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five named authors match the arXiv listing. The problem diagnosis, the PICO proposal and the performance results are all framed as the authors' arguments or reports.","excerpt":"Authors:Zehao Wang, Hanzhu Chen, Runquan Gui, Zhe Wang, Lin Yang"},{"dimension":"summary","status":"uncertain","explanation":"The summary uses RLVR without saying it stands for Reinforcement Learning with Verifiable Rewards. Policy, trajectories and off-policy are also left unexplained for general readers. It omits the authors' central reason for PICO: expert trajectories fall in the student's low-probability regions and decay into surface-level imitation. It also drops consistently from the results claim. Smallest repair: define RLVR and policy, and add one plain sentence on why raw expert data fails.","excerpt":"they are incompatible with the current policy and often lie in the student's low-probability regions, so the surviving updates demand large parameter shifts and decay into surface-level imitation rather than transferable reasoning"},{"dimension":"date","status":"pass","explanation":"The displayed original publication date of 2026-10-27 matches the v1 arXiv submission date. The later feed date is not used.","excerpt":"[Submitted on 27 Oct 2026]"},{"dimension":"support","status":"pass","explanation":"Each claim is supported by abstract text and presented as an author statement. The limitation and the question correctly note that the abstract gives no numbers and names no benchmarks. No unsupported benefits or costs are implied.","excerpt":"Experiments across reasoning diverse datasets and agentic benchmarks demonstrate that PICO consistently outperforms standard RLVR and prior off-policy baselines."},{"dimension":"media","status":"pass","explanation":"The selected media is the verified direct arXiv abstract page for this paper. It is the most direct access to the source.","excerpt":null}],"createdAt":"2026-11-05T08:52:18.611Z","model":"anthropic/claude-opus-5.5"}],"correctionUrl":"https://escapemeridian.com/corrections/"}