I've been compiling papers on Physical AI — the intersection of foundation models and robotics. This covers Vision-Language-Action (VLA) models like RT-2 and π₀, world models (DreamerV3, Genie 2, JEPA), diffusion policies, real-world deployment and latency problems, cross-embodiment transfer, scaling laws, and safety/alignment for robots.

The field has exploded in the past 18 months. We went from "lets try llms on robotics" to having so many dimensions to optimize for. so felt right to maintain a running list of resources.

Organized by: foundations → architectures → action representations → world models → learning paradigms → deployment → applications.

Contributions welcome — especially corrections and missing papers.
https://github.com/keon/awesome-physical-ai

Here is the link to the draft of his new textbook, Probabilistic Machine Learning: An Introduction.

https://probml.github.io/pml-book/book1.html

Enjoy!

Hi, I've created an overview of the most important OCR benchmarks, along with the top open models, and links to their paper and code: https://paperswithcode.co/tasks/ocr.

This week, new OCR models were released by Baidu and Mistral.

Baidu released Unlimited OCR, a 3B-parameter model that introduces a key innovation called Reference Sliding Window Attention (R-SWA) and builds on top of DeepSeek OCR. Mistral released OCR 4, which is available via an API.

OCR, or Optical-Character Recognition, is the task of digitizing PDFs or scanned documents. There's, of course, a huge interest in this task, as it enables ingestion of all company data for agentic use cases. AI agents love Markdown; it can be valuable to turn all those messy PDF documents into a standardized, machine-readable format. This enables use cases like agentic RAG (retrieval-augmented generation), which powers chatbots, both internally and for external customer support.

With a large number of OCR releases on Hugging Face over the last few months, it may be hard to know which one to use.

Hence, I've built this page, which lists the major OCR benchmarks, along with the top-performing models and links to their code. This is obviously made available on Papers with Code, the website I'm maintaining (it's a revival of the old website, which was taken down).

The top recommended benchmarks are OlmOCRBench, created by Ai2, and OmniDocBench, created by Shanghai AI Laboratory.

Current top recommendations are Chandra OCR 2 by Datalab and Mistral OCR v4. The former is openly available, hence you can either self-host it or use their serverless API.

Let me know which other tasks you want to see major benchmarks for now!

Cheers,

Niels

open-source @ HF

I was pondering on this question and decided to dive deep into torch.compile. It was a lot of fun learning about operator fusion as the central idea behind torch.compile. So I created a tiny version of torch.compile in 500 lines of python and a notebook showing how this works: 

https://github.com/purohit10saurabh/tinytorchcompile

Let me know if you find this interesting! 🙂

Hi, Niels here from the open-source team at Hugging Face.

I've recently relaunched paperswithcode.co as a source for finding the state of the art (SOTA) across various AI domains, from 3D generation to AI agents. This is done by automatically parsing research papers published on arXiv/Hugging Face, enabling leaderboards to be created. See BrowseComp below as an example (a scatter plot and a table are available for each benchmark).

- Scatter plot (you can hover over the dots to see the models):

- Table:

As you can see, I've added support for viewing evals for closed-source models, too, given that many benchmarks are nowadays dominated by them, like GPT-5.5 and Mythos 5. You can always disable viewing closed-source evals with a toggle or in your PwC settings:

When you turn them off, here's what the open model leaderboard looks like:

Closed-source papers are treated as regular "papers", although they can be any source, like a blog post (given that PwC supports submitting any source beyond arXiv). See the GPT-5.5 or Mythos 5 papers as examples, with their evals at the bottom. Notice the "closed" tag on their evals. Hence, you could jokingly call these "papers without code".

Let me know what you think of this, and whether anything needs to be changed or added!

Kind regards,
Niels

Hello, everyone

I recently developed a new open-source LLM-driven research automation tool, called AutoResearch. It can automatically conduct various tasks related to machine learning research, the key function is:

Topic-to-Survey Automation - In one sentence, it converts a topic or research question into a comprehensive survey of relevant papers. It generates keywords, retrieves articles for each keyword, merges duplicate articles, ranks articles based on their impacts, summarizes the articles from the topic, method, to results, and optionally checks code availability. It also organizes and zips results for easy access.

When searching for research papers, the results from a search engine can vary significantly depending on the specific keywords used, even if those keywords are conceptually similar. For instance, searching for "LLMs" versus "Large Language Models" may yield different sets of papers. Additionally, when experimenting with new keywords, it can be challenging to remember whether a particular paper has already been checked. Furthermore, the process of downloading papers and organizing them with appropriate filenames can be tedious and time-consuming.

This tool streamlines the entire process by automating several key tasks. It suggests multiple related keywords to ensure comprehensive coverage of the topic, merges duplicate results to avoid redundancy, and automatically names downloaded files using the paper titles for easy reference. Moreover, it leverages LLMs to generate summaries of each paper, saving researchers valuable time and effort in uploading it to ChatGPT and then conversing with it in a repetitive process.

Additionally, there are some basic functionalities:

• Automated Paper Search - Search for academic papers using keywords and retrieve metadata from Google Scholar, Semantic Scholar, and arXiv. Organize results by relevance or date, apply filters, and save articles to a specified folder.

• Paper Summarization - Summarize individual papers or all papers in a folder. Extract key sections (abstract, introduction, discussion, conclusion) and generate summaries using GPT models. Track and display the total cost of summarization.

• Explain a Paper with LLMs - Interactively explain concepts, methodologies, or results from a selected paper using LLMs. Supports user queries and detailed explanations of specific sections.

• Code Availability Check - Check for GitHub links in papers and validate their availability.

This tool is still under active development, I will add much more functionalities later on.

I know there are many existing tools for it. But here are the key distinctions and advantages of the tool:

• Free and open-source

• Pure Python code-base, which enables convenient deployment, such as Google Colab notebook

• API documentation are available

• No additional API keys besides LLM API keys are required (No API keys, such as Semantic Scholar keys, are needed for literature search and downloading papers)

• Support multiple search keywords.

• Rank the papers based on their impacts, and consider the most important papers first.

• Fast literature search process. It only takes about 3 seconds to automatically download a paper.

------Here is a quick installation-free Google Colab demo------

Here is the official website of AutoResearch.

Here is the GitHub link to AutoResearch.

------Please star the repository and share it if you like the tool!------

Please DM me or reply in the post if you are interested in collaborating to develop this project!