{"total":3,"items":[{"citing_arxiv_id":"2606.12182","ref_index":20,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit","primary_cat":"cs.LG","submitted_at":"2026-06-10T15:06:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09949","ref_index":44,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training","primary_cat":"cs.LG","submitted_at":"2026-06-08T08:25:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"OGAS uses a parallel diffusion model to bias PDE configuration sampling toward high surrogate difficulty, reducing 99th-percentile errors and error variance versus uniform sampling across tested 2D PDEs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22222","ref_index":58,"ref_count":3,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models","primary_cat":"cs.LG","submitted_at":"2026-05-21T09:26:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ARC-STAR reduces velocity rollout error by at least 36x over raw Poseidon across all tested regime cells via auditable global and local correction stages on five flow benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}