Reviving Papers with Code

Hi,

Niels here from the open-source team at Hugging Face. Like many others, I was a huge fan of paperswithcode, a website which allowed to easily find the state-of-the-art (SOTA) across any domain of AI, from computer vision to language models to time-series forecasting. Sadly, that website is no longer maintained after its acquisition by Meta.

Hence, I've been working on reviving it. I obviously use AI agents to parse papers at scale and automatically generate leaderboards (for now I'm the one verifying results). So far, I've only parsed high-impact papers for which I know they're SOTA, like Qwen 3.5 and 3.6, RF-DETR for object detection, DINOv3, SOTA embedding models from the MTEB leaderboard, the Open ASR Leaderboard for automatic speech recognition models, etc.

For now, it includes the following:

> trending papers by default based on Github star velocity

> categorization by domain, e.g., [OCR](https://paperswithcode.co/tasks/ocr)

> methods, popular techniques used across AI papers, which PwC used to have as well, like [RLVR](https://paperswithcode.co/methods/rlvr) and

> eval results for high-impact papers, see e.g., Qwen 3.5 at the bottom

> leaderboards for each domain, e.g., MMTEB or COCO val 2017

> conferences, like [CVPR 2026](https://paperswithcode.co/conferences/cvpr-2026)

> support for citation counts (you can also see the most cited papers by domain!)

> automated linked Github, project page URLs, and artifacts (+ multiple repos are supported on a paper page)

> support for external papers beyond Arxiv, see e.g., [DeepSeek v4](https://paperswithcode.co/paper/82956)

> Harness reports for coding agent benchmarks, e.g., Terminal Bench

> "Sign in with HF" and Storage Buckets are used to store humbnails, paper PDFs, and overall data backups.

I'm curious about your feedback + feature requests!

Try it at https://paperswithcode.co

This is a bit off-topic (though tangentially related) - does anyone remember a similar blog where the author would do something like a "5 minute paper" review, i.e. they'd discuss findings and try to communicate the main point? It was usually a paper per week, mostly CompSci / maths papers IIRC

Yes please! I have been frustrated with the state of object detection models especially. Everyone claims SOTA. So you end up having to test manually to find out which one actually is. And unlike LLM's, it should be pretty easily quantifiable.

Is there an RSS feed?

Bring it back! Sing it back!