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Models","Other Applications","AI Agents","Large Language Models","Data-Centric AI and Data Curation","Efficient LLMs and Systems","Long-Context LLMs","Agent Protocols, Memory, and Tooling","Natural Language Processing (NLP)"],"readme":"# Awesome Artificial Intelligence (AI) Research [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)\n\nA curated meta-list of AI research paper lists, surveys, benchmarks, model hubs, and learning resources. It prioritizes active, high-signal, or historically useful resources for researchers and beginners.\n\n## Contents\n\n* [Foundation Models, LLMs, and Agents](#foundation-models-llms-and-agents)\n  * [Large Language Models](#large-language-models)\n  * [Long-Context LLMs](#long-context-llms)\n  * [Reasoning, Post-Training, and Synthetic Data](#reasoning-post-training-and-synthetic-data)\n  * [RAG, Search, and Knowledge-Intensive AI](#rag-search-and-knowledge-intensive-ai)\n  * [AI Agents](#ai-agents)\n  * [Agent Protocols, Memory, and Tooling](#agent-protocols-memory-and-tooling)\n  * [Alignment, Safety, Security, and Trustworthiness](#alignment-safety-security-and-trustworthiness)\n  * [Efficient LLMs and Systems](#efficient-llms-and-systems)\n* [Multimodal, Vision-Language, and Generative AI](#multimodal-vision-language-and-generative-ai)\n  * [Multimodal and Vision-Language Models](#multimodal-and-vision-language-models)\n  * [Diffusion and Generative Models](#diffusion-and-generative-models)\n  * [Computer Vision](#computer-vision)\n  * [Embodied AI, Robotics, and World Models](#embodied-ai-robotics-and-world-models)\n* [Core Machine Learning Research](#core-machine-learning-research)\n  * [General ML, Surveys, and Methods](#general-ml-surveys-and-methods)\n  * [Data-Centric AI and Data Curation](#data-centric-ai-and-data-curation)\n  * [Robustness, Interpretability, and Learning Paradigms](#robustness-interpretability-and-learning-paradigms)\n  * [Reinforcement Learning](#reinforcement-learning)\n  * [Federated and Privacy-Preserving ML](#federated-and-privacy-preserving-ml)\n  * [Graph Learning and Knowledge Graphs](#graph-learning-and-knowledge-graphs)\n* [Domain Applications](#domain-applications)\n  * [Natural Language, Speech, and Audio](#natural-language-speech-and-audio)\n  * [Recommendation, Search, and Ads](#recommendation-search-and-ads)\n  * [Science, Medicine, and Quant](#science-medicine-and-quant)\n  * [Anomaly Detection](#anomaly-detection)\n* [Research Feeds, Benchmarks, and Model/Data Hubs](#research-feeds-benchmarks-and-modeldata-hubs)\n  * [Paper Discovery and Code](#paper-discovery-and-code)\n  * [Models, Datasets, and Evaluation](#models-datasets-and-evaluation)\n  * [Learning Paths](#learning-paths)\n\n## Foundation Models, LLMs, and Agents\n\n### Large Language Models\n\n* [Awesome LLM](https://github.com/Hannibal046/Awesome-LLM) - broad LLM papers, model lists, training, inference, evaluation, and tutorials.\n* [Awesome LLM Reasoning](https://github.com/atfortes/Awesome-LLM-Reasoning) - reasoning, chain-of-thought, o1/R1-style methods, and multimodal reasoning.\n* [Awesome Reasoning Foundation Models](https://github.com/reasoning-survey/Awesome-Reasoning-Foundation-Models) - reasoning with language, vision, and multimodal foundation models.\n* [Awesome LLM Evaluation Papers](https://github.com/tjunlp-lab/Awesome-LLMs-Evaluation-Papers) - evaluation methods, benchmarks, and survey papers.\n* [Awesome Multilingual LLMs Papers](https://github.com/tjunlp-lab/Awesome-Multilingual-LLMs-Papers) - multilingual LLM data, training, evaluation, and applications.\n\n### Long-Context LLMs\n\n* [Thus Spake Long-Context LLM](https://github.com/OpenMOSS/Thus-Spake-Long-Context-LLM) - survey and paper list covering long-context architecture, infrastructure, training, inference, and evaluation.\n* [RULER](https://github.com/NVIDIA/RULER) - synthetic benchmark for evaluating effective long-context language model performance across configurable tasks and sequence lengths.