ardive 0.1.3

Command-line agent that pulls papers from arXiv and summarizes / explains them with a local open-source model.

arDive: a simple dive into your ArXiv

A small command-line agent that pulls papers from arXiv and uses Llama3.2 to summarize, explain, compare, and digest them. Anyone can easily install and use without the need of a paid plan.

Install

# 1. Install Ollama (free, runs models locally): https://ollama.com
ollama pull llama3.2        # or any open model you like

# 2. Install arDive
pip install ardive

That's it. arDive talks to the local Ollama server. Pick a different model with ARDIVE_MODEL (e.g. export ARDIVE_MODEL=qwen2.5), or point at a remote Ollama with OLLAMA_HOST.

From source

git clone https://github.com/rohankosalge/arDive
cd arDive
pip install -e .

Usage

# Summarize a paper as bullet points
ardive sum 1234.56789

# Focus on one section, cap the bullets
ardive sum 1234.56789 --section methodology --max-bullets 5

# Explain like I'm 5 (works on every command)
ardive sum 1234.56789 --eli5

# Compare two or more papers
ardive comp 1234.56789 9876.54321

# Digest a topic (searches arXiv, default 8 papers)
ardive dig "diffusion models for protein folding"
ardive dig "graph neural networks" -n 12

Commands

Command	What it does
sum <id>	Bullet-point summary of one paper (full PDF text).
comp <id> <id> [...]	Compare two or more papers.
dig <topic>	Search arXiv by topic and digest the top results.

Flags

• --eli5 — explain in plain, jargon-free language (all commands).
• --section {abstract,intro,methodology,related,citations} — sum only; focus on one section.
• --max-bullets N — sum only; cap the number of bullets (positive integer).
• -n/--num N — dig only; how many papers to pull (default 8).

How it works

sum and comp download each paper's PDF and extract its full text; dig searches arXiv and works from abstracts. The text is sent to a local open-source model via Ollama (default llama3.2) with a prompt tailored to the command. In a terminal the bullet-point response is rendered as formatted Markdown inside a box; when piped or redirected (e.g. ardive sum 1234.56789 > out.md) it's written as plain Markdown so the file stays clean.

Long papers can exceed the model's context window and be truncated. arDive asks Ollama for an 8192-token window by default; raise it (at the cost of more RAM) with export ARDIVE_NUM_CTX=16384.

Speed

Summaries run entirely on your machine, so wall-clock time is dominated by the model. A few levers:

• Model choice is the biggest one. Smaller models are much faster. Try export ARDIVE_MODEL=llama3.2:1b or export ARDIVE_MODEL=qwen2.5:3b; 7B+ models are noticeably slower on full papers. (Default is llama3.2, ~3B.)
• Abstract is near-instant. ardive sum <id> --section abstract skips the PDF download and summarizes just the abstract.
• First run is slowest. It loads the model into memory; arDive keeps it warm for 15 min afterward (tune with ARDIVE_KEEP_ALIVE), so repeat runs are quicker.
• Smaller asks finish sooner. --max-bullets N shortens the output, and a lower ARDIVE_NUM_CTX trades context for speed.