🎓arXivSub, an Academia Assistant

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With over 300 to 500 new papers uploaded daily to the arXiv Computer Science (CS) category alone, researchers face a real challenge filtering high-value work. arXivSub (arxivsub.comfyai.app) tames this information overload by combining automated crawling, multi-dimensional AI summarization, personalized email alerts, and trend analytics across arXiv plus 86,000+ papers from 15 leading AI conferences, including CVPR, ICLR, NeurIPS, ICML, AAAI, ICCV, ECCV, ACL, EMNLP, MICCAI, IJCAI, ICRA, IROS, RSS, and CoRL.
You can browse free AI-generated summaries of the latest CVPR, ICLR, and AAAI 2026 papers without creating an account.

🚀 Core Features and Functionalities

1. Automated Daily Indexing

arXivSub monitors 40+ arXiv sub-categories across the Computer Science domain (cs.CV, cs.AI, cs.LG, cs.CL, and more) and indexes the accepted papers of 15 leading AI conferences, from CVPR, ICCV, ECCV, and ICML to ICLR, NeurIPS, AAAI, MICCAI, ACL, EMNLP, IJCAI, ICRA, IROS, RSS, and CoRL.
  • Real-time Updates: Get notified of the latest pre-prints the day they drop.
  • Full Coverage: Over 140,000 arXiv papers and 86,000+ conference papers indexed and growing, so nothing in your field slips through.
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2. Multi-Dimensional AI Summarization

Unlike basic abstract readers, arXivSub uses large language models to decompose every paper into six structured, skimmable dimensions, available in both English and Chinese:
  • Problem & Context: What the paper tackles and why it matters.
  • Core Innovations: What makes the research novel.
  • Technical Methodology: The specific architecture or algorithms used.
  • Datasets & Benchmarks: What data was used for training and evaluation.
  • Performance & Comparison: How it stacks up against the previous state of the art.
  • Limitations: Honest drawbacks and open directions.
Every summary is auto-tagged with keywords, so you can judge relevance in seconds without opening the PDF.
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3. Advanced Search and Precision Filtering

arXivSub features a searchable database of 140,000+ arXiv papers and 86,000+ conference papers, including full historical proceedings from major venues. Browse the collections by conference: CVPR 2026 papers, ICLR 2026 papers, and AAAI 2026 papers, with more venues added continuously.
  • Metadata Filtering: Filter by keywords, specific author names (e.g., Kaiming He), or research institutions (e.g., Stanford, DeepMind).
  • Semantic & Similar-Paper Search: Embedding-based search surfaces niche or older papers that match your current interest, and every paper links to its most similar papers so you can trace a topic quickly.
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4. Personalized Email Alerts (Follow Anything)

Stay current without visiting the site daily. Build Watchlists and let the digest come to you:
  • Follow topics, authors, or papers: Track a keyword, or follow every new paper from a specific researcher.
  • Follow institutions: Watch the output of specific universities or labs (Stanford, DeepMind, and the like).
  • Bilingual delivery: Receive the AI summaries in English or Chinese, straight to your inbox.
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5. Research "Insights" & Trend Analytics

The Insights Dashboard gives a macro view of the research landscape through interactive data visualization:
  • Keyword Trends: Watch specific research topics rise and fall over time.
  • Institutional Rankings: See the most active or influential institutions in any sub-field.
  • Growth Metrics: Track total paper volume and growth rates within categories.
  • Comparative Analysis: Compare the publishing output of different authors or schools side by side.

6. Save, Organize, and Revisit

Turn the daily feed into your own research library:
  • Collections & notes: Bookmark papers into named collections and attach private notes.
  • Similar papers: Jump from any paper to its closest related work to map a topic fast.
  • Reference-manager friendly: Bring your finds into your existing Zotero workflow.

🛠 Optimized Research Workflow

arXivSub is built around a three-step discovery loop:
  1. Screen: Use the Insights dashboard and keyword search to spot high-interest areas.
  1. Filter: Read the AI summaries to judge relevance in seconds, no PDF required.
  1. Deep Dive: Use "Ask AI" for a closer read, check citations on Google Scholar, then save the keepers into your collections.

