PapersTok – AI ArXiv Papers with a TikTok Like UX

Launching a fun side project to preview arXiv papers related to AI with a TikTok like experience called PapersTok.

In the current fast paced world of AI research, where hundreds of papers are put up on arXiv daily, keeping up with the latest developments presents significant challenges. One of them being the difficulty of navigating around the arXiv web interface, where new tabs have to be constantly opened and closed just to skim through the title and the abstract. What if there was a much simpler and fun way to do just that?

Inspired by WikiTok, I built PapersTok to scroll through arXiv submissions related to AI. It has LaTeX support to render math equations. It also provides the ability to bookmark papers you find interesting. I'm planning to add more features in the coming days to enhance the experience of skimming through papers.

I request the community to highlight the challenges they currently face that can be alleviated through this tool. Your valuable feedback and comments are much appreciated. Feel free to DM or tweet me @pranftw on X.

Great idea! I would totally swipe through. One small recommendation, consider having the title and a short description written by an LLM making it as dumb and sensational as possible, for a true tiktok like experience for instance the this title and abstract could be transformed into the following

Instead of:

"Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation Framework

Multimodal Retrieval-Augmented Generation (MRAG) enhances reasoning capabilities by integrating external knowledge. However, existing benchmarks primarily focus on simple image-text interactions, overlooking complex visual formats like charts that are prevalent in real-world applications. In this work, we introduce a novel task, Chart-based MRAG, to address this limitation. To semi-automatically generate high-quality evaluation samples, we propose CHARt-based document question-answering GEneration (CHARGE), a framework that produces evaluation data through structured keypoint extraction, crossmodal verification, and keypoint-based generation. By combining CHARGE with expert validation, we construct Chart-MRAG Bench, a comprehensive benchmark for chart-based MRAG evaluation, featuring 4,738 question-answering pairs across 8 domains from real-world documents. Our evaluation reveals three critical limitations in current approaches: (1) unified multimodal embedding retrieval methods struggles in chart-based scenarios, (2) even with ground-truth retrieval, state-of-the-art MLLMs achieve only 58.19% Correctness and 73.87% Coverage scores, and (3) MLLMs demonstrate consistent text-over-visual modality bias during Chart-based MRAG reasoning. The CHARGE and Chart-MRAG Bench are released at https://github.com/Nomothings/CHARGE.git."

Give me: " "AI Fails Hard at Reading Charts: New Study Exposes Shocking Weaknesses!"

A groundbreaking study reveals that even the smartest AI models struggle with charts, scoring just 58% accuracy—proving your brain might still be better than AI at decoding data! "