@akshay_pachaar: Turn any paper into running code. Just swap arxiv → autoarxiv in the paper url. That hands the paper to an AI agent fro…

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ai-agent reproducibility arxiv research paper-implementation automation

Summary

autoarxiv lets you turn any arxiv paper into running code by simply changing the URL to autoarxiv.org. An AI agent from alphaXiv reads the paper, clones the repo, sets up dependencies, and runs a minimal reproduction to verify claims, logging everything live.

Turn any paper into running code. Just swap arxiv → autoarxiv in the paper url. That hands the paper to an AI agent from alphaXiv. It reads the abstract, the claims, and the linked GitHub repo, then clones the codebase and works through the usual setup pain like dependencies, broken paths, environment config, and hardware assumptions. From there it designs a minimal reproduction. That means a smaller model, fewer steps, and a single GPU instead of a cluster, scaled down just enough to test whether the headline claim holds. The whole run is live and fully logged. Loss curves, metrics, and training progress are all observable as it happens. What comes back is a clean signal on whether the minimal run matches the paper's reported result, plus an estimate of what a full replication would cost in compute and time. A lot of research code dies in setup before anyone verifies a single number. This moves reproduction from a weekend of debugging to a url change. Pick a paper and try it now. video credits: @askalphaxiv
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Cached at: 06/22/26, 01:30 AM

Turn any paper into running code.

Just swap arxiv → autoarxiv in the paper url.

That hands the paper to an AI agent from alphaXiv. It reads the abstract, the claims, and the linked GitHub repo, then clones the codebase and works through the usual setup pain like dependencies, broken paths, environment config, and hardware assumptions.

From there it designs a minimal reproduction. That means a smaller model, fewer steps, and a single GPU instead of a cluster, scaled down just enough to test whether the headline claim holds.

The whole run is live and fully logged. Loss curves, metrics, and training progress are all observable as it happens.

What comes back is a clean signal on whether the minimal run matches the paper’s reported result, plus an estimate of what a full replication would cost in compute and time.

A lot of research code dies in setup before anyone verifies a single number. This moves reproduction from a weekend of debugging to a url change.

Pick a paper and try it now.

video credits: @askalphaxiv

Web scraping will never be the same.

(100% open-source visual search at scale)

PixelRAG is a retrieval system that skips HTML parsing completely.

Instead of scraping a page into text and embedding chunks, it screenshots the page and retrieves the image. A vision-language model reads the answer straight off the pixels.

Why that matters: parsing is where web RAG quietly loses information.

  • A single HTML-to-text parser can drop 40%+ of a page.
  • Tables, charts, and layout get flattened or thrown out.
  • Swapping parsers alone can move accuracy ~10 points on the same docs.

PixelRAG indexes the page a person actually sees. The team built a visual index of all of Wikipedia, 30M+ screenshots, and it still beats the strongest text RAG baseline by 18.1% on text-only QA.

The repo also ships a Claude Code plugin that gives Claude eyes.

It lets Claude screenshot any URL and read the rendered page instead of scraping the DOM. So you can hand it a live page, an arXiv paper, or your local site and ask what it actually looks like.

One setup script. No MCP server, no backend.

How the pipeline works:

  • Renders each document (web, PDF, image) to image tiles.
  • Embeds them with Qwen3-VL-Embedding, LoRA fine-tuned on screenshots.
  • Builds a FAISS index and serves a search API.

A stronger reader model lifts accuracy with no re-indexing, since the index is just pixels.

Everything is open-source under Apache-2.0.

GitHub repo: https://github.com/StarTrail-org/PixelRAG…

Talking about RAG, I recently wrote an article on a new approach that makes retrieval much more efficient by cutting corpus size by 40x, reducing tokens per query by 3x, and improving vector search relevance by 2.3x.

The article is quoted below.

@akshay_pachaar honestly, this is a game changer for devs. less setup headache means more time coding.

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