artificial intelligence · 人工智能\n\n**Topics:** [AI foundations and interdisciplinary research · 人工智能基础与交叉研究](#ccs-14-01) · [Machine learning · 机器学习](#ccs-14-02) · [Generative AI and foundation models · 生成式人工智能与基础模型](#ccs-14-03) · [Natural language processing, speech, and audio · 自然语言处理、语音与音频](#ccs-14-04) · [Computer vision and visual perception · 计算机视觉与视觉感知](#ccs-14-05) · [Multimodal learning and intelligence · 多模态学习与智能](#ccs-14-06) · [Knowledge representation and reasoning · 知识表示与推理](#ccs-14-07) · [Search, planning, and scheduling · 搜索、规划与调度](#ccs-14-08) · [Reinforcement learning and sequential decision making · 强化学习与序贯决策](#ccs-14-09) · [Intelligent agents and multi-agent systems · 智能体与多智能体系统](#ccs-14-10) · [Robotics and embodied AI · 机器人与具身智能](#ccs-14-11) · [AI systems and infrastructure · 人工智能系统与基础设施](#ccs-14-12) · [Trustworthy, safe, and responsible AI · 可信、安全与负责任的人工智能](#ccs-14-13)\n\n\u003Ca id=\"ccs-14-01\">\u003C\u002Fa>\n### 2.1 AI foundations and interdisciplinary research · 人工智能基础与交叉研究 (6 collections)\n\n- [mli\u002Fpaper-reading](https:\u002F\u002Fgithub.com\u002Fmli\u002Fpaper-reading) — 深度学习经典、新论文逐段精读。 _★ 33,576_\n- [owainlewis\u002Fawesome-artificial-intelligence](https:\u002F\u002Fgithub.com\u002Fowainlewis\u002Fawesome-artificial-intelligence) — A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers. _★ 15,275_\n- [dair-ai\u002FAI-Papers-of-the-Week](https:\u002F\u002Fgithub.com\u002Fdair-ai\u002FAI-Papers-of-the-Week) — Highlighting the top ML papers every week. _★ 12,653_\n- [eugeneyan\u002Fml-surveys](https:\u002F\u002Fgithub.com\u002Feugeneyan\u002Fml-surveys) — Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc. _★ 2,900_\n- [huggingface\u002Fawesome-papers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fawesome-papers) — Papers & presentation materials from Hugging Face's internal science day. _★ 2,052_\n- [aimerou\u002Fawesome-ai-papers](https:\u002F\u002Fgithub.com\u002Faimerou\u002Fawesome-ai-papers) — A curated list of the most impressive AI papers. _★ 1,304_\n\n\u003Ca id=\"ccs-11-03-01\">\u003C\u002Fa>\n\u003Ca id=\"ccs-14-02\">\u003C\u002Fa>\n### 2.2 Machine learning · 机器学习 (10 collections)\n\n- [floodsung\u002FDeep-Learning-Papers-Reading-Roadmap](https:\u002F\u002Fgithub.com\u002Ffloodsung\u002FDeep-Learning-Papers-Reading-Roadmap) — Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! _★ 39,544_\n- [terryum\u002Fawesome-deep-learning-papers](https:\u002F\u002Fgithub.com\u002Fterryum\u002Fawesome-deep-learning-papers) — The most cited deep learning papers. _★ 26,166_\n- [thunlp\u002FGNNPapers](https:\u002F\u002Fgithub.com\u002Fthunlp\u002FGNNPapers) — Must-read papers on graph neural networks (GNN). _★ 16,815_\n- [jindongwang\u002Ftransferlearning](https:\u002F\u002Fgithub.com\u002Fjindongwang\u002Ftransferlearning) — Transfer learning \u002F domain adaptation \u002F domain generalization \u002F multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习。 _★ 14,339_\n- [diff-usion\u002FAwesome-Diffusion-Models](https:\u002F\u002Fgithub.com\u002Fdiff-usion\u002FAwesome-Diffusion-Models) — A collection of resources and papers on Diffusion Models. _★ 12,352_\n- [yzhao062\u002Fanomaly-detection-resources](https:\u002F\u002Fgithub.com\u002Fyzhao062\u002Fanomaly-detection-resources) — Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works! _★ 9,344_\n- [dair-ai\u002FML-Papers-Explained](https:\u002F\u002Fgithub.com\u002Fdair-ai\u002FML-Papers-Explained) — Explanation to key concepts in ML. _★ 8,578_\n- [hibayesian\u002Fawesome-automl-papers](https:\u002F\u002Fgithub.com\u002Fhibayesian\u002Fawesome-automl-papers) — A curated list of automated machine learning papers, articles, tutorials, slides and projects. _★ 4,150_\n- [tirthajyoti\u002FPapers-Literature-ML-DL-RL-AI](https:\u002F\u002Fgithub.com\u002Ftirthajyoti\u002FPapers-Literature-ML-DL-RL-AI) — Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning. _★ 2,919_\n- [AI-Efficiency\u002FAwesome-Model-Quantization](https:\u002F\u002Fgithub.com\u002FAI-Efficiency\u002FAwesome-Model-Quantization) — A list of papers, docs, codes about model quantization. _★ 2,405_\n\n\u003Ca id=\"ccs-14-03\">\u003C\u002Fa>\n### 2.3 Generative AI and foundation models · 生成式人工智能与基础模型 (7 collections)\n\n- [Hannibal046\u002FAwesome-LLM](https:\u002F\u002Fgithub.com\u002FHannibal046\u002FAwesome-LLM) — Awesome-LLM: a curated list of Large Language Model. _★ 27,149_\n- [BradyFU\u002FAwesome-Multimodal-Large-Language-Models](https:\u002F\u002Fgithub.com\u002FBradyFU\u002FAwesome-Multimodal-Large-Language-Models) — Latest Advances on Multimodal Large Language Models. _★ 17,951_\n- [diff-usion\u002FAwesome-Diffusion-Models](https:\u002F\u002Fgithub.com\u002Fdiff-usion\u002FAwesome-Diffusion-Models) — A collection of resources and papers on Diffusion Models. _★ 12,352_\n- [codefuse-ai\u002FAwesome-Code-LLM](https:\u002F\u002Fgithub.com\u002Fcodefuse-ai\u002FAwesome-Code-LLM) — [TMLR] A curated list of language modeling researches for code (and other software engineering activities), plus related datasets. _★ 3,411_\n- [leofan90\u002FAwesome-World-Models](https:\u002F\u002Fgithub.com\u002Fleofan90\u002FAwesome-World-Models) — A comprehensive list of papers for the definition of World Models and using World Models for General Video Generation, Embodied AI, and Autonomous Driving… _★ 1,896_\n- [YingqingHe\u002FAwesome-LLMs-meet-Multimodal-Generation](https:\u002F\u002Fgithub.com\u002FYingqingHe\u002FAwesome-LLMs-meet-Multimodal-Generation) — A curated list of papers on LLMs-based multimodal generation (image, video, 3D and audio). _★ 549_\n- [DuNGEOnmassster\u002Fawesome-customized-generative-AI](https:\u002F\u002Fgithub.com\u002FDuNGEOnmassster\u002Fawesome-customized-generative-AI) — Papers and codes collection for customized, personalized and editable generative models. _★ 28_\n\n\u003Ca id=\"ccs-14-04\">\u003C\u002Fa>\n### 2.4 Natural language processing, speech, and audio · 自然语言处理、语音与音频 (8 collections)\n\n- [Hannibal046\u002FAwesome-LLM](https:\u002F\u002Fgithub.com\u002FHannibal046\u002FAwesome-LLM) — Awesome-LLM: a curated list of Large Language Model. _★ 27,149_\n- [sebastianruder\u002FNLP-progress](https:\u002F\u002Fgithub.com\u002Fsebastianruder\u002FNLP-progress) — Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. _★ 22,957_\n- [codefuse-ai\u002FAwesome-Code-LLM](https:\u002F\u002Fgithub.com\u002Fcodefuse-ai\u002FAwesome-Code-LLM) — [TMLR] A curated list of language modeling researches for code (and other software engineering activities), plus related datasets. _★ 3,411_\n- [ybayle\u002Fawesome-deep-learning-music](https:\u002F\u002Fgithub.com\u002Fybayle\u002Fawesome-deep-learning-music) — List of articles related to deep learning applied to music. _★ 2,974_\n- [NiuTrans\u002FABigSurvey](https:\u002F\u002Fgithub.com\u002FNiuTrans\u002FABigSurvey) — A collection of 1000+ survey papers on Natural Language Processing (NLP) and Machine Learning (ML). _★ 2,030_\n- [yingpengma\u002FAwesome-Story-Generation](https:\u002F\u002Fgithub.com\u002Fyingpengma\u002FAwesome-Story-Generation) — This repository collects an extensive list of awesome papers about Story Generation \u002F Storytelling, exclusively focusing on the era of Large Language Models… _★ 639_\n- [metame-ai\u002Fawesome-audio-plaza](https:\u002F\u002Fgithub.com\u002Fmetame-ai\u002Fawesome-audio-plaza) — Daily tracking of awesome audio papers, including music generation, zero-shot tts, asr, audio generation. _★ 410_\n- [juhayna-zh\u002FAwesome-Music-Generation-Papers](https:\u002F\u002Fgithub.com\u002Fjuhayna-zh\u002FAwesome-Music-Generation-Papers) — Curated list of groundbreaking music generation research. _★ 21_\n\n\u003Ca id=\"ccs-14-05\">\u003C\u002Fa>\n### 2.5 Computer vision and visual perception · 计算机视觉与视觉感知 (7 collections)\n\n- [MrNeRF\u002Fawesome-3D-gaussian-splatting](https:\u002F\u002Fgithub.com\u002FMrNeRF\u002Fawesome-3D-gaussian-splatting) — Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months. _★ 8,759_\n- [awesome-NeRF\u002Fawesome-NeRF](https:\u002F\u002Fgithub.com\u002Fawesome-NeRF\u002Fawesome-NeRF) — A curated list of awesome neural radiance fields papers. _★ 6,774_\n- [openMVG\u002Fawesome_3DReconstruction_list](https:\u002F\u002Fgithub.com\u002FopenMVG\u002Fawesome_3DReconstruction_list) — A curated list of papers & resources linked to 3D reconstruction from images. _★ 