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reading consistently attributes the method, the labelling-function limitation and the results to the paper's authors (Parac, Ziakas, Ardon, Russo). It presents them as the authors' claims rather than established fact.","excerpt":"Authors:Roko Parac, Christos Ziakas, Leo Ardon, Alessandra Russo"},{"dimension":"summary","status":"pass","explanation":"The title matches the source. The summary faithfully states the problem: reward machines need a hand-built labelling function for each environment. It then gives the proposed fix (an LLM writes the RM, a VLM labels images), the dense-reward estimator and the three test domains. A non-specialist can follow the central idea. Terms such as automata, propositions and dense reward signal are kept exactly, suited to tooltips. Optional repair: state that the stated aim is wider applicability. The question is a genuine open issue the abstract leaves unanswered.","excerpt":"which must be manually engineered for each environment, limiting their wider applicability"},{"dimension":"date","status":"pass","explanation":"The originalPublished date of 2026-11-05 matches the arXiv v1 submission date. It is not the later feed date of 2026-11-06.","excerpt":"[Submitted on 5 Nov 2026]"},{"dimension":"support","status":"pass","explanation":"All claims are grounded in the abstract. The reading correctly marks the gap that the abstract gives no effect sizes and no comparison details. It does not stretch the reported success rates into a claim of real-world reliability.","excerpt":"Our experimental results show that RM-VLM consistently achieves higher task success rates than existing VLM-based reward methods across all tested domains."},{"dimension":"media","status":"pass","explanation":"The selected media is the verified arXiv abstract page, which is the direct original source. It is the most relevant link available.","excerpt":null}],"createdAt":"2026-11-08T21:38:03.089Z","model":"anthropic/claude-opus-5.5"}],"correctionUrl":"https://escapemeridian.com/corrections/"}