\n\n### Reasoning, Post-Training, and Synthetic Data\n\n* [Awesome Inference-Time Scaling](https://github.com/ThreeSR/Awesome-Inference-Time-Scaling) - inference/test-time compute, search, self-refinement, and verifier-guided reasoning papers.\n* [Awesome Test-Time Scaling in LLMs](https://github.com/testtimescaling/testtimescaling.github.io) - survey-oriented taxonomy and paper list for test-time scaling.\n* [Awesome LLM Post-Training](https://github.com/mbzuai-oryx/Awesome-LLM-Post-training) - reasoning LLM post-training, RL, distillation, alignment, and evaluation resources.\n* [TRL](https://github.com/huggingface/trl) - Hugging Face library for SFT, DPO, GRPO, reward modeling, and RLHF post-training.\n* [verl](https://github.com/verl-project/verl) - flexible RL post-training framework for LLMs with scalable rollout and training infrastructure.\n* [Awesome LLM Synthetic Data](https://github.com/wasiahmad/Awesome-LLM-Synthetic-Data) - synthetic data generation papers, tools, and guides for LLM training and post-training.\n\n### RAG, Search, and Knowledge-Intensive AI\n\n* [Awesome RAG](https://github.com/coree/awesome-rag) - retrieval-augmented generation papers, tutorials, tools, and workshops.\n* [FlashRAG](https://github.com/RUC-NLPIR/FlashRAG) - research toolkit for efficient RAG pipelines, datasets, metrics, and reproducible experiments.\n* [Awesome LLM KG](https://github.com/RManLuo/Awesome-LLM-KG) - unifying LLMs and knowledge graphs.\n* [Database Learning](https://github.com/pingcap/awesome-database-learning) - database systems, data management, and ML-related database resources.\n\n### AI Agents\n\n* [Awesome AI Agents](https://github.com/e2b-dev/awesome-ai-agents) - autonomous agent projects and resources.\n* [Awesome AI Agent Papers](https://github.com/VoltAgent/awesome-ai-agent-papers) - weekly updated 2026 agent research papers on memory, tools, evaluation, workflows, and security.\n* [Awesome Code Agents](https://github.com/euniai/awesome-code-agents) - coding agents, software engineering agents, benchmarks, and research papers.\n* [Awesome GUI Agent](https://github.com/showlab/awesome-gui-agent) - papers and resources for multimodal GUI, browser, and computer-use agents.\n* [Awesome Computer Use](https://github.com/ranpox/awesome-computer-use) - computer-use GUI agent papers, projects, blogs, and benchmarks.\n* [Awesome Data Agents](https://github.com/HKUSTDial/awesome-data-agents) - agents for data preparation, analysis, and data management.\n* [Awesome Agents for Science](https://github.com/OSU-NLP-Group/awesome-agents4science) - LLM agents for scientific research and development.\n* [Awesome Edge AI for Multimodal Agents](https://github.com/yh-yao/awesome-edge-ai-agents) - efficient multimodal agents on mobile and edge devices.\n* [Open Deep Research](https://github.com/langchain-ai/open_deep_research) - open-source research agent implementation for iterative search, synthesis, and reporting.\n\n### Agent Protocols, Memory, and Tooling\n\n* [Awesome MCP Servers](https://github.com/punkpeye/awesome-mcp-servers) - large curated list of Model Context Protocol servers for connecting agents to tools and data sources.\n* [Awesome MCP Clients](https://github.com/punkpeye/awesome-mcp-clients) - MCP-capable clients and applications across desktop, IDE, CLI, and agent workflows.\n* [MCP Agent](https://github.com/lastmile-ai/mcp-agent) - framework and patterns for building agents on top of Model Context Protocol.\n* [Awesome Agent Memory](https://github.com/TeleAI-UAGI/Awesome-Agent-Memory) - papers, systems, and benchmarks for long-term memory, context engineering, retrieval, and reasoning in agents.\n\n### Alignment, Safety, Security, and Trustworthiness\n\n* [Awesome LLM Safety Papers](https://github.com/tjunlp-lab/Awesome-LLM-Safety-Papers) - LLM safety, alignment, jailbreaks, privacy, and robustness papers.\n* [AgentHarm](https://proceedings.iclr.cc/paper_files/paper/2025/hash/c493d23af93118975cdbc32cbe7323f5-Abstract-Conference.html) - benchmark for harmful multi-step LLM agent tasks and jailbreak robustness.