4,410_\n- [Daisy-Zhang\u002FAwesome-Deepfakes-Detection](https:\u002F\u002Fgithub.com\u002FDaisy-Zhang\u002FAwesome-Deepfakes-Detection) — A list of tools, papers and code related to Deepfake Detection. _★ 1,803_\n- [tstanislawek\u002Fawesome-document-understanding](https:\u002F\u002Fgithub.com\u002Ftstanislawek\u002Fawesome-document-understanding) — A curated list of resources for Document Understanding (DU) topic. _★ 1,525_\n- [sotayang\u002FAwesome-Streaming-Video-Understanding](https:\u002F\u002Fgithub.com\u002Fsotayang\u002FAwesome-Streaming-Video-Understanding) — [Awesome] Latest Papers, Codes & Datasets on Streaming \u002F Online Video Understanding — Building Always-on, Real-time Video AI. _★ 408_\n- [ingra14m\u002FAwesome-Inverse-Rendering](https:\u002F\u002Fgithub.com\u002Fingra14m\u002FAwesome-Inverse-Rendering) — A collection of papers on neural field-based inverse rendering. _★ 260_\n\n\u003Ca id=\"ccs-14-06\">\u003C\u002Fa>\n### 2.6 Multimodal learning and intelligence · 多模态学习与智能 (6 collections)\n\n- [BradyFU\u002FAwesome-Multimodal-Large-Language-Models](https:\u002F\u002Fgithub.com\u002FBradyFU\u002FAwesome-Multimodal-Large-Language-Models) — Latest Advances on Multimodal Large Language Models. _★ 17,951_\n- [tstanislawek\u002Fawesome-document-understanding](https:\u002F\u002Fgithub.com\u002Ftstanislawek\u002Fawesome-document-understanding) — A curated list of resources for Document Understanding (DU) topic. _★ 1,525_\n- [showlab\u002FAwesome-Unified-Multimodal-Models](https:\u002F\u002Fgithub.com\u002Fshowlab\u002FAwesome-Unified-Multimodal-Models) — This is a repository for organizing papers, codes and other resources related to unified multimodal models. _★ 828_\n- [YingqingHe\u002FAwesome-LLMs-meet-Multimodal-Generation](https:\u002F\u002Fgithub.com\u002FYingqingHe\u002FAwesome-LLMs-meet-Multimodal-Generation) — A curated list of papers on LLMs-based multimodal generation (image, video, 3D and audio). _★ 549_\n- [friedrichor\u002FAwesome-Multimodal-Papers](https:\u002F\u002Fgithub.com\u002Ffriedrichor\u002FAwesome-Multimodal-Papers) — A curated list of awesome Multimodal studies. _★ 341_\n- [WILLOSCAR\u002FAwesome-HCI-LLM](https:\u002F\u002Fgithub.com\u002FWILLOSCAR\u002FAwesome-HCI-LLM) — Awesome-HCI （Ubiquitous, LLM, MLLM, Agent, RAG, Embodied-AI, RLHF). _★ 25_\n\n\u003Ca id=\"ccs-14-07\">\u003C\u002Fa>\n### 2.7 Knowledge representation and reasoning · 知识表示与推理 (7 collections)\n\n- [DEEP-PolyU\u002FAwesome-GraphRAG](https:\u002F\u002Fgithub.com\u002FDEEP-PolyU\u002FAwesome-GraphRAG) — Awesome-GraphRAG: A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation. _★ 2,529_\n- [zjukg\u002FKG-LLM-Papers](https:\u002F\u002Fgithub.com\u002Fzjukg\u002FKG-LLM-Papers) — [Paper List] Papers integrating knowledge graphs (KGs) and large language models (LLMs). _★ 2,217_\n- [heathersherry\u002FKnowledge-Graph-Tutorials-and-Papers](https:\u002F\u002Fgithub.com\u002Fheathersherry\u002FKnowledge-Graph-Tutorials-and-Papers) — Insightful Tutorials and Papers about Knowledge Graphs. _★ 1,058_\n- [zhaoyu-li\u002FDL4TP](https:\u002F\u002Fgithub.com\u002Fzhaoyu-li\u002FDL4TP) — [COLM 2024] A Survey on Deep Learning for Theorem Proving. _★ 226_\n- [thuwzy\u002FNeural-Symbolic-and-Probabilistic-Logic-Papers](https:\u002F\u002Fgithub.com\u002Fthuwzy\u002FNeural-Symbolic-and-Probabilistic-Logic-Papers) — A curated paper list on neural symbolic and probabilistic logic. _★ 137_\n- [mattfaltyn\u002Fawesome-neuro-symbolic-ai](https:\u002F\u002Fgithub.com\u002Fmattfaltyn\u002Fawesome-neuro-symbolic-ai) — A curated list of awesome Neuro-Symbolic AI frameworks, libraries, software, papers, and videos. _★ 15_\n- [tritrang88\u002Fawesome-llm-symbolic-reasoning](https:\u002F\u002Fgithub.com\u002Ftritrang88\u002Fawesome-llm-symbolic-reasoning) — This repository is a living collection of research papers exploring the exciting intersection of Neuro-Symbolic AI and Large Language Models. _★ 6_\n\n\u003Ca id=\"ccs-14-08\">\u003C\u002Fa>\n### 2.8 Search, planning, and scheduling · 搜索、规划与调度 (8 collections)\n\n- [guyulongcs\u002FAwesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising](https:\u002F\u002Fgithub.com\u002Fguyulongcs\u002FAwesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising) — Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. _★ 2,549_\n- [Thinklab-SJTU\u002Fawesome-ml4co](https:\u002F\u002Fgithub.com\u002FThinklab-SJTU\u002Fawesome-ml4co) — Awesome machine learning for combinatorial optimization papers. _★ 2,141_\n- [RUCAIBox\u002FAwesome-RSPapers](https:\u002F\u002Fgithub.com\u002FRUCAIBox\u002FAwesome-RSPapers) — Recommender System Papers. _★ 985_\n- [gabriben\u002Fawesome-generative-information-retrieval](https:\u002F\u002Fgithub.com\u002Fgabriben\u002Fawesome-generative-information-retrieval) — Curated paper and reading-list repository. _★ 726_\n- [ict-bigdatalab\u002Fawesome-pretrained-models-for-information-retrieval](https:\u002F\u002Fgithub.com\u002Fict-bigdatalab\u002Fawesome-pretrained-models-for-information-retrieval) — A curated list of awesome papers related to pre-trained models for information retrieval (a.k.a., pretraining for IR). _★ 677_\n- [S-Lab-System-Group\u002FAwesome-DL-Scheduling-Papers](https:\u002F\u002Fgithub.com\u002FS-Lab-System-Group\u002FAwesome-DL-Scheduling-Papers) — Curated paper and reading-list repository. _★ 332_\n- [matchyc\u002Fvector-search-papers](https:\u002F\u002Fgithub.com\u002Fmatchyc\u002Fvector-search-papers) — Awesome papers and technical blogs on vector DB (database), semantic-based vector search or approximate nearest neighbor search (ANN Search, ANNS). _★ 117_\n- [igeng\u002Fawesome-drl-cloud-scheduling](https:\u002F\u002Fgithub.com\u002Figeng\u002Fawesome-drl-cloud-scheduling) — A curated list of research papers, code, and tools applying deep reinforcement learning (DRL) to cloud\u002Fmicroservice resource scheduling and autoscaling. _★ 9_\n\n\u003Ca id=\"ccs-14-09\">\u003C\u002Fa>\n### 2.9 Reinforcement learning and sequential decision making · 强化学习与序贯决策 (8 collections)\n\n- [aikorea\u002Fawesome-rl](https:\u002F\u002Fgithub.com\u002Faikorea\u002Fawesome-rl) — Reinforcement learning resources curated. _★ 9,872_\n- [opendilab\u002Fawesome-RLHF](https:\u002F\u002Fgithub.com\u002Fopendilab\u002Fawesome-RLHF) — A curated list of reinforcement learning with human feedback resources (continually updated). _★ 4,413_\n- [tirthajyoti\u002FPapers-Literature-ML-DL-RL-AI](https:\u002F\u002Fgithub.com\u002Ftirthajyoti\u002FPapers-Literature-ML-DL-RL-AI) — Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning. _★ 2,919_\n- [TsinghuaC3I\u002FAwesome-RL-for-LRMs](https:\u002F\u002Fgithub.com\u002FTsinghuaC3I\u002FAwesome-RL-for-LRMs) — A Survey of Reinforcement Learning for Large Reasoning Models. _★ 2,467_\n- [hanjuku-kaso\u002Fawesome-offline-rl](https:\u002F\u002Fgithub.com\u002Fhanjuku-kaso\u002Fawesome-offline-rl) — An index of algorithms for offline reinforcement learning (offline-rl). _★ 1,071_\n- [Denghaoyuan123\u002FAwesome-RL-VLA](https:\u002F\u002Fgithub.com\u002FDenghaoyuan123\u002FAwesome-RL-VLA) — A Survey on Reinforcement Learning of Vision-Language-Action Models for Robotic Manipulation. _★ 793_\n- [jiachenli94\u002FAwesome-Decision-Making-Reinforcement-Learning](https:\u002F\u002Fgithub.com\u002Fjiachenli94\u002FAwesome-Decision-Making-Reinforcement-Learning) — A selection of state-of-the-art research materials on decision making and motion planning. _★ 242_\n- [igeng\u002Fawesome-drl-cloud-scheduling](https:\u002F\u002Fgithub.com\u002Figeng\u002Fawesome-drl-cloud-scheduling) — A curated list of research papers, code, and tools applying deep reinforcement learning (DRL) to cloud\u002Fmicroservice resource scheduling and autoscaling. _★ 9_\n\n\u003Ca id=\"ccs-14-10\">\u003C\u002Fa>\n### 2.10 Intelligent agents and multi-agent systems · 智能体与多智能体系统 (7 collections)\n\n- [WooooDyy\u002FLLM-Agent-Paper-List](https:\u002F\u002Fgithub.com\u002FWooooDyy\u002FLLM-Agent-Paper-List) — The paper list of the 86-page SCIS cover paper \"The Rise and Potential of Large Language Model Based Agents: A Survey\" by Zhiheng Xi et al. _★ 8,168_\n- [VoltAgent\u002Fawesome-ai-agent-papers](https:\u002F\u002Fgithub.com\u002FVoltAgent\u002Fawesome-ai-agent-papers) — A curated collection of AI agent research papers released in 2026, covering agent engineering, memory, evaluation, workflows, and autonomous systems. _★ 1,588_\n- [PurCL\u002FASE](https:\u002F\u002Fgithub.com\u002FPurCL\u002FASE) — A