2 min readfrom Machine Learning

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]

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]
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]

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?

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