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.

AI coding has emerged as a core application of large language models (LLMs), evolving from single-file coding tasks towards complex software engineering (SWE) scenarios. Recent advances in agents have enabled multi-file, multi-language, and dependency-aware AI coding, significantly expanding the scope of AI-assisted software development. While a variety of benchmarks have been proposed to evaluate coding capabilities in general-purpose or GPU coding ecosystems such as CUDA and ROCm, systematic evaluation for Huawei Ascend CANN remains largely underexplored. In this work, we propose LiveCANNBench, an SWE-level benchmark designed for AI coding in the CANN software stack. LiveCANNBench is constructed from real-world CANN repositories and consists of over 400 task instances spanning multi-file, multi-language, and execution-aware coding challenges. Unlike existing static benchmarks that primarily focus on kernel-level code generation, LiveCANNBench adopts a live benchmarking paradigm, effectively mitigating data leakage and enabling more reliable evaluation of modern coding agents.

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.

Large Language Models (LLMs) have achieved impressive results across a broad array of tasks, yet their capacity for complex, domain-specific mathematical reasoning—particularly in wireless communications—remains underexplored. In this work, we introduce WirelessMathBench, a novel benchmark specifically designed to evaluate LLMs on mathematical modeling challenges to wireless communications engineering. Our benchmark consists of 587 meticulously curated questions sourced from 40 state-of-the-art research papers, encompassing a diverse spectrum of tasks ranging from basic multiple-choice questions to complex equation completion tasks, including both partial and full completions, all of which rigorously adhere to physical and dimensional constraints. Through extensive experimentation with leading LLMs, we observe that while many models excel in basic recall tasks, their performance degrades significantly when reconstructing partially or fully obscured equations, exposing fundamental limitations in current LLMs. Even DeepSeek-R1, the best performer on our benchmark, achieves an average accuracy of only 38.05%, with a mere 7.83% success rate in full equation completion. By publicly releasing WirelessMathBench along with the evaluation toolkit, we aim to advance the development of more robust, domain-aware LLMs for wireless system analysis and broader engineering applications.