continuously updated collection of papers on agentic SE. _★ 630_\n- [MobileLLM\u002FPersonal_LLM_Agents_Survey](https:\u002F\u002Fgithub.com\u002FMobileLLM\u002FPersonal_LLM_Agents_Survey) — Paper list for Personal LLM Agents. _★ 433_\n- [YoungDubbyDu\u002FAwesome-LLM-Agent-Optimization-Papers](https:\u002F\u002Fgithub.com\u002FYoungDubbyDu\u002FAwesome-LLM-Agent-Optimization-Papers) — This is the reading list for the survey \"A Survey on the Optimization of LLM-based Agents \". _★ 241_\n- [qhy991\u002FAwesome-LLM-Circuit-Agent](https:\u002F\u002Fgithub.com\u002Fqhy991\u002FAwesome-LLM-Circuit-Agent) — A repository for academic works on RTL generation and Analog circuit generation based on LLM. _★ 21_\n- [tamlhp\u002Fawesome-sns](https:\u002F\u002Fgithub.com\u002Ftamlhp\u002Fawesome-sns) — Curated papers, datasets, and benchmarks for social network simulation, from classical network models to LLM-based agentic social systems. _★ 10_\n\n\u003Ca id=\"ccs-14-11\">\u003C\u002Fa>\n### 2.11 Robotics and embodied AI · 机器人与具身智能 (6 collections)\n\n- [LMD0311\u002FAwesome-World-Model](https:\u002F\u002Fgithub.com\u002FLMD0311\u002FAwesome-World-Model) — Collect some World Models for Autonomous Driving (and Robotic, etc.) papers. _★ 2,157_\n- [leofan90\u002FAwesome-World-Models](https:\u002F\u002Fgithub.com\u002Fleofan90\u002FAwesome-World-Models) — A comprehensive list of papers for the definition of World Models and using World Models for General Video Generation, Embodied AI, and Autonomous Driving… _★ 1,896_\n- [keon\u002Fawesome-physical-ai](https:\u002F\u002Fgithub.com\u002Fkeon\u002Fawesome-physical-ai) — A curated list of academic papers and resources on Physical AI — focusing on Vision-Language-Action (VLA) models, world models, embodied ai, and robotic… _★ 354_\n- [jslee02\u002Fawesome-robotics-simulation](https:\u002F\u002Fgithub.com\u002Fjslee02\u002Fawesome-robotics-simulation) — A curated list of resources for multibody dynamics simulation papers. _★ 95_\n- [HaoqinHong\u002FAwesome-Physically-Interactive-World-Models](https:\u002F\u002Fgithub.com\u002FHaoqinHong\u002FAwesome-Physically-Interactive-World-Models) — A curated list of papers and open-source resources focused on Physics-Inspired 3D Reconstruction and Simulation, intended to keep pace with the anticipated… _★ 57_\n- [FutureTwT\u002Fawesome-world-models-for-vla-agents](https:\u002F\u002Fgithub.com\u002FFutureTwT\u002Fawesome-world-models-for-vla-agents) — Official repository for \"Towards Generalist Embodied AI: A Survey on World Models for VLA Agents\". _★ 46_\n\n\u003Ca id=\"ccs-14-12\">\u003C\u002Fa>\n### 2.12 AI systems and infrastructure · 人工智能系统与基础设施 (10 collections)\n\n- [xlite-dev\u002FAwesome-LLM-Inference](https:\u002F\u002Fgithub.com\u002Fxlite-dev\u002FAwesome-LLM-Inference) — A curated list of Awesome LLM\u002FVLM Inference Papers with Codes: Flash-Attention, Paged-Attention, WINT8\u002F4, Parallelism, etc. _★ 5,396_\n- [HuaizhengZhang\u002FAI-Infra-from-Zero-to-Hero](https:\u002F\u002Fgithub.com\u002FHuaizhengZhang\u002FAI-Infra-from-Zero-to-Hero) — Awesome System for Machine Learning ⚡️ AI System Papers and Industry Practice. _★ 4,196_\n- [merrymercy\u002Fawesome-tensor-compilers](https:\u002F\u002Fgithub.com\u002Fmerrymercy\u002Fawesome-tensor-compilers) — A list of awesome compiler projects and papers for tensor computation and deep learning. _★ 2,764_\n- [AI-Efficiency\u002FAwesome-Model-Quantization](https:\u002F\u002Fgithub.com\u002FAI-Efficiency\u002FAwesome-Model-Quantization) — A list of papers, docs, codes about model quantization. _★ 2,405_\n- [byungsoo-oh\u002Fml-systems-papers](https:\u002F\u002Fgithub.com\u002Fbyungsoo-oh\u002Fml-systems-papers) — Curated collection of papers in machine learning systems. _★ 631_\n- [goabiaryan\u002Fawesome-gpu-engineering](https:\u002F\u002Fgithub.com\u002Fgoabiaryan\u002Fawesome-gpu-engineering) — GPU Engineering for AI Systems. _★ 539_\n- [AI-in-Transportation-Lab\u002Fawesome-tinyml](https:\u002F\u002Fgithub.com\u002FAI-in-Transportation-Lab\u002Fawesome-tinyml) — A carefully curated collection of high-quality libraries, projects, tutorials, research papers, and other essential resources focused on TinyML — the… _★ 132_\n- [LonghornSilicon\u002Fawesome-ai-accelerators](https:\u002F\u002Fgithub.com\u002FLonghornSilicon\u002Fawesome-ai-accelerators) — A curated list of AI accelerator papers, resources, tools, and open-source projects. _★ 124_\n- [MPSLab-ASU\u002FML-Accelerators](https:\u002F\u002Fgithub.com\u002FMPSLab-ASU\u002FML-Accelerators) — Topics in Machine Learning Accelerator Design. _★ 105_\n- [Kyrie-Zhao\u002Fawesome-real-time-AI](https:\u002F\u002Fgithub.com\u002FKyrie-Zhao\u002Fawesome-real-time-AI) — This is a list of awesome edgeAI inference related papers. _★ 98_\n\n\u003Ca id=\"ccs-14-13\">\u003C\u002Fa>\n### 2.13 Trustworthy, safe, and responsible AI · 可信、安全与负责任的人工智能 (9 collections)\n\n- [CryptoAILab\u002FAwesome-LM-SSP](https:\u002F\u002Fgithub.com\u002FCryptoAILab\u002FAwesome-LM-SSP) — A reading list for large models safety, security, and privacy (including Awesome LLM Security, Safety, etc.). _★ 2,017_\n- [EthicalML\u002Fawesome-artificial-intelligence-regulation](https:\u002F\u002Fgithub.com\u002FEthicalML\u002Fawesome-artificial-intelligence-regulation) — This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and beyond. _★ 1,452_\n- [ENSTA-U2IS-AI\u002Fawesome-uncertainty-deeplearning](https:\u002F\u002Fgithub.com\u002FENSTA-U2IS-AI\u002Fawesome-uncertainty-deeplearning) — This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models. _★ 821_\n- [stratosphereips\u002Fawesome-ml-privacy-attacks](https:\u002F\u002Fgithub.com\u002Fstratosphereips\u002Fawesome-ml-privacy-attacks) — An awesome list of papers on privacy attacks against machine learning. _★ 640_\n- [uclanlp\u002Fawesome-fairness-papers](https:\u002F\u002Fgithub.com\u002Fuclanlp\u002Fawesome-fairness-papers) — Papers on fairness in NLP. _★ 452_\n- [rockita\u002FcriticalML](https:\u002F\u002Fgithub.com\u002Frockita\u002FcriticalML) — Toward ethical, transparent and fair AI\u002FML: a critical reading list for engineers, designers, and policy makers. _★ 369_\n- [gnipping\u002FAwesome-ML-SP-Papers](https:\u002F\u002Fgithub.com\u002Fgnipping\u002FAwesome-ML-SP-Papers) — A curated list of Meachine learning Security & Privacy papers published in security top-4 conferences (IEEE S&P, ACM CCS, USENIX Security and NDSS). _★ 359_\n- [brighter-ai\u002Fawesome-privacy-papers](https:\u002F\u002Fgithub.com\u002Fbrighter-ai\u002Fawesome-privacy-papers) — Machine\u002Fdeep learning papers that address the topic of privacy in visual data. _★ 74_\n- [DfX-NYUAD\u002FLLM4IC](https:\u002F\u002Fgithub.com\u002FDfX-NYUAD\u002FLLM4IC) — LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trust. _★ 40_\n\n\u003Cp align=\"right\">\u003Ca href=\"#readme-top\">Back to top ↑\u003C\u002Fa>\u003C\u002Fp>\n\n\u003Ca id=\"ccs-02\">\u003C\u002Fa>\n## 3. Hardware · 硬件\n\n**Topics:** [Printed circuit boards · 印刷电路板](#ccs-02-01) · [Communication hardware, interfaces and storage · 通信硬件、接口与存储](#ccs-02-02) · [Integrated circuits · 集成电路](#ccs-02-03) · [Very large scale integration design · 超大规模集成电路设计](#ccs-02-04) · [Power and energy · 功耗与能效](#ccs-02-05) · [Electronic design automation · 电子设计自动化](#ccs-02-06) · [Hardware validation · 硬件验证](#ccs-02-07) · [Hardware test · 硬件测试](#ccs-02-08) · [Robustness · 硬件鲁棒性](#ccs-02-09) · [Emerging technologies · 新兴硬件技术](#ccs-02-10)\n\n\u003Ca id=\"ccs-02-01\">\u003C\u002Fa>\n### 3.1 Printed circuit boards · 印刷电路板 (3 collections)\n\n- [Charmve\u002FSurface-Defect-Detection](https:\u002F\u002Fgithub.com\u002FCharmve\u002FSurface-Defect-Detection) — 目前最大的工业缺陷检测数据库及论文集 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance. _★ 4,080_\n- [magic3007\u002FAwesome-Design-Automation](https:\u002F\u002Fgithub.com\u002Fmagic3007\u002FAwesome-Design-Automation) — Awesome - A curated list of amazing VLSI design automation papers, software and resources. _★ 11_\n- [parkourlrc\u002Fawesome-AI4EDA](https:\u002F\u002Fgithub.com\u002Fparkourlrc\u002Fawesome-AI4EDA) — 一个收录AI × 电子设计自动化（EDA）开源资源的精选列表，覆盖芯片物理设计（平面规划\u002F布局\u002F布线\u002F端到端流程）、PCB 设计、生成式路由、制造检测与研究基准。每一条目均链接到官方仓库或论文，并提供简要中文说明与许可证或网站信息。 _★ 7_\n\n\u003Ca id=\"ccs-02-02\">\u003C\u002Fa>\n### 3.2 Communication hardware, interfaces and storage · 通信硬件、接口与存储 (3 collections)\n\n- [dmemsys\u002Fawesome-disaggregated-memory](https:\u002F\u002Fgithub.com\u002Fdmemsys\u002Fawesome-disaggregated-memory) — A collection of awesome researchers and papers about disaggregated memory. _★ 192_\n- [sg20180546\u002FZNS-awesome-paper](https:\u002F\u002Fgithub.com\u002Fsg20180546\u002FZNS-awesome-paper) — Paper related to Zone NameSpace (SSD,HDD). _★ 90_\n- [yzr95924\u002Fawesome_storage_papers](https:\u002F\u002Fgithub.com\u002Fyzr95924\u002Fawesome_storage_papers) — Some paper lists related to storage systems. _★ 51_\n\n\u003Ca id=\"ccs-02-03\">\u003C\u002Fa>\n### 3.3 Integrated circuits · 集成电路 (3 collections)\n\n- [DfX-NYUAD\u002FGNN4IC](https:\u002F\u002Fgithub.com\u002FDfX-NYUAD\u002FGNN4IC) — Must-read papers on Graph Neural Networks (GNNs) for Integrated Circuits (ICs) design, security and reliability. _★ 93_\n- [DfX-NYUAD\u002FLLM4IC](https:\u002F\u002Fgithub.com\u002FDfX-NYUAD\u002FLLM4IC) — LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trust. _★ 40_\n- [chateda-ichip\u002FAwesome-AI-for-Chip-Design](https:\u002F\u002Fgithub.com\u002Fchateda-ichip\u002FAwesome-AI-for-Chip-Design) — A curated, community-maintained list of ML\u002FLLM research and tools for integrated-circuit design — across the full flow. _★ 20_\n\n\u003Ca id=\"ccs-02-04\">\u003C\u002Fa>\n### 3.4 Very large scale integration design · 超大规模集成电路设计 (3 collections)\n\n- [thu-nics\u002Fawesome_ai4eda](https:\u002F\u002Fgithub.com\u002Fthu-nics\u002Fawesome_ai4eda) — Curated paper and reading-list repository. _★ 290_\n- [Thinklab-SJTU\u002Fawesome-ai4eda](https:\u002F\u002Fgithub.com\u002FThinklab-SJTU\u002Fawesome-ai4eda) — Awesome Artificial Intelligence for Electronic Design Automation Papers. _★ 214_\n- [magic3007\u002FAwesome-Design-Automation](https:\u002F\u002Fgithub.com\u002Fmagic3007\u002FAwesome-Design-Automation) — Awesome - A curated list of amazing VLSI design automation papers, software and resources. _★ 11_\n\n\u003Ca id=\"ccs-02-05\">\u003C\u002Fa>\n### 3.5 Power and energy · 功耗与能效 (3 collections)\n\n- [fengbintu\u002FNeural-Networks-on-Silicon](https:\u002F\u002Fgithub.com\u002Ffengbintu\u002FNeural-Networks-on-Silicon) — This is originally a collection of papers on neural network accelerators. _★ 2,100_\n- [gigwegbe\u002Ftinyml-papers-and-projects](https:\u002F\u002Fgithub.com\u002Fgigwegbe\u002Ftinyml-papers-and-projects) — This is a list of interesting papers and projects about TinyML. _★ 1,027_\n- [open-neuromorphic\u002Fawesome-neuromorphic-hw](https:\u002F\u002Fgithub.com\u002Fopen-neuromorphic\u002Fawesome-neuromorphic-hw) — Repository collecting papers about neuromorphic hardware, such as ASIC and FPGA implementations of SNNs and stuff. _★ 214_\n\n\u003Ca id=\"ccs-02-06\">\u003C\u002Fa>\n### 3.6 Electronic design automation · 电子设计自动化 (3 collections)\n\n- [Thinklab-SJTU\u002FAwesome-LLM4EDA](https:\u002F\u002Fgithub.com\u002FThinklab-SJTU\u002FAwesome-LLM4EDA) — Curated paper and reading-list repository. _★ 292_\n- [thu-nics\u002Fawesome_ai4eda](https:\u002F\u002Fgithub.com\u002Fthu-nics\u002Fawesome_ai4eda) — Curated paper and reading-list repository. _★ 290_\n- [Thinklab-SJTU\u002Fawesome-ai4eda](https:\u002F\u002Fgithub.com\u002FThinklab-SJTU\u002Fawesome-ai4eda) — Awesome Artificial Intelligence for Electronic Design Automation Papers. _★ 214_\n\n\u003Ca id=\"ccs-02-07\">\u003C\u002Fa>\n### 3.7 Hardware validation · 硬件验证 (3 collections)\n\n- [hkustgz-zhang-lab\u002FHW-Formal-Paper](https:\u002F\u002Fgithub.com\u002Fhkustgz-zhang-lab\u002FHW-Formal-Paper) — Recent papers related to hardware formal verification. _★ 78_\n- [rpjayaraman\u002FDV-resource](https:\u002F\u002Fgithub.com\u002Frpjayaraman\u002FDV-resource) — A repository aggregating links to essential documentation, tutorials, and research papers for hardware Design Verification. _★ 60_\n- [qhy991\u002FAwesome-LLM-Circuit-Agent](https:\u002F\u002Fgithub.com\u002Fqhy991\u002FAwesome-LLM-Circuit-Agent) — A repository for academic works on RTL generation and Analog circuit generation based on LLM. _★ 21_\n\n\u003Ca id=\"ccs-02-08\">\u003C\u002Fa>\n### 3.8 Hardware test · 硬件测试 (3 collections)\n\n- [Thinklab-SJTU\u002FAwesome-LLM4EDA](https:\u002F\u002Fgithub.com\u002FThinklab-SJTU\u002FAwesome-LLM4EDA) — Curated paper and reading-list repository. _★ 292_\n- [forestfoxx\u002Fawesome-hardware-fuzzing](https:\u002F\u002Fgithub.com\u002Fforestfoxx\u002Fawesome-hardware-fuzzing) — A curated list of research and repositories on the novel technique of hardware fuzzing. _★ 27_\n- [MrWater98\u002FAwesome-EDA-Testing-Paper](https:\u002F\u002Fgithub.com\u002FMrWater98\u002FAwesome-EDA-Testing-Paper) — Curated paper and reading-list repository. _★ 0_\n\n\u003Ca id=\"ccs-02-09\">\u003C\u002Fa>\n### 3.9 Robustness · 硬件鲁棒性 (3 collections)\n\n- [DfX-NYUAD\u002FGNN4IC](https:\u002F\u002Fgithub.com\u002FDfX-NYUAD\u002FGNN4IC) — Must-read papers on Graph Neural Networks (GNNs) for Integrated Circuits (ICs) design, security and reliability. _★ 93_\n- [hkustgz-zhang-lab\u002FHW-Formal-Paper](https:\u002F\u002Fgithub.com\u002Fhkustgz-zhang-lab\u002FHW-Formal-Paper) — Recent papers related to hardware formal verification. _★ 78_\n- [forestfoxx\u002Fawesome-hardware-fuzzing](https:\u002F\u002Fgithub.com\u002Fforestfoxx\u002Fawesome-hardware-fuzzing) — A curated list of research and repositories on the novel technique of hardware fuzzing. _★ 27_\n\n\u003Ca id=\"ccs-02-10\">\u003C\u002Fa>\n### 3.10 Emerging technologies · 新兴硬件技术 (4 collections)\n\n- [TheBrainLab\u002FAwesome-Spiking-Neural-Networks](https:\u002F\u002Fgithub.com\u002FTheBrainLab\u002FAwesome-Spiking-Neural-Networks) — A paper list of spiking neural networks, including papers, codes, and related websites. 本仓库收集脉冲神经网络相关的顶会顶刊以及CNS论文和代码，正在持续更新中。 _★ 805_\n- [open-neuromorphic\u002Fawesome-neuromorphic-hw](https:\u002F\u002Fgithub.com\u002Fopen-neuromorphic\u002Fawesome-neuromorphic-hw) — Repository collecting papers about neuromorphic hardware, such as ASIC and FPGA implementations of SNNs and stuff. _★ 214_\n- [witmemtech\u002FCIM-Technical-Papers-Collection](https:\u002F\u002Fgithub.com\u002Fwitmemtech\u002FCIM-Technical-Papers-Collection) — Computing in memory optimizes data handling by performing operations directly in memory, ideal for high-speed data processing needs. _★ 36_\n- [Linger0\u002FSRAM-Compute-In-Memory](https:\u002F\u002Fgithub.com\u002FLinger0\u002FSRAM-Compute-In-Memory) — A collection of research papers on SRAM-based compute-in-memory architectures. _★ 32_\n\n\u003Cp align=\"right\">\u003Ca href=\"#readme-top\">Back to top ↑\u003C\u002Fa>\u003C\u002Fp>\n\n\u003Ca id=\"ccs-03\">\u003C\u002Fa>\n## 4. Computer systems organization · 计算机系统组织\n\n**Topics:** [Architectures · 体系结构](#ccs-03-01) · [Embedded and cyber-physical systems · 嵌入式与信息物理系统](#ccs-03-02) · [Real-time systems · 实时系统](#ccs-03-03) · [Dependable and fault-tolerant systems and networks · 可信与容错系统和网络](#ccs-03-04)\n\n\u003Ca id=\"ccs-03-01\">\u003C\u002Fa>\n### 4.1 Architectures · 体系结构 (5 collections)\n\n- [fengbintu\u002FNeural-Networks-on-Silicon](https:\u002F\u002Fgithub.com\u002Ffengbintu\u002FNeural-Networks-on-Silicon) — This is originally a collection of papers on neural network accelerators. _★ 2,100_\n- [Zhen-Dong\u002FAwesome-Quantization-Papers](https:\u002F\u002Fgithub.com\u002FZhen-Dong\u002FAwesome-Quantization-Papers) — List of papers related to neural network quantization in recent AI conferences and journals. _★ 834_\n- [byungsoo-oh\u002Fml-systems-papers](https:\u002F\u002Fgithub.com\u002Fbyungsoo-oh\u002Fml-systems-papers) — Curated collection of papers in machine learning systems. _★ 631_\n- [LonghornSilicon\u002Fawesome-ai-accelerators](https:\u002F\u002Fgithub.com\u002FLonghornSilicon\u002Fawesome-ai-accelerators) — A curated list of AI accelerator papers, resources, tools, and open-source projects. _★ 124_\n- [MPSLab-ASU\u002FML-Accelerators](https:\u002F\u002Fgithub.com\u002FMPSLab-ASU\u002FML-Accelerators) — Topics in Machine Learning Accelerator Design. _★ 105_\n\n\u003Ca id=\"ccs-03-02\">\u003C\u002Fa>\n### 4.2 Embedded and cyber-physical systems · 嵌入式与信息物理系统 (3 collections)\n\n- [gigwegbe\u002Ftinyml-papers-and-projects](https:\u002F\u002Fgithub.com\u002Fgigwegbe\u002Ftinyml-papers-and-projects) — This is a list of interesting papers and projects about TinyML. _★ 1,027_\n- [csarron\u002Fawesome-emdl](https:\u002F\u002Fgithub.com\u002Fcsarron\u002Fawesome-emdl) — Embedded and mobile deep learning research resources. _★ 