The visual and verifiable workspace for deeply understanding research papers.
Loved by 5,000+ researchers globally
How it works
From research paper to a map of your understanding.
Add your sources
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Search 200M+ open-access papers
Ask questions with citations
How does the attention mechanism work?
Attention turns each token into a query, key, and value, then compares the query with every key to decide which tokens matter most . Those scores become weights, and the model combines the value vectors into a context-aware representation for the current token . Multi-head attention repeats this in parallel so different heads can track syntax, meaning, and long-range links at the same time .
Map your understanding
Knowledge Map
Deconstruct a paper into its structure.
Reveal argument structure, causal effects, process flows, relationships, timelines and more with multi-level zoom.
Argument
Uncover claims, premises and evidence.
Causality
Identify cause–effect chains and dependencies.
Pros & Cons
Weigh supporting and opposing perspectives.
Process
Map methods, steps and procedural flows.
Relationships
Surface connections across ideas and entities.
Timeline
Place events and developments in sequence.
Visualize structure
Argument, causal, relationship and more.
Navigate arguments
Follow how each claim builds up.
Multi-level zoom
Zoom in to focus on a concept.
Faithful to the paper
Every concept tied back to the source.
Chat
Ask questions with grounded citations.
Multi-step reasoning across your papers, every claim explained with its source.
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Verify answers
One-click to view the cited source.
Multi-step reasoning
Trust rigorously reasoned answers.
Literature-indexed
Read in context of literature.
Understand claims
Understand argument strength.
Agentic chat
Write and organize research notes.
Follow-up questions
Dive deeper into a concept.
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Citations
Trace claims back to their source arguments.
Every citation is auto-indexed on import, with a breakdown of how it justifies the claim.
Citations that teach
Learn how citations build up into each claim.
Explain claims
Understand how sources justify claims.
Compare citations
Contrast citations for the same claim.
Auto-indexed
Enrich papers on import.
Trace the citation graph
Fetch open-access papers on click.
Read in context
Situate papers in their literature.
Semantic Map
Visualize your research from new angles.
Explore your literature from different angles and perspectives to uncover new insights.
Visualize your research from new angles.
The semantic map is a data visualization of the sources, notes and chats in your project. Enter a new angle, such as methods or limitations, to reposition and cluster around that perspective.
Note
PDF
Website
Chat
Mention
Citation
Cluster by meaning
Group related ideas into clusters.
Multiple perspectives
Project the map in different angles.
Connect ideas
See how papers connect.
Explore papers
Zoom in to dive into each paper.
Features
Designed for understanding research.
Searching for relevant papers.
Found 3 papers.
Reading 3 papers.
Attention Is All You Need.pdfBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.pdfTransformer-XL: Attentive Language Models Beyond a Fixed-Length Context.pdf
Done
Synthesize across sources
Compare findings across papers and turn the pattern into an inspectable map.
Ask across your project
Ask questions over your sources and inspect the citation trail behind each answer.
Attention Is All You Need
Page 5
Defines attention as mapping a query and key-value pairs to an output.
“An attention function can be described as mapping a query and a set of key-value pairs to an output.”
Trace every claim
Move from an answer or map node back to the exact passage that supports it.
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio
CoRR · 2014
Introduces an attention-based encoder-decoder model that soft-searches source-sentence parts while generating translations, reducing the fixed-vector bottleneck in neural machine translation.
Read citations in context
See what each cited paper contributes to the argument you are reading.
Visualize your research from new angles.
The semantic map is a data visualization of the sources, notes and chats in your project. Enter a new angle, such as methods or limitations, to reposition and cluster around that perspective.
Note
PDF
Website
Chat
Mention
Citation
Connect your research
Keep sources, notes, questions, and maps linked inside one research workspace.
Attention Notes
Attention compares queries with keys, then weights values to build context from .
According to , attention is how each token looks across the sequence before choosing what to carry forward.
Write notes with links
Turn highlights and ideas into notes that stay attached to their evidence.
Review notes and maps
Export notes and download knowledge maps when you need to use your work elsewhere.
