PinSieve: Production Selective VLM Serving and a Governed Memory Flywheel for Enterprise Content-Quality Triage
Summary
PinSieve is a production case study for enterprise content-quality triage that uses a selective vision-language model serving agent with a governed memory flywheel, resulting in a 25.7% improvement in review productivity and a 16.2% reduction in operating cost.
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# PinSieve: Production Selective VLM Serving and a Governed Memory Flywheel for Enterprise Content-Quality Triage Source: [https://arxiv.org/abs/2608.24040](https://arxiv.org/abs/2608.24040) [View PDF](https://arxiv.org/pdf/2608.24040) > Abstract:Enterprise AI agents in production often need to be bounded, stateful, observable, and governable rather than fully autonomous\. We present PinSieve, a production case study in a large\-scale content\-quality pipeline\. Its deployed component is a selective vision\-language\-model \(VLM\) Serving Agent that operates only on the grey\-zone slice left unresolved by lightweight upstream models, exposes a scalar routing score online, and preserves controlled human escalation\. On this slice, the deployed system filters 2\.05x more non\-actionable items than the previous production module while slightly reducing estimated miss rate; after promotion, it improves review productivity by 25\.7%, reduces normalized operating cost by 16\.2%, and moves signal delivery from next\-day to same\-day\. We then study maintenance through a governed memory flywheel under selective feedback, where escalated items are reviewed by default and auto\-passed items are labeled mainly through audit sampling\. Feedback Memory records routing traces, observation paths, audit propensities, and replay metadata for evaluation and debugging\. The Data Curation Agent uses a bounded proposal\-verifier loop over representative, uncertainty, recency, and fresh\-review replay, with positive\-rate and score\-bin guardrails before batch acceptance\. In chained monthly refresh over six months of production data, this design reduces average FNR@50% from 17\.73% under representative random replay to 13\.29%\. A Reasoning Review Agent audits teacher\-generated rationales and supports keep/repair/drop decisions\. Production claims are attributed only to the deployed Serving Agent; replay and rationale\-review results are offline or sampled\-governance evidence\. The same serving\-agent recipe has been adopted to several additional internal signals, suggesting transferability beyond one task\. ## Submission history From: Yuanfang Song \[[view email](https://arxiv.org/show-email/0cadfc4f/2608.24040)\] **\[v1\]**Tue, 25 Aug 2026 03:57:56 UTC \(512 KB\)
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