PaperPulse

Every day, arXiv posts hundreds of new papers. PaperPulse picks the five that actually matter to you, tells you whether to trust them, and explains why — all in plain English.

If this saves you from reading a paper that wasn't worth your time, a ⭐ helps other researchers find it too.

📄 See a real digest generated today →  ·  🗺️ Roadmap  ·  🧪 Annotated example

What it actually does

1. Ranks papers by what you care about. You describe your interests in a sentence. PaperPulse ranks today's papers against that, and gets smarter every time you say "more like this" / "less like this."
2. Flags papers that look shaky. No error bars, no code, hyped-up language, results that only hold on one dataset — PaperPulse scans for the patterns that usually mean weaker work, and shows you exactly which words triggered the flag.
3. Remembers what you've already ruled out. Log a topic as "dead end, already tried" once, and PaperPulse will flag it every time it resurfaces — even in a different country's market or a different dataset.

No API keys, no paid services, no sign-up. It works offline out of the box.

pip install paperpulse
paperpulse init      # 30-second setup wizard
paperpulse run       # today's digest -> digests/YYYY-MM-DD.md

Finance papers get an extra "alpha card"

If a paper claims to find a trading edge, PaperPulse pulls out the specifics: what it's predicting, what data it used, what numbers it reported, and over what time period. A card can be:

• Strong — data, numbers, and dates are all there. You could try to reproduce it.
• Vague — the paper talks about markets but gives you nothing to check. This is the important case: it looks credible until you notice there's nothing underneath.

paperpulse alpha                       # today's papers as alpha cards
paperpulse alpha --full-text           # also reads the actual PDF, not just the abstract (needs: pip install "paperpulse[pdf]")

The options-backtest demo — read this before you install it

paperpulse backtest is a teaching tool, not a trading tool. It runs a real backtesting engine (optopsy) on made-up, synthetic data, so you can see how an options strategy is actually tested — how strikes get picked, what a win/loss report looks like, why even a "good" strategy loses money sometimes.

It does not:

• verify whether any paper's claim is true
• use real market data
• connect to a broker or place any trade
• give you financial advice of any kind

Think of it as a flight simulator for reading finance papers, not an autopilot. It's an optional extra (pip install "paperpulse[backtest]", needs Python 3.12+) — it's off by default and the rest of PaperPulse doesn't need it.

Using it

# Command line
paperpulse run --categories cs.LG cs.CL

# Scripting / automation -- structured JSON instead of markdown
paperpulse run --format json | jq '.papers[] | select(.trust.badge == "clean")'

# Straight into your reference manager
paperpulse export -o today.bib      # BibTeX -> Zotero, Mendeley, EndNote, LaTeX

# As a Python library
python -c "from paperpulse.pipeline import run_digest; from paperpulse.config import Config; \
           print(run_digest(Config(), dry_run=True).markdown)"

# Web dashboard (built in, no extra install)
paperpulse serve            # open http://127.0.0.1:8000

The dashboard has clickable topic filters, a settings panel to change what you're tracking without touching a config file, and renders any math in an abstract properly instead of showing raw symbols.

Or with Docker: docker compose up

Why not just ask an LLM to summarize arXiv?

More features

• Multiple sources — arXiv, bioRxiv, medRxiv, PubMed, SSRN
• Finds contradictions — flags pairs of recent papers that disagree with each other, and surfaces "What changed this week" in the digest itself
• BibTeX export — paperpulse export drops today's digest straight into Zotero, Mendeley, EndNote, or a LaTeX bibliography
• JSON output — paperpulse run --format json for scripts, dashboards, and cron jobs that want structured data, not markdown to parse
• Compares to your own code — paperpulse similar my_model.py finds papers closest to what you're already working on
• Live prices for finance papers — if a paper mentions the S&P 500, Bitcoin, etc., the dashboard shows the current price next to it (no API key needed)
• Notes — jot down your own thoughts against any paper and keep them
• Delivery — save to a file, email, RSS feed, or post to Slack/Discord
• Shared trust database — a team can pool their trust scores in one place, and measure whether the trust badge is actually predictive of what people like (paperpulse score-accuracy)

Configuration

paperpulse init writes a paperpulse.yaml you can edit by hand — see paperpulse.example.yaml for every option, commented.

API keys and passwords always go in environment variables, never in the config file: OPENAI_API_KEY / ANTHROPIC_API_KEY (only needed if you want AI-written summaries instead of the free built-in ones), PAPERPULSE_SMTP_* (email), PAPERPULSE_SLACK_WEBHOOK / PAPERPULSE_DISCORD_WEBHOOK, NCBI_API_KEY, PAPERPULSE_API_TOKEN (if set, every dashboard/API request — reads included — must carry it as a Bearer token).

Security

• All outbound fetches that check a host allowlist (PDF downloads, code/data link checks) go through a shared no-redirect opener, so an allowlisted host can't be used to bounce a request off-host.
• SQLite access is fully parameterized; config is loaded with yaml.safe_load.
• PAPERPULSE_API_TOKEN gates every route except the static dashboard shell itself, which has nothing to protect.
• Found a real issue? Please open an issue rather than a public PR with exploit details.

Scheduling

Run it daily with cron, or use the included GitHub Action to generate and commit a digest every weekday morning (.github/workflows/digest.yml).

Contributing

pip install -e ".[dev]"
pytest

Tests run fully offline — no network, no API keys required. See ROADMAP.md for what's built and what's planned.

License

MIT — see LICENSE.