\n* [Awesome AI Safety](https://github.com/Giskard-AI/awesome-ai-safety) - AI quality, testing, robustness, fairness, and privacy.\n* [Awesome AI Alignment](https://github.com/dit7ya/awesome-ai-alignment) - alignment research resources and reading paths.\n* [Awesome AI Security](https://github.com/DeepSpaceHarbor/Awesome-AI-Security) - adversarial ML, LLM security, and broader AI security.\n* [Machine Learning for Cyber Security](https://github.com/jivoi/awesome-ml-for-cybersecurity) - ML methods for cyber security.\n* [Threat Detection and Hunting](https://github.com/0x4D31/awesome-threat-detection#research-papers) - practical security detection and hunting research resources.\n\n### Efficient LLMs and Systems\n\n* [Awesome Efficient LLM](https://github.com/horseee/Awesome-Efficient-LLM) - quantization, pruning, KV cache compression, MoE, serving, and efficient reasoning.\n* [Awesome Resource-Efficient LLM Papers](https://github.com/tiingweii-shii/Awesome-Resource-Efficient-LLM-Papers) - resource-efficient LLM pre-training, fine-tuning, and inference.\n* [Awesome LLMOps](https://github.com/awesomelistsio/awesome-llmops) - production LLM lifecycle, evaluation, observability, deployment, and prompt management.\n* [Model Inference Deployment](https://github.com/Yulv-git/Model-Inference-Deployment) - inference deployment frameworks across TensorRT, ONNX Runtime, OpenVINO, TVM, mobile runtimes, and edge accelerators.\n\n## Multimodal, Vision-Language, and Generative AI\n\n### Multimodal and Vision-Language Models\n\n* [Awesome Multimodal Modeling](https://github.com/OpenEnvision-Lab/Awesome-Multimodal-Modeling) - unified multimodal modeling across MLLMs, understanding, generation, and omni-modal agents.\n* [Awesome Multimodal Large Language Models](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) - current MLLM papers, benchmarks, datasets, and evaluation.\n* [Awesome Large Multimodal Models](https://github.com/FudanDISC/Awesome-Large-Multimodal-Models) - large multimodal models from input-output space perspectives.\n* [Awesome Large Vision-Language Model](https://github.com/SuperBruceJia/Awesome-Large-Vision-Language-Model) - VLM papers, medical foundation models, benchmarks, and presentations.\n* [Awesome Vision-Language Models for Vision Tasks](https://github.com/jingyi0000/VLM_survey) - VLMs for classification, detection, segmentation, and other vision tasks.\n* [Awesome Multimodal Reasoning](https://github.com/The-Martyr/Awesome-Multimodal-Reasoning) - multimodal reasoning and RL-based visual reasoning papers, surveys, benchmarks, and datasets.\n* [Awesome Document Understanding](https://github.com/harrytea/Awesome-Document-Understanding) - document AI, OCR, layout understanding, and document-oriented MLLM resources.\n* [Awesome Multimodal ML](https://github.com/pliang279/awesome-multimodal-ml) - classic and modern multimodal machine learning resources.\n* [MMLongBench](https://github.com/EdinburghNLP/MMLongBench) - benchmark and evaluation code for long-context vision-language models.\n* [Video-MME-v2](https://github.com/MME-Benchmarks/Video-MME-v2) - benchmark and evaluation pipeline for comprehensive video understanding in multimodal models.\n* [MMMU](https://github.com/MMMU-Benchmark/MMMU) - multidisciplinary multimodal understanding and reasoning benchmark with evaluation code and leaderboard.\n\n### Diffusion and Generative Models\n\n* [Awesome Diffusion Models](https://github.com/diff-usion/Awesome-Diffusion-Models) - diffusion and score-based generative model papers and resources.\n* [Awesome Video World Models](https://github.com/hit-perfect/Awesome-Video-World-Models) - video generation and interactive world-model papers with a state/dynamics taxonomy.\n* [Awesome Video Diffusions](https://github.com/longxiang-ai/awesome-video-diffusions) - auto-updated video diffusion and video generation paper tracker.\n* [Really Awesome GAN](https://github.com/nightrome/really-awesome-gan#papers) - GAN papers and resources.\n* [Awesome Normalizing Flows](https://github.com/janosh/awesome-normalizing-flows) - normalizing flow papers and implementations.