769_\n- [AI-in-Transportation-Lab\u002Fawesome-tinyml](https:\u002F\u002Fgithub.com\u002FAI-in-Transportation-Lab\u002Fawesome-tinyml) — A carefully curated collection of high-quality libraries, projects, tutorials, research papers, and other essential resources focused on TinyML — the… _★ 132_\n\n\u003Ca id=\"ccs-03-03\">\u003C\u002Fa>\n### 4.3 Real-time systems · 实时系统 (3 collections)\n\n- [sotayang\u002FAwesome-Streaming-Video-Understanding](https:\u002F\u002Fgithub.com\u002Fsotayang\u002FAwesome-Streaming-Video-Understanding) — [Awesome] Latest Papers, Codes & Datasets on Streaming \u002F Online Video Understanding — Building Always-on, Real-time Video AI. _★ 408_\n- [S-Lab-System-Group\u002FAwesome-DL-Scheduling-Papers](https:\u002F\u002Fgithub.com\u002FS-Lab-System-Group\u002FAwesome-DL-Scheduling-Papers) — Curated paper and reading-list repository. _★ 332_\n- [Kyrie-Zhao\u002Fawesome-real-time-AI](https:\u002F\u002Fgithub.com\u002FKyrie-Zhao\u002Fawesome-real-time-AI) — This is a list of awesome edgeAI inference related papers. _★ 98_\n\n\u003Ca id=\"ccs-03-04\">\u003C\u002Fa>\n### 4.4 Dependable and fault-tolerant systems and networks · 可信与容错系统和网络 (3 collections)\n\n- [lorin\u002Fresilience-engineering](https:\u002F\u002Fgithub.com\u002Florin\u002Fresilience-engineering) — Resilience engineering papers. _★ 3,054_\n- [heidihoward\u002Fdistributed-consensus-reading-list](https:\u002F\u002Fgithub.com\u002Fheidihoward\u002Fdistributed-consensus-reading-list) — A list of papers about distributed consensus. _★ 2,641_\n- [rupc\u002Fawesome-bft](https:\u002F\u002Fgithub.com\u002Frupc\u002Fawesome-bft) — Awesome Byzantine Fault Tolerance (BFT). _★ 69_\n\n\u003Cp align=\"right\">\u003Ca href=\"#readme-top\">Back to top ↑\u003C\u002Fa>\u003C\u002Fp>\n\n\u003Ca id=\"ccs-04\">\u003C\u002Fa>\n## 5. Networks · 网络\n\n**Topics:** [Network architectures · 网络体系结构](#ccs-04-01) · [Network protocols · 网络协议](#ccs-04-02) · [Network components · 网络组件](#ccs-04-03) · [Network algorithms · 网络算法](#ccs-04-04) · [Network performance evaluation · 网络性能评估](#ccs-04-05) · [Network properties · 网络属性](#ccs-04-06) · [Network services · 网络服务](#ccs-04-07) · [Network types · 网络类型](#ccs-04-08)\n\n\u003Ca id=\"ccs-04-01\">\u003C\u002Fa>\n### 5.1 Network architectures · 网络体系结构 (3 collections)\n\n- [sdnds-tw\u002Fawesome-sdn](https:\u002F\u002Fgithub.com\u002Fsdnds-tw\u002Fawesome-sdn) — A awesome list about Software Defined Network (SDN). _★ 1,645_\n- [rg0now\u002Fprog_dataplane_reading_list](https:\u002F\u002Fgithub.com\u002Frg0now\u002Fprog_dataplane_reading_list) — The Programmable Data Plane: Reading List. _★ 52_\n- [snlab-freedom\u002Fawesome-sdn](https:\u002F\u002Fgithub.com\u002Fsnlab-freedom\u002Fawesome-sdn) — An awesome list of papers, projects and communities about SDN. _★ 52_\n\n\u003Ca id=\"ccs-04-02\">\u003C\u002Fa>\n### 5.2 Network protocols · 网络协议 (3 collections)\n\n- [Romero027\u002Fsysnet-reading-list](https:\u002F\u002Fgithub.com\u002FRomero027\u002Fsysnet-reading-list) — This repository contains a list of papers on various topics (that I am working\u002Fworked on) in the system and networking area. _★ 88_\n- [dbarrosop\u002Fawesome-networking-papers-presos](https:\u002F\u002Fgithub.com\u002Fdbarrosop\u002Fawesome-networking-papers-presos) — Awesome papers and presentations related to networking. _★ 32_\n- [andreia-oca\u002Fawesome-network-protocol-fuzzing](https:\u002F\u002Fgithub.com\u002Fandreia-oca\u002Fawesome-network-protocol-fuzzing) — A list of curated papers focusing on Network Protocol Fuzzing. _★ 14_\n\n\u003Ca id=\"ccs-04-03\">\u003C\u002Fa>\n### 5.3 Network components · 网络组件 (3 collections)\n\n- [sdnds-tw\u002Fawesome-sdn](https:\u002F\u002Fgithub.com\u002Fsdnds-tw\u002Fawesome-sdn) — A awesome list about Software Defined Network (SDN). _★ 1,645_\n- [rg0now\u002Fprog_dataplane_reading_list](https:\u002F\u002Fgithub.com\u002Frg0now\u002Fprog_dataplane_reading_list) — The Programmable Data Plane: Reading List. _★ 52_\n- [dbarrosop\u002Fawesome-networking-papers-presos](https:\u002F\u002Fgithub.com\u002Fdbarrosop\u002Fawesome-networking-papers-presos) — Awesome papers and presentations related to networking. _★ 32_\n\n\u003Ca id=\"ccs-04-04\">\u003C\u002Fa>\n### 5.4 Network algorithms · 网络算法 (4 collections)\n\n- [ML4Comm-Netw\u002FPaper-with-Code-of-Wireless-communication-Based-on-DL](https:\u002F\u002Fgithub.com\u002FML4Comm-Netw\u002FPaper-with-Code-of-Wireless-communication-Based-on-DL) — 无线与深度学习结合的论文代码整理\u002FPaper-with-Code-of-Wireless-communication-Based-on-DL。 _★ 2,256_\n- [jwwthu\u002FCommSurvey](https:\u002F\u002Fgithub.com\u002Fjwwthu\u002FCommSurvey) — This is the repository for the collection of surveys and reviews in communication and networking domains. _★ 174_\n- [GeminiLight\u002Fsdn-nfv-papers](https:\u002F\u002Fgithub.com\u002FGeminiLight\u002Fsdn-nfv-papers) — This is a paper list about Resource Allocation in Network Functions Virtualization (NFV) and Software-Defined Networking (SDN). _★ 148_\n- [Romero027\u002Fsysnet-reading-list](https:\u002F\u002Fgithub.com\u002FRomero027\u002Fsysnet-reading-list) — This repository contains a list of papers on various topics (that I am working\u002Fworked on) in the system and networking area. _★ 88_\n\n\u003Ca id=\"ccs-04-05\">\u003C\u002Fa>\n### 5.5 Network performance evaluation · 网络性能评估 (3 collections)\n\n- [GeminiLight\u002Fsdn-nfv-papers](https:\u002F\u002Fgithub.com\u002FGeminiLight\u002Fsdn-nfv-papers) — This is a paper list about Resource Allocation in Network Functions Virtualization (NFV) and Software-Defined Networking (SDN). _★ 148_\n- [Romero027\u002Fsysnet-reading-list](https:\u002F\u002Fgithub.com\u002FRomero027\u002Fsysnet-reading-list) — This repository contains a list of papers on various topics (that I am working\u002Fworked on) in the system and networking area. _★ 88_\n- [dbarrosop\u002Fawesome-networking-papers-presos](https:\u002F\u002Fgithub.com\u002Fdbarrosop\u002Fawesome-networking-papers-presos) — Awesome papers and presentations related to networking. _★ 32_\n\n\u003Ca id=\"ccs-04-06\">\u003C\u002Fa>\n### 5.6 Network properties · 网络属性 (3 collections)\n\n- [jwwthu\u002FCommSurvey](https:\u002F\u002Fgithub.com\u002Fjwwthu\u002FCommSurvey) — This is the repository for the collection of surveys and reviews in communication and networking domains. _★ 174_\n- [Romero027\u002Fsysnet-reading-list](https:\u002F\u002Fgithub.com\u002FRomero027\u002Fsysnet-reading-list) — This repository contains a list of papers on various topics (that I am working\u002Fworked on) in the system and networking area. _★ 88_\n- [JiahangOK\u002FNMA_Research](https:\u002F\u002Fgithub.com\u002FJiahangOK\u002FNMA_Research) — This repository is used to collect some papers and codes about NMA(Network Measurement and Analysis). _★ 1_\n\n\u003Ca id=\"ccs-04-07\">\u003C\u002Fa>\n### 5.7 Network services · 网络服务 (3 collections)\n\n- [dmemsys\u002Fawesome-disaggregated-memory](https:\u002F\u002Fgithub.com\u002Fdmemsys\u002Fawesome-disaggregated-memory) — A collection of awesome researchers and papers about disaggregated memory. _★ 192_\n- [GeminiLight\u002Fsdn-nfv-papers](https:\u002F\u002Fgithub.com\u002FGeminiLight\u002Fsdn-nfv-papers) — This is a paper list about Resource Allocation in Network Functions Virtualization (NFV) and Software-Defined Networking (SDN). _★ 148_\n- [IntelligentDDS\u002Fawesome-papers](https:\u002F\u002Fgithub.com\u002FIntelligentDDS\u002Fawesome-papers) — Awesome-papers is a collection of awesome papers about cloud computing including resource management, serverless, microservice, observerbility and so on. _★ 126_\n\n\u003Ca id=\"ccs-04-08\">\u003C\u002Fa>\n### 5.8 Network types · 网络类型 (3 collections)\n\n- [NTUMARS\u002FAwesome-WiFi-CSI-Sensing](https:\u002F\u002Fgithub.com\u002FNTUMARS\u002FAwesome-WiFi-CSI-Sensing) — A list of awesome papers and cool resources on WiFi CSI sensing. _★ 1,051_\n- [jwwthu\u002FCommSurvey](https:\u002F\u002Fgithub.com\u002Fjwwthu\u002FCommSurvey) — This is the repository for the collection of surveys and reviews in communication and networking domains. _★ 174_\n- [Beerkay\u002FIoTResearch](https:\u002F\u002Fgithub.com\u002FBeerkay\u002FIoTResearch) — IoT Reading List (IoT research papers from 2016 to 2019). _★ 85_\n\n\u003Cp align=\"right\">\u003Ca href=\"#readme-top\">Back to top ↑\u003C\u002Fa>\u003C\u002Fp>\n\n\u003Ca id=\"ccs-05\">\u003C\u002Fa>\n## 6. Software and its engineering · 软件及其工程\n\n**Topics:** [Software