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统计口径与数据新鲜度

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版本更新 · v3arXiv:2607.00516

When A Beats B Needs Evidence: CLAIMSTAB-QC for Auditing Comparative Claims in Quantum Software

Boshuai Ye, Peng Liang, Maryam Tavassoli Sabzevari, Arif Ali Khan

原文摘要

Empirical quantum-software papers often claim that one compiler produces smaller circuits, one optimizer is more reliable, or one ansatz achieves better solution quality. Such comparisons depend on benchmark scope, circuit construction, compilation, sampling, backends, noise models, optimizer choices, and resource budgets. Existing testing, benchmarking, and reproducibility methods help assess programs and experiments but do not directly audit whether the reported comparison is supported by the evidence available in the paper and artifact. We present CLAIMSTAB-QC, a source-bounded framework that records baselines, metric, relation, and admissible evidence; locks the audit design before outcomes are computed; and reports a scoped relation outcome or an explicit evidence boundary. Strict scalar-directional comparisons are classified as Sustained, Unresolved, or Not preserved within the locked audit scope. We examine 455 comparative claims from 119 quantum-software papers. A materialization gap emerges: 175 claims can be represented for audit planning, 79 become scalar-directional planning records, and 53 yield lockable audit or diagnostic designs, but only 8 expose enough matched evidence to audit the original comparison without proxy reconstruction. Among these 8, 2 are Sustained, 4 Unresolved, and 2 Not preserved. Controlled diagnostics over 24 benchmark-relevant quantum-workload comparisons further show that simpler checks can preserve apparent directions despite weaker support under locked designs. These results indicate that performance orderings should be reported with matched evidence sufficient to audit their stated scope.

cs.SE

版本更新 · v2arXiv:2611.06323

Generative Retrieval under Real-Time Supply Constraints in Local Commerce

Shaopeng Liang, Biao Zhang, Hongzhen Ye, Zeyang Hou, Ma Jiang, Jintao Tu, yunxi. hz, Zhijia Fang 等 11 位作者

原文摘要

Generative retrieval predicts semantic identifiers for candidate entities. In local retail and food delivery, candidates must also be serviceable for a particular user at a particular time. Location, weather, and traffic reshape merchant and product supply, so a catalog-valid identifier may be infeasible for the current request. Post-generation filtering removes such candidates only after beam capacity and inference computation have been spent. We study retrieval over an explicitly materialized, request-time supply snapshot, where short output sequences leave little opportunity to amortize online constraint preparation. Our RTP-LLM pipeline compiles eligible residual-quantization identifiers into a compressed sparse row prefix index, overlaps CPU compilation with model prefill, and shares the uploaded index across beams. Legal expansions retain original-model log probabilities, and constraint states follow selected beam parents. We formalize snapshot-relative identifier validity and distinguish it from entity-level eligibility and freshness. The implementation generates three-token merchant identifiers. Historical evidence covers T+1 daily-refresh catalog constraints and subsequent request-scoped serving evaluations. In one matched-beam daily-constraint case, filtered merchants decrease from 170 of 299 candidates to 29 of 299; residual distance filtering motivates request-specific constraints. A different-host comparison with 30,000 supplied entries reports a 0.6 ms service-side latency increment, while client measurements expose additional request costs. Captured requests from three geographic settings characterize behavior across supply sizes and output widths. These observations span development stages and measurement boundaries. They establish a practical implementation and identify bottlenecks, but do not establish compute savings at matched retrieval quality.

cs.IR