\n\n### Computer Vision\n\n* [Awesome Deep Vision](https://github.com/kjw0612/awesome-deep-vision) - classic deep vision resources with high historical value.\n* [Awesome Object Detection](https://github.com/amusi/awesome-object-detection) - object detection papers.\n* [Deep Learning Object Detection](https://github.com/hoya012/deep_learning_object_detection) - object detection paper list with many classic references.\n* [3D Gaussian Splatting Papers](https://github.com/Awesome3DGS/3D-Gaussian-Splatting-Papers) - actively maintained 3D Gaussian Splatting paper tracker, including conference-specific lists.\n* [Awesome Lane Detection](https://github.com/amusi/awesome-lane-detection) - lane detection papers.\n* [Awesome Visual Transformer](https://github.com/dk-liang/Awesome-Visual-Transformer) - transformers for computer vision.\n* [Awesome Point Cloud Analysis](https://github.com/NUAAXQ/awesome-point-cloud-analysis-2021) - 3D point cloud papers and datasets.\n* [3D Point Cloud](https://github.com/zhulf0804/3D-PointCloud) - point cloud deep learning resources.\n* [Awesome Crowd Counting](https://github.com/gjy3035/Awesome-Crowd-Counting) - crowd counting papers and datasets.\n* [Awesome Face Recognition](https://github.com/ChanChiChoi/awesome-Face_Recognition) - face detection, recognition, alignment, generation, and anti-spoofing.\n* [Awesome Image Classification](https://github.com/weiaicunzai/awesome-image-classification) - image classification papers and code.\n* [Awesome CBIR Papers](https://github.com/willard-yuan/awesome-cbir-papers) - image retrieval and content-based image retrieval.\n\n### Embodied AI, Robotics, and World Models\n\n* [Awesome Embodied AI](https://github.com/wadeKeith/Awesome-Embodied-AI) - embodied AI surveys, VLA models, datasets, simulators, humanoids, and safety.\n* [Awesome Physical AI](https://github.com/keon/awesome-physical-ai) - VLA models, robot foundation models, world models, diffusion policies, evaluation, and safety.\n* [Awesome VLA Robotics](https://github.com/Jiaaqiliu/Awesome-VLA-Robotics) - vision-language-action models and robot foundation model papers.\n* [Awesome VLA Papers](https://github.com/hanjianhua44/Awesome-VLA-Papers) - VLA papers covering robotics, autonomous driving, world models, and spatial reasoning.\n* [Awesome World Models](https://github.com/leofan90/Awesome-World-Models) - world models for video generation, embodied AI, robotics, and autonomous driving.\n* [Awesome World Models for Robots](https://github.com/operator22th/awesome-world-models-for-robots) - world-model papers, datasets, and workshops focused on robotics.\n* [Awesome Autonomous Vehicles](https://github.com/manfreddiaz/awesome-autonomous-vehicles#papers) - self-driving and autonomous vehicle resources.\n\n## Core Machine Learning Research\n\n### General ML, Surveys, and Methods\n\n* [Machine Learning Surveys](https://github.com/eugeneyan/ml-surveys) - survey papers across ML, NLP, CV, graphs, RL, and recommendation.\n* [Machine Learning Surveys and Tutorials](https://github.com/metrofun/machine-learning-surveys) - older but still useful survey collection.\n* [AutoML Papers](https://github.com/hibayesian/awesome-automl-papers) - automated machine learning papers and tutorials.\n* [Awesome AutoDL](https://github.com/D-X-Y/Awesome-AutoDL) - automated deep learning and neural architecture search.\n* [Awesome Architecture Search](https://github.com/markdtw/awesome-architecture-search) - neural architecture search resources.\n* [Awesome Causality Algorithms](https://github.com/rguo12/awesome-causality-algorithms) - causal discovery and inference algorithms.\n* [Awesome Decision Tree Papers](https://github.com/benedekrozemberczki/awesome-decision-tree-papers) - decision tree, classification tree, and regression tree papers.\n* [Bayesian Deep Learning Survey](https://github.com/js05212/BayesianDeepLearning-Survey) - Bayesian deep learning survey resources.\n* [Deep Learning Uncertainty](https://github.com/ahmedmalaa/deep-learning-uncertainty) - predictive uncertainty in deep learning.