organization and properties · 软件组织与属性](#ccs-05-01) · [Software notations and tools · 软件表示与工具](#ccs-05-02) · [Software creation and management · 软件创建与管理](#ccs-05-03)\n\n\u003Ca id=\"ccs-05-01\">\u003C\u002Fa>\n### 6.1 Software organization and properties · 软件组织与属性 (3 collections)\n\n- [binhnguyennus\u002Fawesome-scalability](https:\u002F\u002Fgithub.com\u002Fbinhnguyennus\u002Fawesome-scalability) — The Patterns of Scalable, Reliable, and Performant Large-Scale Systems. _★ 72,448_\n- [facundoolano\u002Fsoftware-papers](https:\u002F\u002Fgithub.com\u002Ffacundoolano\u002Fsoftware-papers) — A curated list of papers for Software Engineers. _★ 6,547_\n- [Developer-Y\u002FScalable-Software-Architecture](https:\u002F\u002Fgithub.com\u002FDeveloper-Y\u002FScalable-Software-Architecture) — Collection of tech talks, papers and web links on Distributed Systems, Scalability and System Design. _★ 2,253_\n\n\u003Ca id=\"ccs-05-02\">\u003C\u002Fa>\n### 6.2 Software notations and tools · 软件表示与工具 (5 collections)\n\n- [steshaw\u002Fplt](https:\u002F\u002Fgithub.com\u002Fsteshaw\u002Fplt) — Programming Language Theory λΠ. _★ 5,363_\n- [merrymercy\u002Fawesome-tensor-compilers](https:\u002F\u002Fgithub.com\u002Fmerrymercy\u002Fawesome-tensor-compilers) — A list of awesome compiler projects and papers for tensor computation and deep learning. _★ 2,764_\n- [zwang4\u002Fawesome-machine-learning-in-compilers](https:\u002F\u002Fgithub.com\u002Fzwang4\u002Fawesome-machine-learning-in-compilers) — Must read research papers and links to tools and datasets that are related to using machine learning for compilers and systems optimisation. _★ 1,680_\n- [nuprl\u002F10PL](https:\u002F\u002Fgithub.com\u002Fnuprl\u002F10PL) — 10 papers that all PhD students in programming languages ought to know, for some value of 10. _★ 943_\n- [FedericoBruzzone\u002Fpapers-on-compiler-optimizations](https:\u002F\u002Fgithub.com\u002FFedericoBruzzone\u002Fpapers-on-compiler-optimizations) — A chronologically sorted list of influential papers on compiler optimization, from the seminal works of 1952 through the advanced techniques of 1994. _★ 81_\n\n\u003Ca id=\"ccs-05-03\">\u003C\u002Fa>\n### 6.3 Software creation and management · 软件创建与管理 (5 collections)\n\n- [facundoolano\u002Fsoftware-papers](https:\u002F\u002Fgithub.com\u002Ffacundoolano\u002Fsoftware-papers) — A curated list of papers for Software Engineers. _★ 6,547_\n- [saltudelft\u002Fml4se](https:\u002F\u002Fgithub.com\u002Fsaltudelft\u002Fml4se) — A curated list of papers, theses, datasets, and tools related to the application of Machine Learning for Software Engineering. _★ 732_\n- [PurCL\u002FASE](https:\u002F\u002Fgithub.com\u002FPurCL\u002FASE) — A continuously updated collection of papers on agentic SE. _★ 630_\n- [iSEngLab\u002FAwesomeLLM4SE](https:\u002F\u002Fgithub.com\u002FiSEngLab\u002FAwesomeLLM4SE) — [SCIS 2025] A Survey on Large Language Models for Software Engineering. _★ 334_\n- [manjunath5496\u002FSoftware-Engineering-Papers](https:\u002F\u002Fgithub.com\u002Fmanjunath5496\u002FSoftware-Engineering-Papers) — I got more interested in the calculator than I did in the problem.\" ― Fernando Corbató. _★ 7_\n\n\u003Cp align=\"right\">\u003Ca href=\"#readme-top\">Back to top ↑\u003C\u002Fa>\u003C\u002Fp>\n\n\u003Ca id=\"ccs-06\">\u003C\u002Fa>\n## 7. Theory of computation · 计算理论\n\n**Topics:** [Models of computation · 计算模型](#ccs-06-01) · [Formal languages and automata theory · 形式语言与自动机理论](#ccs-06-02) · [Computational complexity and cryptography · 计算复杂性与密码学](#ccs-06-03) · [Logic · 逻辑](#ccs-06-04) · [Design and analysis of algorithms · 算法设计与分析](#ccs-06-05) · [Randomness, geometry and discrete structures · 随机性、几何与离散结构](#ccs-06-06) · [Theory and algorithms for application domains · 应用领域的理论与算法](#ccs-06-07) · [Semantics and reasoning · 语义与推理](#ccs-06-08)\n\n\u003Ca id=\"ccs-06-01\">\u003C\u002Fa>\n### 7.1 Models of computation · 计算模型 (3 collections)\n\n- [mostafatouny\u002Fawesome-theoretical-computer-science](https:\u002F\u002Fgithub.com\u002Fmostafatouny\u002Fawesome-theoretical-computer-science) — Math & CS awesome List, distinguished by proof and logic technique. _★ 1,177_\n- [grageragarces\u002FQuantum-tech-papers](https:\u002F\u002Fgithub.com\u002Fgrageragarces\u002FQuantum-tech-papers) — My personal quantum paper library. _★ 236_\n- [dwoiwode\u002Fawesome-neural-cellular-automata](https:\u002F\u002Fgithub.com\u002Fdwoiwode\u002Fawesome-neural-cellular-automata) — A list of paper and resources regarding Neural Cellular Automata. _★ 25_\n\n\u003Ca id=\"ccs-06-02\">\u003C\u002Fa>\n### 7.2 Formal languages and automata theory · 形式语言与自动机理论 (3 collections)\n\n- [papers-we-love\u002Fpapers-we-love](https:\u002F\u002Fgithub.com\u002Fpapers-we-love\u002Fpapers-we-love) — Papers from the computer science community to read and discuss. _★ 107,875_\n- [mostafatouny\u002Fawesome-theoretical-computer-science](https:\u002F\u002Fgithub.com\u002Fmostafatouny\u002Fawesome-theoretical-computer-science) — Math & CS awesome List, distinguished by proof and logic technique. _★ 1,177_\n- [dwoiwode\u002Fawesome-neural-cellular-automata](https:\u002F\u002Fgithub.com\u002Fdwoiwode\u002Fawesome-neural-cellular-automata) — A list of paper and resources regarding Neural Cellular Automata. _★ 25_\n\n\u003Ca id=\"ccs-06-03\">\u003C\u002Fa>\n### 7.3 Computational complexity and cryptography · 计算复杂性与密码学 (3 collections)\n\n- [papers-we-love\u002Fpapers-we-love](https:\u002F\u002Fgithub.com\u002Fpapers-we-love\u002Fpapers-we-love) — Papers from the computer science community to read and discuss. _★ 107,875_\n- [pFarb\u002Fawesome-crypto-papers](https:\u002F\u002Fgithub.com\u002FpFarb\u002Fawesome-crypto-papers) — A curated list of cryptography papers, articles, tutorials and howtos. _★ 2,076_\n- [mostafatouny\u002Fawesome-theoretical-computer-science](https:\u002F\u002Fgithub.com\u002Fmostafatouny\u002Fawesome-theoretical-computer-science) — Math & CS awesome List, distinguished by proof and logic technique. _★ 1,177_\n\n\u003Ca id=\"ccs-06-04\">\u003C\u002Fa>\n### 7.4 Logic · 逻辑 (3 collections)\n\n- [papers-we-love\u002Fpapers-we-love](https:\u002F\u002Fgithub.com\u002Fpapers-we-love\u002Fpapers-we-love) — Papers from the computer science community to read and discuss. _★ 107,875_\n- [mostafatouny\u002Fawesome-theoretical-computer-science](https:\u002F\u002Fgithub.com\u002Fmostafatouny\u002Fawesome-theoretical-computer-science) — Math & CS awesome List, distinguished by proof and logic technique. _★ 1,177_\n- [thuwzy\u002FNeural-Symbolic-and-Probabilistic-Logic-Papers](https:\u002F\u002Fgithub.com\u002Fthuwzy\u002FNeural-Symbolic-and-Probabilistic-Logic-Papers) — A curated paper list on neural symbolic and probabilistic logic. _★ 137_\n\n\u003Ca id=\"ccs-06-05\">\u003C\u002Fa>\n### 7.5 Design and analysis of algorithms · 算法设计与分析 (3 collections)\n\n- [papers-we-love\u002Fpapers-we-love](https:\u002F\u002Fgithub.com\u002Fpapers-we-love\u002Fpapers-we-love) — Papers from the computer science community to read and discuss. _★ 107,875_\n- [facundoolano\u002Fsoftware-papers](https:\u002F\u002Fgithub.com\u002Ffacundoolano\u002Fsoftware-papers) — A curated list of papers for Software Engineers. _★ 6,547_\n- [Thinklab-SJTU\u002Fawesome-ml4co](https:\u002F\u002Fgithub.com\u002FThinklab-SJTU\u002Fawesome-ml4co) — Awesome machine learning for combinatorial optimization papers. _★ 2,141_\n\n\u003Ca id=\"ccs-06-06\">\u003C\u002Fa>\n### 7.6 Randomness, geometry and discrete structures · 随机性、几何与离散结构 (3 collections)\n\n- [briatte\u002Fawesome-network-analysis](https:\u002F\u002Fgithub.com\u002Fbriatte\u002Fawesome-network-analysis) — A curated list of awesome network analysis resources. _★ 4,078_\n- [mostafatouny\u002Fawesome-theoretical-computer-science](https:\u002F\u002Fgithub.com\u002Fmostafatouny\u002Fawesome-theoretical-computer-science) — Math & CS awesome List, distinguished by proof and logic technique. _★ 1,177_\n- [theAlgorithmist\u002FCompGeometryWhitePapers](https:\u002F\u002Fgithub.com\u002FtheAlgorithmist\u002FCompGeometryWhitePapers) — Historical archive of white papers on computational geometry. _★ 1_\n\n\u003Ca id=\"ccs-06-07\">\u003C\u002Fa>\n### 7.7 Theory and algorithms for application domains · 应用领域的理论与算法 (3 collections)\n\n- [Thinklab-SJTU\u002Fawesome-ml4co](https:\u002F\u002Fgithub.com\u002FThinklab-SJTU\u002Fawesome-ml4co) — Awesome machine learning for combinatorial optimization