Story · Semantic Scholar
Agent-driven Model Development for RNA 3D Structure Prediction (Semantic Scholar)
paper · Story page
Under a fixed budget and human supervision, an LLM agent proposed, implemented, trained and evaluated changes to an RNA structure model 297 times, taking it from a random baseline to an 8.9M-parameter trunk the authors say matches RhoFold+ and NuFold within noise at a fraction of their inference cost. Read it for the loop design.
In plain words
- Researchers used artificial intelligence to help build software for predicting the shape of ribonucleic acid, a biological molecule.
- With human supervision and a fixed budget, the assistant repeatedly suggested changes, built them, and tested the results.
- The authors report no clear accuracy difference from leading alternatives on examples kept separate from development.
- For researchers predicting these shapes, the authors report that the new software costs much less to run.
Appeared in
- An agent deleted an AML control, and benchmark scaffolds do the model's work
Sep 14, 2026 · in the sections
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