AI Research Radar – arXiv + Hugging Face daily papers + trending models

One ranked, de-duplicated feed of what is new in AI for your topic:

• arXiv (official API): new papers by category, keyword and date window.
• Hugging Face daily papers: community upvotes, GitHub repo and stars, project page, HF AI summary and keywords.
• Hugging Face trending models: by task (text-generation, image-text-to-text, ASR…), with licence, likes and downloads.

An arXiv paper that is also featured on HF becomes one row carrying both arXiv metadata and the HF community signals.

Ranking

radarScore = HF upvotes + √GitHub stars + 5 × keyword-in-title + 2 if code + recency bonus. The formula is transparent and reproducible. You can also set rankBy: newest.

Typical uses

• A weekly "what to read" digest for an AI team, sent to Slack or e-mail (onlyNew: true on a schedule).
• Tracking a niche such as ["speculative decoding", "KV cache"] or ["tool use", "agent"].
• Scouting open-source releases (onlyWithCode: true) and trending open-weight models.

Optional AI digest (bring your own key)

You get a headline, up to 5 themes and why they matter, must-read papers with practical takeaways, models to watch and a short Markdown digest. It works with any OpenAI-compatible endpoint.

Pricing (pay per event)

• Paper: $0.002
• Trending model: $0.002
• AI digest: $0.03 per run

Sources & etiquette

arXiv metadata is CC0. The Actor waits at least 3 s between arXiv requests, as arXiv's API terms ask, and links to papers rather than redistributing PDFs. Thank you to arXiv for use of its open access interoperability. Hugging Face data comes from the public Hub API with no login. Model weights remain under each model's own licence.

Try it in one click

These ready-made example tasks show real output. Open one, click Try for free and you get your own copy to edit and run:

• Track new AI agent, tool-use and RAG research papers
• Monitor new LLM reasoning and reinforcement learning papers

Sample output

Real rows from a recent run (some fields trimmed). The full row schema is shown in the Output tab.

[
  {
    "rank": 1,
    "radarScore": 140.41,
    "title": "Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents",
    "published": "2026-10-24",
    "hfUpvotes": 130,
    "githubRepo": "https://github.com/yihaohu0118/Trajectory2Gym",
    "githubStars": 2,
    "hfAiSummary": "Trajectory2Gym improves tool-use training by constructing interactions with compositional coverage and decision grounding rather than collecting successful traj…",
    "matchedKeywords": [
      "agent"
    ],
    "absUrl": "https://arxiv.org/abs/2610.33568",
    "hfUrl": "https://huggingface.co/papers/2610.33568"
  },
  {
    "rank": 2,
    "radarScore": 118,
    "title": "StreamVibe: Proactive Video Agents for Evolving User Intent",
    "published": "2026-10-26",
    "hfUpvotes": 110,
    "githubRepo": null,
    "githubStars": null,
    "hfAiSummary": "StreamVibe introduces a benchmark, streaming vision-language model, and hierarchical memory harness to enable proactive, intent-aware interaction with implicit…",
    "matchedKeywords": [
      "agent"
    ],
    "absUrl": "https://arxiv.org/abs/2610.34662",
    "hfUrl": "https://huggingface.co/papers/2610.34662"
  }
]

How much does it cost?

You pay only for results (pay per event). There is no separate compute charge, and you can cap any run with Max total charge.

Scenario	Cost
20 papers + 5 trending models (one example run)	25 × $0.002 = $0.05
Daily feed of the top 20 papers	600 × $0.002 = $1.20 / month
Weekly AI digest on top (your own LLM key)	4 × $0.03 = +$0.12 / month

A tiny "Actor start" event (about $0.00005) is also charged once per run.

Use it from your code or tools

Run it and get the results in one call:

curl -X POST "https://api.apify.com/v2/acts/Tendoyo~ai-research-radar/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"keywords": ["speculative decoding", "KV cache"], "daysBack": 7, "onlyNew": true}'

from apify_client import ApifyClient
client = ApifyClient("<APIFY_TOKEN>")
run = client.actor("Tendoyo/ai-research-radar").call(run_input={"keywords": ["speculative decoding", "KV cache"], "daysBack": 7, "onlyNew": true})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

To get alerts, add a Schedule (for example daily at 07:00) with onlyNew: true where available. Then connect Slack, e-mail, Google Sheets, Make, Zapier, n8n or a webhook in the Integrations tab. It also works with AI agents through the Apify MCP server.

FAQ

How is this different from arXiv e-mail alerts? arXiv alerts list everything in a category. This Actor merges arXiv with Hugging Face community signals (upvotes, GitHub stars, code links), filters by your keywords, de-duplicates and ranks, so you read the 10 papers that matter.

Can I send it to Slack every morning? Yes. Add a schedule with onlyNew: true and connect the Slack integration, or turn on the AI digest and post the Markdown digest.

Can I see only papers with code? Set onlyWithCode: true.

Do you redistribute PDFs? No. Rows contain metadata and links; PDFs stay on arXiv under their own licences.

Support

Questions, bugs or a source you'd like added: open an issue on the Actor page or e-mail youcanneverhacktendoyo@gmail.com. Maintained by Tendoyo.