Ship papers withless frictionand a calmer flow.
Draft and revise LaTeX through a clean chat loop, attach context from PDFs and links, and keep the source editable at every step.
Preview
paper • sections • math
Untitled
Preview
Abstract
We sketch a compact intuition for contrastive representation learning and provide a clean scaffold for quick iteration.
Equation
Idea
Pull positive views together and push negatives apart.
Objective
\[ =-(z_i,z_j)/)}{_k((z_i,z_k)/)} \]
latex-ai • demo
Interactive mini workspace
Local simulation — no external calls. Designed to feel like the real product.
Prompt
From outline → clean LaTeX
mode: Draft
Generate to preview the flow
Output
Chat-style summary + copy-ready LaTeX
Summary
Generate to render the preview and LaTeX.
StructureClarityConsistency
Draft a 1-page research note on contrastive learning with a short abstract, 2 sections, and one equation.
Preview
29 lines
Generate to render a document preview.
**Local demo** (no AI calls)
Drafted a clean scaffold from your intent.
**What this simulates**
- Chat-driven drafting and editing
- Reference scaffolding (citations / structure)
- Real-time LaTeX output you can copy into the editor
**Your prompt**
> Draft a 1-page research note on contrastive learning with a short abstract, 2 sections, and one equation.
```latex
\documentclass[11pt]{article}
\usepackage{amsmath,amssymb}
\title{A Minimal Research Note on Contrastive Learning}
\author{Anonymous}
\date{}
\begin{document}
\maketitle
\begin{abstract}
Contrastive learning trains representations by pulling related views together while pushing unrelated samples apart.
This note sketches a compact intuition and a clean LaTeX scaffold for quick iteration.
\end{abstract}
\section{Idea}
Given two augmented views of the same sample, we maximize agreement in embedding space.
\section{Objective}
Let $z_i$ and $z_j$ be embeddings of a positive pair and $\tau$ a temperature.
The InfoNCE loss is:
\[
\mathcal{L} = -\log\frac{\exp(\mathrm{sim}(z_i,z_j)/\tau)}{\sum\limits_{k}\exp(\mathrm{sim}(z_i,z_k)/\tau)}
\]
\end{document}
% Prompt (local demo):
% Draft a 1-page research note on contrastive learning with a short abstract, 2 sections, and one equation.
```
Features
A LaTeX workflow that stays readable
Draft, edit, and keep structure stable — with context from files and links when you need it.
Chat-native drafting
Turn a prompt into a compilable scaffold (title, abstract, sections) — fast and clean.
Example
“Write the abstract + 2 sections.”
→ structured LaTeX scaffold
→ iterate with small edits
Editor-first iteration
Keep the source visible. Make a local edit. Preserve the rest of the document.
Rapid revision loops
Ask for a change in one place (tone, math, layout) without rewriting everything.
PDF + file context
Drop a PDF and pull definitions or snippets into your draft without losing structure.
Web context ingestion
Bring a link, extract what matters, and write with fewer tabs open.
Ready-to-export output
Copy LaTeX, compile when you’re ready, and keep the output predictable.
Workflow
Small steps, stable structure
The goal isn’t magic — it’s fewer rewrites, cleaner diffs, and predictable LaTeX.
01
Draft
Start from intent. Get a clean scaffold you can compile and extend.
02
Ground
Attach PDFs or links only when needed — keep the core draft simple.
03
Refine
Make local changes that preserve structure: one paragraph, one section, one equation.
Open the workspace
Start a session, try the editor, and iterate on structure without the noise.