2 papers
cs.LG2026
Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs For Link Prediction
Chen Shao, Donald Loveland, Tobias Käfer +1
Graph Neural Networks (GNNs) are effective for learning node and link embeddings through permutation-equivariant aggregation. However, standard GNNs collapse automorphic nodes, i.e…
cs.AI2026
Iris: Climbing to the Search Frontier
Ziyuan Liu, Hengqi Liu, Zichuan Wang +8
We present Iris-mini and Iris-pro, two search agents trained at the 35B-A3B and 397B-A17B scales, together with the data pipeline and training recipe behind them. Tasks are reverse…