Modern reasoning systems increasingly rely on verifier-guided selection: sample several solution traces, score them with a reward model, and return the top-ranked answer. In multi-generator deployments, this verifier is often reused across generators. We ask whether this reuse assumption is reliable. On a 34-generator math panel with K=64 candidates per problem and 7 verifiers, the 7B process reward model (PRM-7B) is the strongest fixed verifier, but a per-target single-verifier oracle gains +1.6 percentage points (pp). We then ask whether this target-local mismatch is actionable. Four label-free candidate-text statistics predict the coarse PRM outcome-verifier regime, and three budgeted responses improve over fixed PRM on the 22-target first-pass panel: a no-label gated router (+1.41 pp), PRM+V4 fusion (+2.35 pp), and a labelled PRM-only verifier-allocation arbiter (PVA-Adapt; +2.26 pp). A same-panel American Invitational Mathematics Examination (AIME) 2026 held-out slice gives +3.88 pp, and a second candidate-bank check keeps the scored and labelled gains positive while weakening the no-label router. We frame the result as a seed- and verifier-family-conditioned deployment diagnostic rather than a universal routing rule; a supervised fine-tuning (SFT) replication also finds no positive transfer from better selected traces.

In e-commerce, product detail pages (PDPs) often contain hundreds or thousands of customer reviews, yet the interface can surface only a small set of concise and reliable insights. A useful review aggregation system must therefore abstract recurring themes while preserving which reviews support each insight. We study this problem as structured review-insight mining: review-level tuples are first extracted and then aggregated into ranked product-level insight clusters with explicit member traceability. We propose GraphReduce, a coverage-preserving framework for product-scale review insight aggregation. It combines a contrastively trained projection layer over frozen sentence embeddings, graph-based clustering on local neighborhood structure, and an LLM reducer/ranker for naming and ordering canonical clusters. By keeping membership accounting outside the generative step, GraphReduce preserves complete tuple coverage and avoids a common failure mode of direct LLM summarization: producing plausible high-level themes while silently dropping long-tail evidence. Across three product-category benchmarks, GraphReduce improves recovery of gold insight clusters over strong lossless embedding-based baselines while maintaining comparable ranking quality. We further show that flat LLM and topic-modeling baselines can appear competitive under ranking metrics despite omitting substantial input evidence, and that an anti-copy reinforcement objective improves the quality of upstream tuple extraction. These results highlight the importance of coverage-aware aggregation for faithful review insight mining in e-commerce settings.