{"total":3,"items":[{"citing_arxiv_id":"2606.19106","ref_index":63,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Quantifying Compromise Risk in Exceptional Access Architectures Under Sparse and Indirect Evidence","primary_cat":"cs.CR","submitted_at":"2026-06-17T14:18:36+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19989","ref_index":38,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Error Bounds for Importance Sampling with Estimated Proposal Distributions","primary_cat":"math.ST","submitted_at":"2026-05-19T15:27:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2508.06316","ref_index":6,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"The Beauty of Anisotropic Mesh Refinement: Omnitrees for Efficient Dyadic Discretizations","primary_cat":"cs.DS","submitted_at":"2025-08-08T13:42:59+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":5.0,"formal_verification":"none","one_line_summary":"An omnitree that bisects only selected dimensions per node can raise dyadic AMR convergence by up to the dimension count d for strongly anisotropic problems; on 4,166 shapes it improved mean convergence 1.5x versus octrees.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}