arXiv Machine Learning By Chuqing Gao, Yuanfang Song, Jonathan Zhang, Yifan Wu, Vishwakarma Singh, Qinglong Zeng, Andrey Gusev

PinSieve: Production Selective VLM Serving and a Governed Memory Flywheel for Enterprise Content-Quality Triage

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PinSieve is a production system that selectively serves vision‑language models (VLMs) for enterprise content‑quality triage, operating only on the grey‑zone cases that lightweight models cannot resolve. The deployed VLM Serving Agent filters 2.05× more non‑actionable items, improves review productivity by 25.7%, cuts operating costs by 16.2%, and delivers signals the same day instead of the next. A governed memory flywheel with selective feedback, audit sampling, and a bounded proposal‑verifier loop further reduces false‑negative rates from 17.73% to 13.29% over six months, while a reasoning review agent audits teacher‑generated rationales for keep/repair/drop decisions. whyItMatters":"The system demonstrates how selective VLM serving and governed feedback loops can substantially improve efficiency, cost, and accuracy in enterprise AI content‑quality pipelines."

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arXiv Computer Vision
Aug 25

From Subjective Judgments to Auditable Standards:Protocol-Guided AI Auditing of Website Redundancy

The paper introduces CORA (Counterfactual, Observable Redundancy Audit), a protocol for auditing website redundancy by measuring repetition load, normal-use tax, and failure-domain recovery reserve. Each audit run records screenshots, stable element identities, and task traces, while a versioned vision‑language model generates annotations that are validated and released only if they meet calibrated criteria. Experiments on a transparent mechanistic testbed show that CORA’s factorized representation separates reserve from normal-use tax and predicts perturbed success more accurately than scalar-load baselines, but it withholds automated scores when instruments fail to meet release requirements, indicating that CORA is an auditable candidate procedure for the studied benchmark rather than a universal standard.

By Ge Kong, Yongtong Cao