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reading correctly attributes the work to Yuvraj Sehgal, Mahsa Panahandeh and Naser Ezzati-Jivan. It keeps the findings framed as the authors' reports rather than established fact.","excerpt":"Authors:Yuvraj Sehgal, Mahsa Panahandeh, Naser Ezzati-Jivan"},{"dimension":"summary","status":"uncertain","explanation":"The core idea, that tool output is cut before the model sees it, is clear and faithful. But the summary ends by saying the authors leave open whether data loss causes 'the observed accuracy drop' without first describing that drop. Its omitted context is the 4.7% vs 99.3% field split: which field held the evidence depended on the faulty process's start time, and accuracy followed that split. The task, diagnosing injected faults, is only alluded to, and 'yield' and 'broken JSON' go unexplained for general readers. Smallest repair: one sentence on the field split and the matching accuracy pattern, plus brief plain glosses or tooltips for JSON and yield.","excerpt":"One field reached the model in only 4.7% of calls. A second field, in the very same results, reached the model in 99.3% of calls."},{"dimension":"date","status":"pass","explanation":"The originalPublished date of 2026-10-26 matches the arXiv v1 submission date. The later feed date is correctly not used.","excerpt":"[Submitted on 26 Oct 2026]"},{"dimension":"support","status":"pass","explanation":"Each claim is directly supported by the abstract: the counts, the fixed-length JSON slicing, the extra work and lower yield, the alternative route, the two of seven frameworks, and the open causal question. The limitations and the question stay within the source's scope and do not invent benefits or costs.","excerpt":"the cut's real cost appears first as extra work, not as wrong answers: 43% more tool calls, twice the computation, and yield falling from 0.61 to 0.31"},{"dimension":"media","status":"pass","explanation":"The selected media is the verified direct arXiv abstract page for this paper. 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