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原文摘要

SignalAudit-QD is a reproducible audit resource for testing whether a dated public signal adds information beyond query history in a fixed 20-term U.S. AI panel. It aligns two archived Google Trends extractions with eight USPTO AI-patent publication streams and releases origin-indexed predictions, paired losses, provenance, executable tests, and tamper-evident run manifests. Both inputs are retrospective reconstructions obtained after the modeled 2020--2023 origins; the task is therefore a pseudo-origin assay within frozen measurement vintages, not a historical deployment simulation. In the designated one-month comparison, adding a two-component patent block to a level ridge changes macro mean absolute error by +0.0012 Trends points, with conditional 95% moving-block interval [-0.0363,+0.0387]. This establishes neither aggregate benefit nor equivalence. Alternative dimensions, semantic mapping, patent definitions, a second extraction, and a three-month horizon remain unresolved; persistence-anchored and nonlinear hosts instead show model-specific worsening, tiny for the anchored fit. Matched timing controls expose availability assumptions, while a planted-signal assay detects only the largest tested injection. A within-query high-interest alert demonstrates the released interface but not online utility. The contribution is a bounded negative case study and an extensible evidence schema for auditing external-signal claims in temporal information retrieval.