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It is shown that strong small-option performance does not necessarily imply robust large-scale candidate comparison and that hierarchical partitioning and permutation-based inference improve accuracy by roughly 20 percentage points at $N=160$ on both HotpotQA and MIMIC.
A controlled diagnostic framework with two synthetic tasks that disentangle perception from reasoning reveals a consistent perception-reasoning asymmetry: post-training improves reasoning more substantially than perception, though the underlying mechanism differs across training paradigms.
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