2.4 million scientific papers Now LLM-ready
Figures become extracted values.
Equations become explanations.
Claims become searchable.
How ArXiParse transforms papers
Before
Papers as documents
- PDFs that LLMs can't truly read
- Figures are opaque images
- Equations are LaTeX symbols
- Claims buried in dense prose
- One paper = 30 minutes of focus
After
Papers as structured data
- Semantic content LLMs can reason about
- Figures → values with full context
- Equations → plain English
- Claims extracted and linked to evidence
- 100 papers = same cognitive load
What this enables
Research at a scale humans can't do alone
Cross-paper analysis
"Find all papers where observed β contradicts predicted β" — across thousands of papers, one query.
Hidden gems surface
Obscure papers with 3 citations become discoverable. Their claims are searchable, not buried.
Research for everyone
Feed output to Claude. Ask "explain like I'm a PM." No PhD required to understand.
How it works
Three layers of understanding
-
Parse
Extract structure from ar5iv HTML. Sections, equations, figures, tables — identified and linked. Pure parsing, no LLM.
-
Enhance
Language model extracts claims, explains equations in plain English, identifies findings vs. predictions.
-
Interpret
Vision model reads figures. Extracts data points, trends, values. Cross-references against text claims.
Proof of depth
We understand papers well enough
to catch their errors
Discrepancy detection isn't the product — it's proof we actually understand the content.
arXiv:2403.00001
Metabolic scaling in small life forms
Discrepancy detected
- Figure 3
- β = 0.33 ± 0.02
- Section 4.2
- β = 0.73
Figure shows prokaryotes only; text discusses combined dataset. Both values may be correct — scope mismatch flagged for review.
2.4M
papers accessible
~2m
per paper
3
AI models per parse
Free
10 papers/day
Use cases
What you can ask
"What does the research actually say about X?"
Parse 20 papers, feed to Claude, get synthesis with actual numbers from the figures.
"Are there contradictions in this literature?"
Extract claims from multiple papers, compare. Paper A says β=0.33, Paper B says β=0.73. Why?
"Explain this paper like I'm not a physicist"
Feed enhanced output to any LLM. Equations already explained. Figures already interpreted.
"Does this data actually support the conclusions?"
Cross-reference claims against figure data. See if the numbers align.
Parse your first paper
Free · 10 papers/day · Results in ~2 minutes
Free, 10 papers per day, results in approximately 2 minutes
Quick access
On any arXiv page, swap the domain: