PaperLens: Read Research Papers with an Open-Weight Model Running on Your Own Machine

Quan Zhan — Mon, 19 Oct 2026 07:22:08 +0000

The problem

I'm an undergraduate student, and I read a lot of papers — optimization algorithms, optics, whatever my coursework drags me into. The bottleneck isn't finding papers, it's digesting them: a 12-page PDF takes me an evening, and half of that evening goes to re-reading the method section.

Proprietary AI APIs would solve this, but they charge per token and — worse for a student — they'd see every unpublished manuscript or thesis draft I upload. I wanted a paper-reading assistant where the intelligence is free and the data never leaves my laptop.

What I built

PaperLens: a ~130-line Python CLI. Point it at a PDF, get back a structured Markdown report:

TL;DR — problem + headline result in two sentences
Key contributions — bullet list
Method — explained in plain language
Key results — the numbers that matter
Reproduction notes — what you'd need to reproduce it, with honest "unclear" flags instead of invented details

pip install -r requirements.txt

# Fully local: data never leaves your machine
ollama pull qwen3:8b
python paperlens.py paper.pdf -o report.md

# Or Groq Cloud free tier (open-weight Llama, no credit card needed)
export GROQ_API_KEY=your_key_here
python paperlens.py paper.pdf --backend groq -o report.md

How it works

The pipeline is deliberately boring:

PDF → text (pypdf) → structured prompt → open-weight model → Markdown report

Extract — pypdf pulls raw text from the PDF (.txt also works, handy for testing).
Constrain — long papers are truncated to 12,000 characters so they fit the context window; the user is told when this happens.
Prompt — one fixed template demands exactly the five sections above and instructs the model to say "unclear" rather than hallucinate when text is missing. This is the whole "prompt engineering": narrow the job, forbid invention.
Run — three backends, all stdlib HTTP, no SDK needed:
- ollama (default): local server, zero cost, zero data egress
- groq: free-tier cloud inference of open-weight models, OpenAI-compatible
- echo: dry-run that prints the prompt — lets anyone verify the pipeline without a GPU or API key

Tech stack

Python stdlib + pypdf. That's it — one dependency. I wanted something a first-year student could read end to end in one sitting.

Why open matters here

Three reasons, all practical:

Cost. Reading 100 papers through a proprietary API is a student-budget problem. A local 8B model or a free-tier endpoint makes it $0.
Privacy. Students and researchers routinely work with unpublished manuscripts. With the Ollama backend, the paper never leaves the device — there is no trust decision to make.
Auditability. The entire pipeline fits in one short file. If the summary looks wrong, you can read the prompt and see exactly what the model was asked. Try that with a black-box reading app.

My agent session

I built PaperLens with an AI coding agent: I described the pipeline I wanted, it drafted the code, and I directed the testing — including a real PDF-extraction test with a generated PDF and an end-to-end dry run. The agent wrote fast; I kept the design boring on purpose.

Try it

Repo: https://github.com/yniantongtian-oss/paperlens — MIT licensed, PRs welcome.

Prize categories: submitted for the Hacktoberfest Open-Source AI Challenge: Week 3.

DEV Community: Quan Zhan