\n* [Awesome Online Machine Learning](https://github.com/MaxHalford/awesome-online-machine-learning) - online and streaming ML resources.\n* [Awesome Time Series Papers](https://github.com/TSCenter/awesome-time-series-papers) - recent time-series papers and code across forecasting, anomaly detection, foundation models, and representation learning.\n\n### Data-Centric AI and Data Curation\n\n* [Awesome Open Data-Centric AI](https://github.com/Renumics/awesome-open-data-centric-ai) - open-source tools for data-centric AI on unstructured data.\n* [Data-Juicer](https://github.com/datajuicer/data-juicer) - data processing, cleaning, filtering, and analysis toolkit for foundation-model datasets.\n* [Awesome Synthetic Datasets](https://github.com/davanstrien/awesome-synthetic-datasets) - practical resources and examples for creating synthetic text and vision datasets.\n\n### Robustness, Interpretability, and Learning Paradigms\n\n* [Adversarial Machine Learning](https://github.com/wangjksjtu/awesome-AML) - adversarial ML papers and resources.\n* [Awesome Machine Learning Interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability#review-and-general-papers) - responsible ML, interpretability, and explainability.\n* [Awesome LLM Interpretability](https://github.com/JShollaj/awesome-llm-interpretability) - LLM interpretability tools, papers, communities, sparse autoencoders, probing, and mechanistic interpretability resources.\n* [Awesome Interpretable Machine Learning](https://github.com/lopusz/awesome-interpretable-machine-learning) - interpretable ML papers.\n* [Awesome Explainable AI](https://github.com/wangyongjie-ntu/Awesome-explainable-AI) - explainability papers and resources.\n* [Awesome Knowledge Distillation](https://github.com/dkozlov/awesome-knowledge-distillation) - distillation papers and implementations.\n* [Awesome Self-Supervised Learning](https://github.com/jason718/awesome-self-supervised-learning) - self-supervised learning methods.\n* [Awesome Learning with Label Noise](https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise) - noisy-label learning papers.\n* [Awesome Meta Learning](https://github.com/sudharsan13296/Awesome-Meta-Learning) - meta-learning papers and resources.\n* [Awesome Transfer Learning](https://github.com/artix41/awesome-transfer-learning) - transfer learning and domain adaptation.\n* [Awesome Domain Adaptation](https://github.com/zhaoxin94/awesome-domain-adaptation) - domain adaptation papers and code.\n* [Awesome Open World Learning](https://github.com/zhoudw-zdw/Awesome-open-world-learning) - open-world, open-set, and incremental recognition.\n* [Awesome Incremental Learning](https://github.com/xialeiliu/Awesome-Incremental-Learning) - incremental and lifelong learning.\n* [Continual Learning Papers](https://github.com/optimass/continual_learning_papers) - continual learning papers.\n\n### Reinforcement Learning\n\n* [Awesome RL](https://github.com/aikorea/awesome-rl) - reinforcement learning resources.\n* [Awesome Deep RL](https://github.com/tigerneil/awesome-deep-rl) - deep reinforcement learning papers.\n* [Deep Reasoning Papers](https://github.com/floodsung/Deep-Reasoning-Papers) - neural-symbolic, logical, visual, and planning-oriented reasoning.\n\n### Federated and Privacy-Preserving ML\n\n* [Awesome Federated Learning](https://github.com/weimingwill/awesome-federated-learning) - federated learning papers and resources.\n* [Awesome Federated Machine Learning](https://github.com/innovation-cat/Awesome-Federated-Machine-Learning) - federated ML papers, books, code, tutorials, and videos.\n\n### Graph Learning and Knowledge Graphs\n\n* [Graph-Based Deep Learning Literature](https://github.com/naganandy/graph-based-deep-learning-literature) - graph deep learning papers by venue.\n* [Awesome Graph Neural Networks](https://github.com/nnzhan/Awesome-Graph-Neural-Networks) - GNN paper list.\n* [Must-Read Papers on GNN](https://github.com/thunlp/GNNPapers) - curated GNN papers.\n* [Awesome Self-Supervised GNN](https://github.com/ChandlerBang/awesome-self-supervised-gnn) - self-supervised learning on graphs.