{"id":"f299e6ca-afcf-48cd-8b9b-9fa5dd2d803e","arxiv_id":"2505.14005","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":4,"one_line_summary":"OPEN clusters training graphs into inferred environments and trains a variational subgraph generator to explain GNN predictions across distribution shifts without model internals or edge weights.","lead":"OPEN is a new explainer for graph neural networks that claims to capture a GNN's decision logic across distribution shifts without needing access to model internals or learnable edge weights. It works by clustering training graphs into inferred environments and training a variational generator to sample explanation subgraphs from each environment.","discovery_kind":"new_method","skeptic_critique":null,"referee_report":null,"author_rebuttal":null,"desk_editor":null,"rs_alignment":null,"lean_confirmation":null,"pith_extraction":null,"created_at":"2026-08-07T15:42:40.131087+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}