Searching for relevant papers.
Found 3 papers.
Reading 3 papers.
Attention Is All You Need.pdfBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.pdfTransformer-XL: Attentive Language Models Beyond a Fixed-Length Context.pdf
Done
Synthesize across sources
Compare findings across papers and turn the pattern into an inspectable map.
Ask across your project
Ask questions over your sources and inspect the citation trail behind each answer.
Attention Is All You Need
Page 5
Defines attention as mapping a query and key-value pairs to an output.
“An attention function can be described as mapping a query and a set of key-value pairs to an output.”
Trace every claim
Move from an answer or map node back to the exact passage that supports it.
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio
CoRR · 2014
Introduces an attention-based encoder-decoder model that soft-searches source-sentence parts while generating translations, reducing the fixed-vector bottleneck in neural machine translation.
Read citations in context
See what each cited paper contributes to the argument you are reading.
Visualize your research from new angles.
The semantic map is a data visualization of the sources, notes and chats in your project. Enter a new angle, such as methods or limitations, to reposition and cluster around that perspective.
Note
PDF
Website
Chat
Mention
Citation
Connect your research
Keep sources, notes, questions, and maps linked inside one research workspace.
Attention Notes
Attention compares queries with keys, then weights values to build context from .
According to , attention is how each token looks across the sequence before choosing what to carry forward.
Write notes with links
Turn highlights and ideas into notes that stay attached to their evidence.
Review notes and maps
Export notes and download knowledge maps when you need to use your work elsewhere.
Use cases
Built for evidence-heavy research workflows.
Literature reviews
Turn scattered papers into a mapped literature review.
Compare papers, find recurring claims, and keep every synthesis connected to the source passages behind it.
“For a literature review, I don't need more summaries, I need to see which papers are making the same claim, which methods they depend on, where the gaps are, and whether the evidence is strong enough to carry into my own argument. Atlas makes that comparison much easier to keep straight.”
Industry Analyst
Loved by researchers
From those who map their research in Atlas
“Atlas is beautiful and thoughtfully made.
As a researcher, I really appreciate being able to explore and interrogate any source type in depth through Atlas's chat and knowledge map functions.
The citations function is SO helpful, as I get to trace the precise quotes/content from my chat conversations back to exactly where they are mentioned in each source.”
Research Assistant at Duke-NUS Global Health Institute
“Atlas is beautiful and thoughtfully made.
As a researcher, I really appreciate being able to explore and interrogate any source type in depth through Atlas's chat and knowledge map functions.
The citations function is SO helpful, as I get to trace the precise quotes/content from my chat conversations back to exactly where they are mentioned in each source.”
Research Assistant at Duke-NUS Global Health Institute
“Atlas has been a real time-saver for me. I'm not some AI expert or data scientist - I just needed a tool to help me wade through the sea of articles I come across daily. What I love about Atlas is how it gives me the gist of an article in seconds.”
“TLDR: Saves time, summarizes well. My personal favourite use case is to ask it to summarize YouTube videos so that I can decide whether to watch it. It can perform off-the-shelf RAG over multiple websites and even WhatsApp / Telegram Web chats.”
“WHAT THE HECK DID YOU CREATE. It's like an ultimate GPT. I was actually quite surprised how the AI pulled so many concepts tgt. I think it's a huge addition to many super users of GPT. Excited to use it for my own research!”
“As a software engineer in a big company that has to read lots of documentation, this helps me summarize multiple fragmented docs to get me up to speed on the domain I'm working on - in a fraction of the time.”
“Atlas is constantly trying to push the boundaries of information and interaction design using innovative visual forms.
Using Atlas has not only given me a clear mental model to synthesize and consolidate information, but also an enjoyable and tactile feedback loop to generate original ideas.”
Data Visualization Specialist at Complexity Science Hub
2x Winner of The Pudding Cup
“Atlas is constantly trying to push the boundaries of information and interaction design using innovative visual forms.
Using Atlas has not only given me a clear mental model to synthesize and consolidate information, but also an enjoyable and tactile feedback loop to generate original ideas.”
