The Research Engine That Thinks With You

ArXiv Intelligence

Scientific discovery moves faster than ever. ArXiv Intelligence gives you a brain-upgrade for navigating it.

This isn't another paper reader. It's a full-stack research intelligence layer: discovery, analysis, collaboration, media, and knowledge organization — all powered by frontier AI.

Researchers use it to stay ahead
Students use it to understand faster
Innovators use it to spot opportunities

Get Started

Sign in to access all features

Why People Love It

ArXiv Intelligence reads papers the way you wish you could at 3× speed: patiently, critically, and with an eye for the good ideas hiding inside the math.

Smart Discovery

Type a keyword, pick a category, or just explore what's trending — the system pulls fresh papers directly from arXiv, sorts them by relevance, and learns your research tastes along the way.

🧠

Complete Analysis

Every analysis gives you a crisp summary, methods, results, critiques, applications, monetization angles, and related references — the whole scaffolding of understanding, laid out cleanly.

💬

Collaborative Studio

Comment threads around papers, collaborative projects, AI dialogues you can fork and build on, neatly organized folders, saved queries, and personal notes. It's a place where reading turns into doing.

💡

Rich Media Generation

When text isn't enough, you can generate images, audio breakdowns, or video explainers — perfect for presentations, teaching, or pitching an idea.

ArXiv Intelligence Dashboard - AI-powered research paper analysis interface
Live Demo

What It Feels Like to Use

Paper Analysis View - Deep AI-powered research paper insights

You sign in, and a dashboard greets you with the intellectual weather report: trending papers, active discussions, recommended topics, and your own research streams.

Pick a paper. Hit "Analyze." In under a minute, you have a structured understanding and a jumping-off point for deeper thought.

Start a discussion, ask the AI to critique a method, add the paper to a project, or spin off a visual concept.

Follow a paper or author and get nudged when something new lands.

Bit by bit, you build a living knowledge system around your interests — not a static library, but an evolving map.

Who It's For

👥

Researchers

who want a sharper, faster workflow

📖

Students

struggling with dense literature

📈

Entrepreneurs

hunting for scientific edges

📁

Teams

building projects that depend on staying updated

🎯

Anyone

who wants to turn 'I should read more papers' into action

Why It Matters

Science is exploding in volume. The gap between "published" and "understood" is growing. ArXiv Intelligence exists to shrink that gap — making discovery more human, more collaborative, and more creatively charged.

It becomes not just a research assistant, but a place where fresh ideas collide, where conversations spark, and where collaboration can form naturally around the world's most interesting problems.

This is the future of reading papers:

faster insight, deeper understanding, more connected minds.

Ready to Get Started?

Join researchers, students, and innovators who are already using ArXiv Intelligence to stay ahead.

Latest Research Papers

Discover the latest AI research from arXiv

View All Papers

When RL Meets Adaptive Speculative Training: A Unified Training-Serving System

This paper proposes a unified training-serving system that integrates reinforcement learning (RL) with adaptive speculative training. The approach aim...

By: Junxiong Wang, Fengxiang Bie, Jisen Li, Zhongzhu Zhou, Zelei Shao, Yubo Wang, Yinghui Liu, Qingyang Wu, Avner May, Sri Yanamandra, Yineng Zhang, Ce Zhang, Tri Dao, Percy Liang, Ben Athiwaratkun, Shuaiwen Leon Song, Chenfeng Xu, Xiaoxia Wu2026-02-09
Read More

DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos

DreamDojo introduces a generalist robot world model learned from large-scale human videos, enabling efficient reinforcement learning of robotic polici...

By: Shenyuan Gao, William Liang, Kaiyuan Zheng, Ayaan Malik, Seonghyeon Ye, Sihyun Yu, Wei-Cheng Tseng, Yuzhu Dong, Kaichun Mo, Chen-Hsuan Lin, Qianli Ma, Seungjun Nah, Loic Magne, Jiannan Xiang, Yuqi Xie, Ruijie Zheng, Dantong Niu, You Liang Tan, K.R. Zentner, George Kurian2026-02-09
Read More