🌐 Get Started

Experience a smarter way to track the frontier of Artificial Intelligence.
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Article List
LLM Learning Roadmap
✨ Awesome-Anything
Lazy loaded image Awesome-AI-Tutorials
Lazy loaded image Awesome-AI-Researchs
Lazy loaded image Awesome-GitHub-Repos
Lazy loaded image Awesome-AI-Examples
🖼️ Digital Image Processing
Lazy loaded image DIP I Image Demosaicing and Histogram Manipulation
Lazy loaded image DIP II Image Denoising
Lazy loaded image DIP III Edge Detection
Lazy loaded image DIP IV Digital Half-toning
Lazy loaded image DIP V Geometric Image Modification
🍃 LLM Components
Lazy loaded image 1.1 Tokenization
Lazy loaded image 1.2 Embedding
Lazy loaded image 1.3 Attention
Lazy loaded image 1.4 FFN, Residual Addition, and LN
Lazy loaded image 1.5 Positional Encoding
Lazy loaded image 1.6 Architecture & Decoding Policy
🌱 LLM Pre-training
Lazy loaded image 2.1 Pre-training Definition
Lazy loaded image 2.2 Pre-training Data
Lazy loaded image 2.3 Pre-training Workflow
Lazy loaded image 2.4 Continued Pre-training (CPT)
Lazy loaded image 2.5 Pre-training Evaluation
☘️ LLM Post-Training
Lazy loaded image 3.1 Supervised Fine-Tuning (SFT)
Lazy loaded image 3.2 Reinforcement Learning
Lazy loaded image 3.3 RLHF: Reinforcement Learning from Human Feedback
Lazy loaded image 3.4 Optimizing PPO-Based Algorithms
Lazy loaded image 3.5 Direct Preference Optimization (DPO)
🍀 LLM Popular Models
Lazy loaded image 4.1 BERT & Variants
Lazy loaded image 4.2 PaLM Series
Lazy loaded image 4.3 GPT Series
Lazy loaded image 4.4 LLaMA Series
Lazy loaded image 4.5 GLM Series
Lazy loaded image 4.6 Qwen Series
Lazy loaded image 4.7 DeepSeek Series
Lazy loaded image 4.8 MOE Series
Lazy loaded image 4.9 Other
🪴 LLM Applications
Lazy loaded image 5.1 Prompt Engineering
Lazy loaded image 5.2 LLM-based Agents
Lazy loaded image 5.3 Retrieval-Augmented Generation (RAG)
Lazy loaded image 5.4 Enterprise-level RAG Practice (I)
Lazy loaded image 5.5 Enterprise-level RAG Practice (II)
🌿 LLM Optimization
Lazy loaded image 6.1 GPU Usage Analysis
Lazy loaded image 6.2 Latency Analysis for Inference
Lazy loaded image 6.3 FlashAttention
Lazy loaded image 6.4 PagedAttention
Lazy loaded image 6.5 Packing for Efficient Training
Lazy loaded image 6.6 PEFT
🌾 LLM Compression
Lazy loaded image 7.1 Quantization
Lazy loaded image 7.2 Pruning
Lazy loaded image 7.3 Knowledge Distillation
🌵 LLM Hands-on Practice
Lazy loaded image 8.1 nanoGPT: Training a GPT from Scratch
Lazy loaded image 8.2 Instruction Fine-Tuning Qwen3-0.6B: A Minimal Working Example
Lazy loaded image 8.3 OpenRLHF: SFT Qwen3-0.6B and Code Analysis
Lazy loaded image 8.4 OpenRLHF: Reward Model Training and Code Analysis
Lazy loaded image 8.5 OpenRLHF: PPO Tutorial and Code Analysis
🌴 LLM Must-read Papers
Lazy loaded image 9.1 Technical Reports Explained
Lazy loaded image 9.2 MedLLM
Lazy loaded image 9.3 o1 Techinical Paper
Lazy loaded image 9.4 Prompting
Lazy loaded image 9.5 LLM-as-Judge
🌳 LLM Q&A
Lazy loaded image 10.1 Why GRPO training loss starts at 0
Lazy loaded image 10.2 VLM-R1 Reproduction Results
Lazy loaded image 10.3 KL(P||Q) or KL(Q||P)?
Lazy loaded image 10.4 Left Padding or Right Padding?
Lazy loaded image 10.5 What is QK-Norm?
Lazy loaded image 10.6 Why LLM training rarely adopts Dropout?
Lazy loaded image 10.7 Dropout and DropPath
🐝 VLM Image Encoders
Lazy loaded image 11.0 Introduction
Lazy loaded image 11.1 Vision Transformer
Lazy loaded image 11.2 CLIP
Lazy loaded image 11.3 BLIP
Lazy loaded image 11.4 EVA
Lazy loaded image 11.6 BLIP-2
Lazy loaded image 11.7 SigLIP
📝 MISC.
🤔 Large Language Models Pass the Turing Test.
Lazy loaded image OpenAI ChatGPT Progression
⏳ Claude Sonnet 4 Test with Sandtris
🔍 What DeepSeek-R1's Surge Teaches Us About SEO
💰 CivitAI’s Payment Issue
🎓 arXivSub, an Academia Assistant