\n* [Awesome Graph Classification](https://github.com/benedekrozemberczki/awesome-graph-classification) - graph classification and representation learning.\n* [Awesome Community Detection](https://github.com/benedekrozemberczki/awesome-community-detection) - community detection papers.\n* [Knowledge Graphs](https://github.com/shaoxiongji/knowledge-graphs) - knowledge graph papers and resources.\n* [Knowledge Representation Learning](https://github.com/thunlp/KRLPapers) - knowledge representation learning papers.\n\n## Domain Applications\n\n### Natural Language, Speech, and Audio\n\n* [Awesome NLG](https://github.com/tokenmill/awesome-nlg) - natural language generation papers and resources.\n* [Awesome Sentence Embedding](https://github.com/Separius/awesome-sentence-embedding) - sentence embedding resources.\n* [Awesome Large Speech Model](https://github.com/huangcanan/Awesome-Large-Speech-Model) - large speech/audio model papers, datasets, tools, applications, and benchmarks.\n* [Awesome Speech Recognition and Speech Synthesis Papers](https://github.com/zzw922cn/awesome-speech-recognition-speech-synthesis-papers) - ASR and TTS papers.\n* [Awesome Speech Enhancement](https://github.com/nanahou/Awesome-Speech-Enhancement) - speech enhancement papers and tutorials.\n* [Speech Synthesis Papers](https://github.com/xcmyz/speech-synthesis-paper) - speech synthesis paper list.\n* [Awesome Deep Learning Music](https://github.com/ybayle/awesome-deep-learning-music) - deep learning for music.\n\n### Recommendation, Search, and Ads\n\n* [RSTutorials](https://github.com/hongleizhang/RSPapers) - must-read recommender system papers.\n* [Awesome Recommender System](https://github.com/scnu-dil/awesome-RecSys) - recommender system papers.\n* [Awesome Deep Learning Papers for Search, Recommendation, and Advertising](https://github.com/guyulongcs/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising) - industrial search, recommendation, ranking, ads, LLM, and RL papers.\n* [Real-Time Bidding Papers](https://github.com/wnzhang/rtb-papers) - RTB and computational advertising papers.\n\n### Science, Medicine, and Quant\n\n* [Awesome AI for Science](https://github.com/ai-boost/awesome-ai-for-science) - AI tools, papers, datasets, and frameworks for scientific discovery.\n* [Awesome Scientific Language Models](https://github.com/yuzhimanhua/Awesome-Scientific-Language-Models) - scientific language models across math, physics, chemistry, materials, biology, medicine, and geoscience.\n* [Awesome AI for Research](https://github.com/THU-KEG/Awesome-AI-for-Research) - AI-for-research papers and systems organized by research stage, role, domain, and evaluation.\n* [Awesome Deep Research Agent](https://github.com/ai-agents-2030/awesome-deep-research-agent) - deep research agents, AI scientist systems, search-augmented reasoning, and research workflow papers.\n* [Awesome AI Scientist](https://github.com/ResearAI/Awesome-AI-Scientist) - survey-style collection on AI scientists, AI researchers, AI engineers, and automated research pipelines.\n* [SciAgentArena](https://sciagentarena.github.io/) - living benchmark for evaluating AI agents on real-world scientific research tasks across domains.\n* [Awesome DeepBio](https://github.com/gokceneraslan/awesome-deepbio) - deep learning for computational biology.\n* [Helical](https://github.com/helicalAI/helical) - framework for using and fine-tuning bio foundation models across genomics and transcriptomics.\n* [Awesome GAN for Medical Imaging](https://github.com/xinario/awesome-gan-for-medical-imaging) - medical image synthesis papers.\n* [Awesome AI Agents for Healthcare](https://github.com/AgenticHealthAI/Awesome-AI-Agents-for-Healthcare) - healthcare agentic AI papers and resources.\n* [HealthBench](https://openai.com/index/healthbench/) - benchmark for evaluating AI systems on realistic health conversations with physician-written rubrics.\n* [Awesome Quant Machine Learning Trading](https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading) - quant trading and ML resources.