6 min readJust now

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Just recently Hugging Face announced their collaboration with Meta to build the successor to Papers With Code (PWC), which has been sunset:

If you’ve ever tried to keep up with the latest artificial intelligence research, you know the struggle. Every day brings a flood of new papers, breakthrough discoveries, and technical innovations. What was once a manageable stream of publications has become a raging river of information that even seasoned researchers find difficult to navigate.

Meet Papers, an AI-powered workspace that’s changing how we collect, organize, and understand AI research. Think of it as your personal research assistant that never sleeps, never gets overwhelmed, and always remembers where you left that important paper about transformer architectures.

The AI Research Avalanche

At the time of this writing, arXiv sees 24,000 publications uploaded monthly.

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Monthly submissions to arXiv exceed 24000 papers: https://arxiv.org/stats/monthly_submissions

The numbers are staggering. While not all papers are on AI topic, Computer Science on arXiv has been dominating in volume compared to math, physics, quantitive biology and finance etc in the past 5 years.

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Submissions to arXiv by category, last 5 years: https://info.arxiv.org/about/reports/submission_category_by_year.html

With the volume of AI research growing almost exponentially, staying current with meaningful developments is a constant challenge — even for experts. We’re living through what many consider the golden age of AI research, with breakthroughs happening at breakneck speed across machine learning, natural language processing, computer vision, automatic speech recognition and beyond.

But here’s the paradox: the very field that’s supposed to make information processing easier has created an information overload problem for its own practitioners.

PhD students spend hours sifting through ArXiv. Industry researchers struggle to identify which papers are worth their time. Even AI pioneers find themselves drowning in the sea of publications they helped create.

What Makes Muves Papers Different

Papers isn’t just another paper repository or bookmark manager. It’s a comprehensive research platform designed specifically for the modern AI researcher’s workflow.

Intelligent Discovery

Instead of manually hunting through endless paper lists, Papers actively helps you discover trending research and key developments. The platform cuts through the noise to surface papers that matter, using AI to identify genuine breakthroughs from incremental work.

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Trending papers in Muves Papers

Project-Centered Organization

One of Papers’ most powerful features is its project-based organization system. Whether you’re working on computer vision for autonomous vehicles, exploring large language models, or diving into reinforcement learning, you can create dedicated project spaces that keep related research organized and accessible. What is also handy, is that you can upload and analyze a paper of your own, if you wrote one or found one online or on an old USB flash drive.

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Workspace: a collection of projects, with each project holding papers. This helps you organize your research process, generate literature review and chat to papers in the given project

Helping you conduct science

If you are a research scientist, MSc / PhD student, your research often times starts with doing a literature review. In other words, you’d like to know what has been done so far on the topic of your choice. Doing so is important, but can also take a long amount of time. This is where Papers offers an automated Literature Review. To access it, create a project, add relevant papers and hit the button “Generate Literature Review”.

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Literature review, generated in the project “Reasoning LLMs”

If you need to, you can make edits to the text, or just download the PDF version to accompany your seminar presentation or a chat with your supervisor(s).

AI-Assisted Understanding

Perhaps most importantly, Muves doesn’t just store papers — it helps you understand them. The platform’s AI capabilities break down complex research into clear summaries, making it easier to grasp key concepts without getting lost in technical jargon.

Picking on the paper from the previous screenshot, I have clicked on the “View analysis” blue button and got this output:

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Example of a paper analysis

Related papers

When viewing a detailed paper page, in addition to the paper’s abstract and analysis (like you saw in the previous section), Papers will present the list of papers, related to the given paper’s topics.

For example, for the paper “ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability” I currently see the following papers as related:

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Related paper list for a given paper

You can add all of these to a project for further study (via chat or paper analysis).

Upload a paper (or any PDF)

To make sure you can conduct your research not only based on the available sources, like arXiv, we added a feature for uploading any PDF file. For example, I’ve uploaded an entire book called “Principles of Building AI Agents”, and Papers managed to correctly parse the book’s author (Sam Bhagwat), publication year (2025), and even the page with the author’s photo and recommendations of readers.

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Detailed page of the “Principles of Building AI Agents” book

Now imagine you can upload any document at all to study it more precisely. Everything you upload will be visible only to you, to preserve your data privacy.

Muves Research Assistant

What sets Papers apart is its conversational Research Assistant we called Muves. Imagine being able to chat with your entire research collection, asking questions like:

• “What are the latest trends in transformer architectures?”
• “How do these three papers on few-shot learning relate to each other?”
• “Can you generate a literature review for my project on multimodal AI?”

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Chat to paper(s) feature: relation between papers

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Chat to paper(s) feature: asking a general question about trends in transformer architectures

This isn’t science fiction — it’s the reality of modern research tools. The Muves Research Assistant can generate literature reviews, help you spot connections between papers, and even assist with citation management.

Beyond Individual Productivity

While Papers aims to solve individual productivity challenges, its potential impact extends beyond personal research management. By making AI research more accessible and digestible, tools like Papers are democratizing access to cutting-edge knowledge.

Graduate students who might otherwise struggle with the overwhelming literature can now engage more effectively with research. Industry practitioners can stay current with academic developments without dedicating their entire day to paper reading. Even seasoned researchers can explore adjacent fields more efficiently.

The Future of Research Management

Papers represents a broader trend in academic tooling — the application of AI to solve problems created by AI’s own success. As research velocity continues to accelerate, we need increasingly sophisticated tools to help us navigate the knowledge landscape.

The platform’s approach suggests a future where research tools are not just repositories but active participants in the research process. Instead of passive databases, we’re moving toward intelligent research partners that can synthesize information, identify patterns, and generate insights.

Getting Started

For researchers tired of drowning in papers, Papers offers a lifeline. The platform’s AI-powered workspace provides the structure and intelligence needed to transform overwhelming research chaos into organized, actionable knowledge.

Whether you’re a PhD student beginning your research journey, an industry professional trying to stay current, or a seasoned researcher looking to streamline your workflow, Papers offers a new way to engage with AI research — one that emphasizes understanding over accumulation, insight over information.

In a world where staying current with AI research feels like an impossible task, Papers proves that the solution might just be more AI. Sometimes the best way to solve a problem created by technology is with better technology.