{"id":"f3e3e440-826e-4bf9-be2f-776e8c0c0df1","arxiv_id":"2505.13967","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":2,"one_line_summary":"A BFGS quasi-Newton method is claimed to converge to robust weakly efficient solutions of finite-scenario uncertain multiobjective problems under a regularity condition on the limit point.","lead":"The paper presents a quasi-Newton (BFGS) algorithm designed to find robust weakly efficient solutions for multiobjective optimization problems that depend on finitely many uncertain scenarios. It matters because real-world multiobjective decisions under uncertainty currently rely on convexity-hard Newton variants, and this work claims to drop that convexity requirement while still guaranteeing convergence.","discovery_kind":"extension","skeptic_critique":null,"referee_report":null,"author_rebuttal":null,"desk_editor":null,"rs_alignment":null,"lean_confirmation":null,"pith_extraction":null,"created_at":"2026-08-07T15:45:38.348339+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}