\n\n### Anomaly Detection\n\n* [Anomaly Detection Resources](https://github.com/yzhao062/anomaly-detection-resources#4-papers) - anomaly detection papers, books, code, and datasets.\n* [Awesome Anomaly Detection](https://github.com/hoya012/awesome-anomaly-detection) - anomaly detection papers and resources.\n\n## Research Feeds, Benchmarks, and Model/Data Hubs\n\n### Paper Discovery and Code\n\n* [Papers with Code](https://paperswithcode.com/) - papers, tasks, datasets, code, and leaderboards.\n* [Hugging Face Papers](https://huggingface.co/papers) - daily ML paper discovery and discussion.\n* [arXiv Sanity](https://arxiv-sanity-lite.com/) - arXiv search and paper recommendation.\n* [Semantic Scholar](https://www.semanticscholar.org/) - AI-powered academic search.\n* [OpenReview](https://openreview.net/) - conference submissions, reviews, and accepted papers.\n\n### Models, Datasets, and Evaluation\n\n* [Hugging Face Models](https://huggingface.co/models) - model hub for open models.\n* [Hugging Face Datasets](https://huggingface.co/datasets) - dataset hub for ML research.\n* [Open LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) - open model evaluation leaderboard.\n* [LMArena Leaderboard](https://lmarena.ai/leaderboard) - human-preference leaderboard for chat and multimodal models.\n* [LiveBench](https://livebench.ai/) - contamination-resistant LLM benchmark with refreshed objective tasks and a public leaderboard.\n* [Epoch AI Benchmarks](https://epoch.ai/benchmarks) - benchmark results hub for tracking frontier model capabilities across major evaluation suites.\n* [Awesome AI Benchmarks](https://github.com/panilya/awesome-ai-benchmarks) - searchable collection of benchmarks for agents, reasoning, code, multimodal, translation, and other AI domains.\n* [Awesome LLM Eval](https://github.com/onejune2018/awesome-llm-eval) - tools, datasets, benchmarks, leaderboards, papers, and demos for LLM evaluation.\n* [Awesome Scientific LLM Benchmarks](https://github.com/subinium/Awesome-Scientific-LLM-Benchmarks) - benchmarks for evaluating LLMs on scientific reasoning, discovery, and domain knowledge.\n* [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) - widely used framework for evaluating language models on many benchmark tasks.\n* [OpenAI Evals](https://github.com/openai/evals) - framework and open registry for evaluating LLMs and LLM systems.\n* [Inspect AI](https://github.com/UKGovernmentBEIS/inspect_ai) - open-source framework for LLM, multimodal, coding, and agent evaluations.\n* [Inspect Evals](https://github.com/UKGovernmentBEIS/inspect_evals) - community collection of ready-to-run benchmark implementations for Inspect AI.\n* [OpenCompass](https://github.com/open-compass/opencompass) - open evaluation platform for LLMs and multimodal models.\n* [HELM](https://github.com/stanford-crfm/helm) - holistic evaluation framework and leaderboards for language, multimodal, safety, and domain benchmarks.\n* [HAL](https://hal.cs.princeton.edu/) - standardized cost-aware leaderboard and harness for reproducible AI agent evaluation.\n* [GAIA](https://huggingface.co/gaia-benchmark) - benchmark and leaderboard for tool-using, multimodal, web-browsing AI assistants.\n* [BrowseComp](https://openai.com/index/browsecomp/) - benchmark for browsing agents that must locate hard-to-find information on the web.\n* [AgentBench](https://github.com/THUDM/AgentBench) - benchmark and leaderboard for evaluating LLM agents across interactive environments.\n* [ARC-AGI](https://github.com/fchollet/ARC-AGI) - abstraction-and-reasoning benchmark for testing generalization on novel visual puzzle tasks.\n* [WebArena](https://github.com/web-arena-x/webarena) - realistic self-hosted web environment and benchmark for autonomous web agents.\n* [OSWorld](https://github.com/xlang-ai/OSWorld) - benchmark for multimodal agents completing open-ended tasks in real computer environments.\n* [tau2-bench](https://github.com/sierra-research/tau2-bench) - tool-agent-user interaction benchmark for text and voice customer-service agents.