papers. _★ 2,141_\n- [ai4co\u002Fawesome-fm4co](https:\u002F\u002Fgithub.com\u002Fai4co\u002Fawesome-fm4co) — Recent research papers about Foundation Models for Combinatorial Optimization. _★ 569_\n- [shaohua0116\u002Fawesome-program](https:\u002F\u002Fgithub.com\u002Fshaohua0116\u002Fawesome-program) — A curated list of papers related to program synthesis, program induction, program execution, program and code repair, and programmatic reinforcement learning. _★ 169_\n\n\u003Ca id=\"ccs-06-08\">\u003C\u002Fa>\n### 7.8 Semantics and reasoning · 语义与推理 (3 collections)\n\n- [papers-we-love\u002Fpapers-we-love](https:\u002F\u002Fgithub.com\u002Fpapers-we-love\u002Fpapers-we-love) — Papers from the computer science community to read and discuss. _★ 107,875_\n- [mostafatouny\u002Fawesome-theoretical-computer-science](https:\u002F\u002Fgithub.com\u002Fmostafatouny\u002Fawesome-theoretical-computer-science) — Math & CS awesome List, distinguished by proof and logic technique. _★ 1,177_\n- [madnight\u002Fawesome-category-theory](https:\u002F\u002Fgithub.com\u002Fmadnight\u002Fawesome-category-theory) — A curated list of awesome Category Theory resources. _★ 146_\n\n\u003Cp align=\"right\">\u003Ca href=\"#readme-top\">Back to top ↑\u003C\u002Fa>\u003C\u002Fp>\n\n\u003Ca id=\"ccs-07\">\u003C\u002Fa>\n## 8. Mathematics of computing · 计算数学\n\n**Topics:** [Discrete mathematics · 离散数学](#ccs-07-01) · [Probability and statistics · 概率与统计](#ccs-07-02) · [Mathematical software · 数学软件](#ccs-07-03) · [Information theory · 信息论](#ccs-07-04) · [Mathematical analysis · 数学分析](#ccs-07-05) · [Continuous mathematics · 连续数学](#ccs-07-06)\n\n\u003Ca id=\"ccs-07-01\">\u003C\u002Fa>\n### 8.1 Discrete mathematics · 离散数学 (3 collections)\n\n- [papers-we-love\u002Fpapers-we-love](https:\u002F\u002Fgithub.com\u002Fpapers-we-love\u002Fpapers-we-love) — Papers from the computer science community to read and discuss. _★ 107,875_\n- [briatte\u002Fawesome-network-analysis](https:\u002F\u002Fgithub.com\u002Fbriatte\u002Fawesome-network-analysis) — A curated list of awesome network analysis resources. _★ 4,078_\n- [Thinklab-SJTU\u002Fawesome-ml4co](https:\u002F\u002Fgithub.com\u002FThinklab-SJTU\u002Fawesome-ml4co) — Awesome machine learning for combinatorial optimization papers. _★ 2,141_\n\n\u003Ca id=\"ccs-07-02\">\u003C\u002Fa>\n### 8.2 Probability and statistics · 概率与统计 (5 collections)\n\n- [ddz16\u002FTSFpaper](https:\u002F\u002Fgithub.com\u002Fddz16\u002FTSFpaper) — This repository contains a reading list of papers on Time Series Forecasting\u002FPrediction (TSF) and Spatio-Temporal Forecasting\u002FPrediction (STF). _★ 3,178_\n- [matteocourthoud\u002Fawesome-causal-inference](https:\u002F\u002Fgithub.com\u002Fmatteocourthoud\u002Fawesome-causal-inference) — A curated list of causal inference libraries, resources, and applications. _★ 1,179_\n- [ENSTA-U2IS-AI\u002Fawesome-uncertainty-deeplearning](https:\u002F\u002Fgithub.com\u002FENSTA-U2IS-AI\u002Fawesome-uncertainty-deeplearning) — This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models. _★ 821_\n- [bighuang624\u002FTime-Series-Papers](https:\u002F\u002Fgithub.com\u002Fbighuang624\u002FTime-Series-Papers) — List of awesome papers about time series, mainly including algorithms based on machine learning \\| 收录时间序列分析中各个研究领域的高水平文章，主要包含基于机器学习的算法。 _★ 491_\n- [materials-data-facility\u002Fawesome-bayesian-optimization](https:\u002F\u002Fgithub.com\u002Fmaterials-data-facility\u002Fawesome-bayesian-optimization) — Curated paper and reading-list repository. _★ 54_\n\n\u003Ca id=\"ccs-07-03\">\u003C\u002Fa>\n### 8.3 Mathematical software · 数学软件 (3 collections)\n\n- [zhaoyu-li\u002FDL4TP](https:\u002F\u002Fgithub.com\u002Fzhaoyu-li\u002FDL4TP) — [COLM 2024] A Survey on Deep Learning for Theorem Proving. _★ 226_\n- [seewoo5\u002Fawesome-ai-for-math](https:\u002F\u002Fgithub.com\u002Fseewoo5\u002Fawesome-ai-for-math) — List of awesome works that use AI for mathematical discoveries. _★ 70_\n- [fpvandoorn\u002Flean-links](https:\u002F\u002Fgithub.com\u002Ffpvandoorn\u002Flean-links) — Links to recourses for the Lean Theorem Prover. _★ 13_\n\n\u003Ca id=\"ccs-07-04\">\u003C\u002Fa>\n### 8.4 Information theory · 信息论 (3 collections)\n\n- [papers-we-love\u002Fpapers-we-love](https:\u002F\u002Fgithub.com\u002Fpapers-we-love\u002Fpapers-we-love) — Papers from the computer science community to read and discuss. _★ 107,875_\n- [ZIYU-DEEP\u002FAwesome-Information-Bottleneck](https:\u002F\u002Fgithub.com\u002FZIYU-DEEP\u002FAwesome-Information-Bottleneck) — This is a curated list for Information Bottleneck Principle, in memory of Professor Naftali Tishby. _★ 400_\n- [ShizheHu\u002FTPAMI24_Awesome-Information-Bottleneck](https:\u002F\u002Fgithub.com\u002FShizheHu\u002FTPAMI24_Awesome-Information-Bottleneck) — Papers, codes, datasets, researchers on information bottleneck. _★ 65_\n\n\u003Ca id=\"ccs-07-05\">\u003C\u002Fa>\n### 8.5 Mathematical analysis · 数学分析 (3 collections)\n\n- [rossant\u002Fawesome-math](https:\u002F\u002Fgithub.com\u002Frossant\u002Fawesome-math) — A curated list of awesome mathematics resources. _★ 15,892_\n- [idrl-lab\u002FPINNpapers](https:\u002F\u002Fgithub.com\u002Fidrl-lab\u002FPINNpapers) — Must-read Papers on Physics-Informed Neural Networks. _★ 1,522_\n- [Event-AHU\u002FPINN_Paper_List](https:\u002F\u002Fgithub.com\u002FEvent-AHU\u002FPINN_Paper_List) — Paper List of Physics-Informed Neural Network (PINN). _★ 87_\n\n\u003Ca id=\"ccs-07-06\">\u003C\u002Fa>\n### 8.6 Continuous mathematics · 连续数学 (3 collections)\n\n- [idrl-lab\u002FPINNpapers](https:\u002F\u002Fgithub.com\u002Fidrl-lab\u002FPINNpapers) — Must-read Papers on Physics-Informed Neural Networks. _★ 1,522_\n- [ikespand\u002Fawesome-machine-learning-fluid-mechanics](https:\u002F\u002Fgithub.com\u002Fikespand\u002Fawesome-machine-learning-fluid-mechanics) — Curated list for ML in FM. _★ 246_\n- [Emory-Melody\u002FAwesome-Graph-Neural-Differential-Equations](https:\u002F\u002Fgithub.com\u002FEmory-Melody\u002FAwesome-Graph-Neural-Differential-Equations) — Paper list on GNNs + Differential Equations (ODE, PDE, SDE). _★ 64_\n\n\u003Cp align=\"right\">\u003Ca href=\"#readme-top\">Back to top ↑\u003C\u002Fa>\u003C\u002Fp>\n\n\u003Ca id=\"ccs-08\">\u003C\u002Fa>\n## 9. Information systems · 信息系统\n\n**Topics:** [Data management systems · 数据管理系统](#ccs-08-01) · [Information storage systems · 信息存储系统](#ccs-08-02) · [Information systems applications · 信息系统应用](#ccs-08-03) · [World Wide Web · 万维网](#ccs-08-04) · [Information retrieval · 信息检索](#ccs-08-05)\n\n\u003Ca id=\"ccs-08-01\">\u003C\u002Fa>\n### 9.1 Data management systems · 数据管理系统 (7 collections)\n\n- [pingcap\u002Fawesome-database-learning](https:\u002F\u002Fgithub.com\u002Fpingcap\u002Fawesome-database-learning) — A list of learning materials to understand databases internals. _★ 10,926_\n- [rxin\u002Fdb-readings](https:\u002F\u002Fgithub.com\u002Frxin\u002Fdb-readings) — Readings in Databases. _★ 8,126_\n- [ept\u002Fddia-references](https:\u002F\u002Fgithub.com\u002Fept\u002Fddia-references) — Literature references for “Designing Data-Intensive Applications. _★ 7,170_\n- [DEEP-PolyU\u002FAwesome-LLM-based-Text2SQL](https:\u002F\u002Fgithub.com\u002FDEEP-PolyU\u002FAwesome-LLM-based-Text2SQL) — [TKDE2025] Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL \\| A curated list of resources (surveys, papers, benchmarks, and opensource… _★ 1,344_\n- [OpenDataBox\u002Fawesome-data-llm](https:\u002F\u002Fgithub.com\u002FOpenDataBox\u002Fawesome-data-llm) — Official Repository of \"LLM × DATA\" Survey Paper. _★ 803_\n- [LumingSun\u002FML4DB-paper-list](https:\u002F\u002Fgithub.com\u002FLumingSun\u002FML4DB-paper-list) — Papers for database systems powered by artificial intelligence (machine learning for database). _★ 777_\n- [Wind-Gone\u002Fawesome-olap-paper](https:\u002F\u002Fgithub.com\u002FWind-Gone\u002Fawesome-olap-paper) — Paper related to OLAP database systems. _★ 127_\n\n\u003Ca id=\"ccs-08-02\">\u003C\u002Fa>\n### 9.2 Information storage systems · 信息存储系统 (4 collections)\n\n- [papers-we-love\u002Fpapers-we-love](https:\u002F\u002Fgithub.com\u002Fpapers-we-love\u002Fpapers-we-love) — Papers from the computer science community to read and discuss. _★ 107,875_\n- [yingjunwu\u002FDBMS-Indexology](https:\u002F\u002Fgithub.com\u002Fyingjunwu\u002FDBMS-Indexology) — A Collection of Papers on Database Index Structures. _★ 