پروفسور آنیما آناندکومار و تیمش از یه فریم‌ورک تر و تمیز به اسم ReRoute رونمایی کردن که تخصصش جواب دادن به سوال‌های «چی می‌شد اگه...» توی دنیای علمه

Excited to share ReRoute, our new framework for scientific counterfactual prediction: informed by partial physics knowledge for much better generalization. 🌍 AI models can be remarkably accurate on familiar data, yet struggle with scientific ‘what-if’ questions The key idea: Combine observational data with partial physical knowledge to predict unseen interventions—without requiring new controlled experiments. Instead of retraining models on expensive new simulations, ReRoute uses known partial physical mechanisms to guide pretrained models through lightweight fine-tuning on existing data. The results: 🌍 Up to 31.8% lower prediction error under severe CO₂ distribution shifts in climate emulators. 🔬 Up to 89.6% lower counterfactual error in controlled advection–diffusion experiments. ⚡ No new intervention data. No retraining from scratch. Just lightweight fine-tuning. We also establish a causal identification result under explicit structural assumptions, with a machine-checked proof in Lean. The bigger picture: Scientific AI doesn't always need more data or bigger models to generalize beyond what it has seen. Sometimes, knowing just a little about how the world works can make a big difference. @Dingling_Yao @kahaan_gandhi @FrancescoLocat8 @Caltech 📄 Paper: https://arxiv.org/abs/2610.02252 #AIforScience #ScientificML #ClimateAI #CausalInference #PhysicsAI