← machinelearningpapers.com
Agent Skills
How to use this site with AI agents (Claude, ChatGPT, etc.) for ML research
What This Site Is
machinelearningpapers.com is a continuously updated database of
ML papers fetched daily from arXiv, HuggingFace, and Reddit — analyzed by LLM for relevance and impact.
Each paper gets a structured analysis: relevance reasoning, methodology assessment, limitations,
broader impact, and an impact score (0–100).
The Best of All Time page is a hand-curated collection of landmark papers
with significance notes. The main index shows papers from the last 14 days with impact score ≥ 84.
audio
vision
llm
interpretability
general_ml
robotics
systems
bio
Using With an AI Agent
The easiest way to use this site with Claude or another agent is to share a page URL and ask it
to fetch the content. Every page is plain HTML — the agent can read paper titles, abstracts,
scores, and analysis fields directly.
Pages you can share:
- machinelearningpapers.com — recent high-impact papers (last 14 days, score ≥ 84)
- machinelearningpapers.com/best.html — landmark / best-of-all-time papers
Example Prompts
Literature review before starting a project
Fetch https://machinelearningpapers.com/best.html and https://machinelearningpapers.com and give me an overview of the current state of the art in [your area]. Focus on papers in the [audio/vision/llm/etc] category.
Staying current with the field
Fetch https://machinelearningpapers.com and summarize the most impactful papers from the past two weeks. Which ones are most relevant to [your problem]?
Finding prior work on a specific problem
Fetch https://machinelearningpapers.com/best.html and search for papers related to [your problem: e.g. "training stability", "audio generation", "mechanistic interpretability"]. What approaches have been tried and what are their limitations?
Understanding a paper's significance
Fetch https://machinelearningpapers.com/best.html — find [paper name or arXiv ID] and explain its contribution, methodology, and what papers it influenced.
Debugging a training issue
Fetch https://machinelearningpapers.com/best.html and https://machinelearningpapers.com — find papers about [e.g. "catastrophic forgetting", "training instability", "reward hacking"]. What solutions have been published and what are the tradeoffs?
Impact Score
Each paper is scored 0–100 from three components:
- institution_score (0–10): author institution prestige as a weak prior
- novelty_score (0–40): novelty of the contribution
- technical_impact_score (0–50): expected field impact based on methodology and results
Score ≥ 84 → main feed (roughly 1–5 papers/day). Landmark papers bypass thresholds entirely.
Browsing the Site Directly
- Main feed — last 14 days, impact score ≥ 84, filterable by category tab
- Best of All Time — landmark papers; click to expand full analysis
- Each paper card has: abstract preview, relevance reasoning, methodology assessment, limitations, broader impact, and arXiv PDF link.