\n* [The Agent Company](https://github.com/TheAgentCompany/TheAgentCompany) - benchmark for autonomous agents completing workplace tasks in a simulated software company.\n* [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard) - leaderboard and evaluation suite for LLM tool-use and function-calling capabilities.\n* [Humanity's Last Exam](https://lastexam.ai/) - expert-level benchmark designed to test frontier models across broad academic subjects.\n* [FrontierMath](https://epoch.ai/frontiermath/tiers-1-4/about) - advanced mathematical reasoning benchmark and leaderboard for evaluating frontier AI systems.\n* [SWE-bench](https://github.com/SWE-bench/SWE-bench) - benchmark and evaluation harness for real-world software engineering issue resolution.\n* [SWE-Lancer](https://github.com/openai/frontier-evals/tree/main/project/swelancer) - benchmark of real freelance software engineering tasks with end-to-end tests and managerial decisions.\n* [LiveCodeBench](https://github.com/LiveCodeBench/LiveCodeBench) - contamination-aware benchmark for code generation, repair, execution, and test prediction.\n* [BigCodeBench](https://github.com/bigcode-project/bigcodebench) - practical code-generation benchmark with diverse function calls, complex instructions, and a leaderboard.\n* [Terminal-Bench](https://www.tbench.ai/) - realistic terminal-agent benchmark suite spanning software engineering, ML, security, and data tasks.\n* [PaperBench](https://github.com/openai/frontier-evals/tree/main/project/paperbench) - benchmark for end-to-end replication of state-of-the-art AI papers.\n* [MLE-bench](https://github.com/openai/mle-bench) - benchmark for measuring how well AI agents perform at machine learning engineering.\n* [MLGym](https://github.com/facebookresearch/MLGym) - framework and benchmark for evaluating AI research agents on open-ended machine learning tasks.\n* [SciCode](https://github.com/scicode-bench/SciCode) - scientist-curated benchmark for code generation on realistic scientific research problems.\n* [AstaBench](https://allenai.org/asta/bench) - benchmark suite and leaderboards for evaluating agents on scientific research tasks.\n* [MTEB](https://github.com/embeddings-benchmark/mteb) - embedding benchmark suite and leaderboard across languages, tasks, and modalities.\n* [Simple Evals](https://github.com/openai/simple-evals) - lightweight evaluation examples and baseline eval implementations.\n* [DeepEval](https://github.com/confident-ai/deepeval) - LLM evaluation framework for unit-style tests, RAG metrics, agents, and CI workflows.\n* [Awesome Public Datasets](https://github.com/awesomedata/awesome-public-datasets) - topic-centric list of public datasets.\n* [LeRobot](https://github.com/huggingface/lerobot) - open models, datasets, tools, and tutorials for robotics research.\n* [MinerU](https://github.com/OpenDataLab/MinerU) - open-source document extraction tool for converting complex PDFs and Office files into Markdown/JSON.\n* [OpenAI Cookbook](https://github.com/openai/openai-cookbook) - practical examples and guides for building and evaluating AI systems.\n\n### Learning Paths\n\n* [Dive into Deep Learning](https://d2l.ai/) - interactive deep learning book with code.\n* [fast.ai Practical Deep Learning](https://course.fast.ai/) - practical deep learning course.\n* [CS229 Machine Learning](https://cs229.stanford.edu/) - Stanford machine learning course.\n* [CS231n Convolutional Neural Networks for Visual Recognition](https://cs231n.stanford.edu/) - computer vision fundamentals.\n* [CS224N Natural Language Processing with Deep Learning](https://web.stanford.edu/class/cs224n/) - NLP and language model foundations.\n* [CS324 Large Language Models](https://stanford-cs324.github.io/winter2022/) - large language model concepts, training, evaluation, and societal impact.\n* [Berkeley CS285 Deep Reinforcement Learning](https://rail.eecs.berkeley.edu/deeprlcourse/) - deep RL lectures and assignments.\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/thyrixyang%2Fawesome-artificial-intelligence-research/projects"}