| Spent the last few months building a deeper context layer over arxiv. Each paper gets a Tomesphere page with a TLDR + key findings (LLM-curated), OpenReview reviews where the venue is public, linked GitHub repos, HuggingFace models, conference videos, the citation graph in both directions, and a SPECTER2-based semantic neighbor graph. Same panel renders inline on arxiv via a Chrome extension (MV3 side panel API), or you can browse directly at tomesphere.com. 3M arxiv papers indexed. Caveats: reviewer scores only cover venues that publish openly on OpenReview (NeurIPS, ICLR, ICML, TMLR, COLM). Blind-review venues like CVPR, AAAI, ECCV are out of scope until contributors fill them in. GitHub, Hugging Face, and conference video matches are best-effort. Free, no signup. Site: tomesphere.com Chrome: chromewebstore.google.com/detail/tomesphere/nopoigoclhjcopjppnehidnkljmabllk Would love feedback, especially: which paper did you check first, and what's missing that you'd actually use? [link] [comments] |
Kept context-switching between arxiv, OpenReview, GitHub, and HuggingFace for every paper, so I built this. Chrome extension + website with everything inline, plus citation graph + SPECTER2 neighbors. 3M papers, free, feedback welcome [P]
Reddit r/MachineLearning / 5/28/2026
📰 NewsDeveloper Stack & InfrastructureSignals & Early TrendsTools & Practical Usage
Key Points
- The project “Tomesphere” adds a deeper context layer for arXiv papers by aggregating TLDRs and key findings (LLM-curated), public OpenReview reviews, GitHub repos, Hugging Face models, and conference videos onto a single page per paper.
- It includes both directions citation graphs and a SPECTER2-based semantic neighbor graph to help users discover related work.
- A Chrome extension renders this panel inline directly on arXiv using the MV3 side panel API, while users can also browse the content at tomesphere.com.
- The system indexes about 3 million arXiv papers and is free with no signup, but it has scope limits for venues without public OpenReview data and uses best-effort matching for GitHub/Hugging Face/video.
- The author is actively seeking feedback on what users check first and what information would be most useful or missing.
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