491_\n- [matchyc\u002Fvector-search-papers](https:\u002F\u002Fgithub.com\u002Fmatchyc\u002Fvector-search-papers) — Awesome papers and technical blogs on vector DB (database), semantic-based vector search or approximate nearest neighbor search (ANN Search, ANNS). _★ 117_\n- [yzr95924\u002Fawesome_storage_papers](https:\u002F\u002Fgithub.com\u002Fyzr95924\u002Fawesome_storage_papers) — Some paper lists related to storage systems. _★ 51_\n\n\u003Ca id=\"ccs-08-03\">\u003C\u002Fa>\n### 9.3 Information systems applications · 信息系统应用 (5 collections)\n\n- [briatte\u002Fawesome-network-analysis](https:\u002F\u002Fgithub.com\u002Fbriatte\u002Fawesome-network-analysis) — A curated list of awesome network analysis resources. _★ 4,078_\n- [safe-graph\u002Fgraph-fraud-detection-papers](https:\u002F\u002Fgithub.com\u002Fsafe-graph\u002Fgraph-fraud-detection-papers) — A curated list of Graph\u002FTransformer-based fraud, anomaly, and outlier detection papers & resources. _★ 1,869_\n- [benedekrozemberczki\u002Fawesome-fraud-detection-papers](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-fraud-detection-papers) — A curated list of data mining papers about fraud detection. _★ 1,814_\n- [mathisonian\u002Fawesome-visualization-research](https:\u002F\u002Fgithub.com\u002Fmathisonian\u002Fawesome-visualization-research) — A list of recommended research papers and other readings on data visualization. _★ 978_\n- [SpaceLearner\u002FAwesome-DynamicGraphLearning](https:\u002F\u002Fgithub.com\u002FSpaceLearner\u002FAwesome-DynamicGraphLearning) — Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks \u002F knowledge graphs). _★ 709_\n\n\u003Ca id=\"ccs-08-04\">\u003C\u002Fa>\n### 9.4 World Wide Web · 万维网 (4 collections)\n\n- [zjukg\u002FKG-LLM-Papers](https:\u002F\u002Fgithub.com\u002Fzjukg\u002FKG-LLM-Papers) — [Paper List] Papers integrating knowledge graphs (KGs) and large language models (LLMs). _★ 2,217_\n- [heathersherry\u002FKnowledge-Graph-Tutorials-and-Papers](https:\u002F\u002Fgithub.com\u002Fheathersherry\u002FKnowledge-Graph-Tutorials-and-Papers) — Insightful Tutorials and Papers about Knowledge Graphs. _★ 1,058_\n- [gesiscss\u002Fawesome-computational-social-science](https:\u002F\u002Fgithub.com\u002Fgesiscss\u002Fawesome-computational-social-science) — A list of awesome resources for Computational Social Science. _★ 914_\n- [ceo21ckim\u002FAwesome-Recsys](https:\u002F\u002Fgithub.com\u002Fceo21ckim\u002FAwesome-Recsys) — This Repository includes recent papers (RecSys, SIGIR, WWW, etc.) related to the Recommender Systems. _★ 257_\n\n\u003Ca id=\"ccs-08-05\">\u003C\u002Fa>\n### 9.5 Information retrieval · 信息检索 (5 collections)\n\n- [DEEP-PolyU\u002FAwesome-GraphRAG](https:\u002F\u002Fgithub.com\u002FDEEP-PolyU\u002FAwesome-GraphRAG) — Awesome-GraphRAG: A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation. _★ 2,529_\n- [RUCAIBox\u002FAwesome-RSPapers](https:\u002F\u002Fgithub.com\u002FRUCAIBox\u002FAwesome-RSPapers) — Recommender System Papers. _★ 985_\n- [gabriben\u002Fawesome-generative-information-retrieval](https:\u002F\u002Fgithub.com\u002Fgabriben\u002Fawesome-generative-information-retrieval) — Curated paper and reading-list repository. _★ 726_\n- [ict-bigdatalab\u002Fawesome-pretrained-models-for-information-retrieval](https:\u002F\u002Fgithub.com\u002Fict-bigdatalab\u002Fawesome-pretrained-models-for-information-retrieval) — A curated list of awesome papers related to pre-trained models for information retrieval (a.k.a., pretraining for IR). _★ 677_\n- [matchyc\u002Fvector-search-papers](https:\u002F\u002Fgithub.com\u002Fmatchyc\u002Fvector-search-papers) — Awesome papers and technical blogs on vector DB (database), semantic-based vector search or approximate nearest neighbor search (ANN Search, ANNS). _★ 117_\n\n\u003Cp align=\"right\">\u003Ca href=\"#readme-top\">Back to top ↑\u003C\u002Fa>\u003C\u002Fp>\n\n\u003Ca id=\"ccs-09\">\u003C\u002Fa>\n## 10. Security and privacy · 安全与隐私\n\n**Topics:** [Cryptography · 密码学](#ccs-09-01) · [Formal methods and theory of security · 安全形式化方法与理论](#ccs-09-02) · [Security services · 安全服务](#ccs-09-03) · [Intrusion\u002Fanomaly detection and malware mitigation · 入侵\u002F异常检测与恶意软件缓解](#ccs-09-04) · [Security in hardware · 硬件安全](#ccs-09-05) · [Systems security · 系统安全](#ccs-09-06) · [Network security · 网络安全](#ccs-09-07) · [Database and storage security · 数据库与存储安全](#ccs-09-08) · [Software and application security · 软件与应用安全](#ccs-09-09) · [Human and societal aspects of security and privacy · 安全与隐私的人因及社会层面](#ccs-09-10)\n\n\u003Ca id=\"ccs-09-01\">\u003C\u002Fa>\n### 10.1 Cryptography · 密码学 (3 collections)\n\n- [papers-we-love\u002Fpapers-we-love](https:\u002F\u002Fgithub.com\u002Fpapers-we-love\u002Fpapers-we-love) — Papers from the computer science community to read and discuss. _★ 107,875_\n- [pFarb\u002Fawesome-crypto-papers](https:\u002F\u002Fgithub.com\u002FpFarb\u002Fawesome-crypto-papers) — A curated list of cryptography papers, articles, tutorials and howtos. _★ 2,076_\n- [veorq\u002Fawesome-post-quantum](https:\u002F\u002Fgithub.com\u002Fveorq\u002Fawesome-post-quantum) — A curated list of resources about post-quantum cryptography. _★ 492_\n\n\u003Ca id=\"ccs-09-02\">\u003C\u002Fa>\n### 10.2 Formal methods and theory of security · 安全形式化方法与理论 (3 collections)\n\n- [ksluckow\u002Fawesome-symbolic-execution](https:\u002F\u002Fgithub.com\u002Fksluckow\u002Fawesome-symbolic-execution) — A curated list of awesome symbolic execution resources including essential research papers, lectures, videos, and tools. _★ 1,489_\n- [leonardoalt\u002Fethereum_formal_verification_overview](https:\u002F\u002Fgithub.com\u002Fleonardoalt\u002Fethereum_formal_verification_overview) — Overview of the formal verification projects in the Ethereum ecosystem. _★ 353_\n- [ElNiak\u002Fawesome-formal-verification](https:\u002F\u002Fgithub.com\u002FElNiak\u002Fawesome-formal-verification) — Welcome to the ultimate list of resources for formal verification techniques and tools. _★ 152_\n\n\u003Ca id=\"ccs-09-03\">\u003C\u002Fa>\n### 10.3 Security services · 安全服务 (4 collections)\n\n- [kdeldycke\u002Fawesome-iam](https:\u002F\u002Fgithub.com\u002Fkdeldycke\u002Fawesome-iam) — Identity and Access Management knowledge for cloud platforms. _★ 2,238_\n- [jacobian\u002Finfosec-engineering](https:\u002F\u002Fgithub.com\u002Fjacobian\u002Finfosec-engineering) — A reading list for infosec engineers. _★ 543_\n- [Guyanqi\u002FAwesome-Privacy](https:\u002F\u002Fgithub.com\u002FGuyanqi\u002FAwesome-Privacy) — Repository for collection of research papers on privacy. _★ 344_\n- [secretflow\u002Fawesome-pets](https:\u002F\u002Fgithub.com\u002Fsecretflow\u002Fawesome-pets) — Awesome lists about Privacy-Enhancing Technologies (PETs). _★ 24_\n\n\u003Ca id=\"ccs-09-04\">\u003C\u002Fa>\n### 10.4 Intrusion\u002Fanomaly detection and malware mitigation · 入侵\u002F异常检测与恶意软件缓解 (3 collections)\n\n- [jivoi\u002Fawesome-ml-for-cybersecurity](https:\u002F\u002Fgithub.com\u002Fjivoi\u002Fawesome-ml-for-cybersecurity) — octocat: Machine Learning for Cyber Security. _★ 9,118_\n- [0x4D31\u002Fawesome-threat-detection](https:\u002F\u002Fgithub.com\u002F0x4D31\u002Fawesome-threat-detection) — A curated list of awesome threat detection and hunting resources. _★ 4,679_\n- [wcventure\u002FFuzzingPaper](https:\u002F\u002Fgithub.com\u002Fwcventure\u002FFuzzingPaper) — Recent Fuzzing Paper. _★ 2,765_\n\n\u003Ca id=\"ccs-09-05\">\u003C\u002Fa>\n### 10.5 Security in hardware · 硬件安全 (3 collections)\n\n- [fkie-cad\u002Fawesome-embedded-and-iot-security](https:\u002F\u002Fgithub.com\u002Ffkie-cad\u002Fawesome-embedded-and-iot-security) — A curated list of awesome embedded and IoT security resources. _★ 2,352_\n- [firmianay\u002Fsecurity-paper](https:\u002F\u002Fgithub.com\u002Ffirmianay\u002Fsecurity-paper) — 与本人兴趣强相关的）各种安全or计算机资料收集。 _★ 749_\n- [PreOS-Security\u002Fawesome-firmware-security](https:\u002F\u002Fgithub.com\u002FPreOS-Security\u002Fawesome-firmware-security) — Awesome Firmware Security & Other Helpful Documents. _★ 619_\n\n\u003Ca id=\"ccs-09-06\">\u003C\u002Fa>\n### 10.6 Systems security · 系统安全 (3 collections)\n\n- [papers-we-love\u002Fpapers-we-love](https:\u002F\u002Fgithub.com\u002Fpapers-we-love\u002Fpapers-we-love) — Papers from the computer science community to read and discuss. _★ 107,875_\n- [wcventure\u002FFuzzingPaper](https:\u002F\u002Fgithub.com\u002Fwcventure\u002FFuzzingPaper) — Recent F",2,"2026-07-21 02:30:03","CREATED_QUERY"]