OmniScientist: An Omni-Modal Omni-Discipline AI Scientist
Recent advances in foundation models have enabled AI scientists to automate increasingly complete research workflows, from hypothesis generation and code execution to manuscript preparation. Yet workflow coverage alone does not provide access to the full evidence on which scientific discovery depends. Existing systems typically reason over text, code, labels, or precomputed summaries, leaving scientifically decisive spatial, temporal, cross-channel, and procedural relations unavailable to the agent. We introduce OmniScientist, an end-to-end, omni-modal AI scientist that conducts multidisciplin
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 61%USC leads national AI research project to accelerate scientific discovery - USC Today →
- FuzzyOverlapping authors or contributors · 62%browser-use/browser-use →
“Shared author/contributor keys: lee”
- LinkedLinked via arxiv author · 85%Bobo Li →
“OmniScientist: An Omni-Modal Omni-Discipline AI Scientist”
- LinkedLinked via arxiv author · 85%Hao Fei →
“OmniScientist: An Omni-Modal Omni-Discipline AI Scientist”
- LinkedLinked via arxiv author · 85%Tianjie Ju →
“OmniScientist: An Omni-Modal Omni-Discipline AI Scientist”
- LinkedLinked via arxiv author · 85%Mong-Li Lee →
“OmniScientist: An Omni-Modal Omni-Discipline AI Scientist”
- LinkedLinked via arxiv author · 85%Wynne Hsu →
“OmniScientist: An Omni-Modal Omni-Discipline AI Scientist”