SoupFold from KAIST: no single co-folding model wins everywhere, so learn simple…
This is a AI post classified by Jev as AI research (research), kept by the AI Radar because it carries real work, not commentary.
SoupFold from KAIST: no single co-folding model wins everywhere, so learn simple mappings between AlphaFold3, Protenix, ESMFold2, OpenDDE reps at inference. No retraining. SOTA on FoldBench.We hit this exact issue on CDK20 in 2023.https://arxiv.org/abs/2609.15552
Posted by Alex Zhavoronkov, PhD (aka Aleksandrs Zavoronkovs) (43.8k followers) 56 min ago · 0 likes · 399 views · view the original post on X. Kept by the AI Radar as AI research. Tools mentioned: arXiv.org.
More AI work like this
- We built AI that actually knows aging biology. Longevity-LLM (9B params) beats OpenAI… — @biogerontology
- We need more examples like this in the open-source RL ecosystem — @adithya_s_k
- "Verbalizing Subliminal Learning Effects Using Text Optimization" — @askalphaxiv
- “What Does Privileged Information Add to On-Policy Self-Distillation?” — @askalphaxiv
- “Score Centering Stabilizes Off-Policy Reinforcement Learning” — @askalphaxiv
- Banger paper from Google Cloud AI Research. — @dair_ai
- If you liked the MiMo-V2.6 livestreamed RL run, don’t forget to check out Marin’s… — @adi_baradwaj
- Astra solved this hand-object reconstruction and tracking problem in one shot😂 — @YuXiang_IRVL
Every post is read and classified by Jev (TypeSafe): what it is, which market it belongs to, and whether the link is a real tool. 27.3k posts from 4.8k X accounts over the last 14 days, 1.2k tools, 19 markets. Collected every 5 minutes, fully re-ranked every hour — last update 2026-09-19 19:27 UTC. Full method.