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Paper → 3D simulation
Turn complex research into interactive visual models.
Visual learning for AI research
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Turn papers, ideas, and code into interactive 3D lessons, useful explanations, and runnable experiments.
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T2
Math
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T3
Silicon
Code and runnable simulations
PAPERFrom text to interactive 3D simulations.
CONCEPTSTo knowledge graphs and visualizations.
REPOSTo runnable code and live simulations.
PAPERFrom text to interactive 3D simulations.
CONCEPTSTo knowledge graphs and visualizations.
REPOSTo runnable code and live simulations.
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See KXAI in action, grounded in the papers below, never invented
- Architecture, broken down: Ask what a paper's system looks like and get the real components, pulled from its Architecture tab, not a generic summary.
- Results you can check: Ask for the key numbers and get the actual benchmark results, cited back to the Cheatsheet tab.
- Gaps and what's next: Ask what's missing or what to read next and get the paper's own stated limitations, plus the paper it leads to.
One AI workspace. Six ways to learn.
Go from papers to understanding with interactive visualizations, AI guidance, and hands-on simulations, all in one place.
02
Three-tier AI tutor
Learn at your level, story, math, or silicon.
03
Ask the paper
Get grounded answers from the source, with citations.
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Quizzes & flashcards
Reinforce understanding with adaptive practice.
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Code walkthroughs
Step through implementations with AI guidance.
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Discover emerging topics and connect the dots.
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Ask KinetixAI
Ask a question.
See where the answer came from.
What is the difference between Mixture of Agents and Mixture of Experts?
KinetixAI · Story level
Mixture of Experts happens inside one model. A router sends each token to a few expert layers 1
Mixture of Agents uses several separate models, then combines their answers 2
Think of one server with an internal load balancer, versus a group of servers that vote.
Open the simulationShow me the mathQuiz me
Stays on topic: AI, ML, systems, maths and quantum. Uses your Research RAG when you are signed in.
Researcher search
Find a researcher.
Open their work in 3D.
Search by Google Scholar link, ORCID iD or name. Open any paper as a simulation, or add the whole list to your Research RAG.
AB[N] citations
[Researcher name]
[University] · [Field]
[Paper title] · [Venue, year]VisualizeAdd to RAG
[Paper title] · [Venue, year]VisualizeAdd to RAG
Research RAG for your own papers.
Your papers, notes and repos in one searchable place. Equations and code stay intact, and every answer shows its source.
01ConnectPDFs, arXiv links, notes and GitHub repos
02ParseEquations, tables, figures and code kept whole
03IndexVector and keyword search together
04RetrieveRanked by relevance, recency and citations
05AnswerReplies that cite the exact source
- Cited answersEvery claim links to a page, section or line.
- Stays up to dateRe-indexes when you push to GitHub. No manual uploads.
- Private by defaultYour files stay in your workspace and are never used for training.
- Safe retrievalChecks for prompt injection and poisoned documents.
- Share with your labTeam workspaces with roles and access control.
Example answer
“Where does my FlashAttention note differ from the paper?”
Your note fixes K and V blocks at 64 rows 2. The paper sizes each block to the available SRAM, so it changes with the GPU 1. Your reduction step does match the paper's online softmax 1.
1Dao et al., FlashAttention §3.1
2Your notes, flash-attention.md, line 42
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