SAGE: New Framework for Scalable AI Governance in Large-Scale Search Systems
LinkedIn researchers have published SAGE (Scalable AI Governance & Evaluation), a framework that uses large language models to automate and scale search relevance judgments at production levels. The system combines natural-language policy documents, curated precedent cases, and an LLM-based surrogate judge in a bidirectional calibration loop to approximate human-level evaluation quality. Deployed within LinkedIn Search, SAGE reduced evaluation costs by 92× and contributed to a 0.25% lift in daily active users.
Researchers from LinkedIn have introduced SAGE, a framework designed to close the governance gap between human oversight quality and the high-throughput demands of large-scale search systems. Traditional search evaluation methods—such as engagement proxies like clicks or sparse manual review—often miss high-impact relevance failures, motivating the need for a more systematic approach. At SAGE's core is a bidirectional calibration loop in which a natural-language Policy, curated Precedent examples, and an LLM Surrogate Judge co-evolve to resolve semantic ambiguities and produce a multi-dimensional, executable relevance rubric. To make this practical at industrial scale, the team applies teacher-student knowledge distillation, compressing high-fidelity frontier-model judgments into compact student models at 92× lower inference cost. In production, SAGE enabled simulation-driven model development, detected relevance regressions invisible to engagement metrics, and supported oversight of ramped model variants. The system is reported to have achieved near human-level inter-rater agreement and collectively contributed to a 0.25% increase in LinkedIn daily active users. The paper was submitted to arXiv in February 2026 and revised through June 2026.
What's missing
The paper does not detail the specific benchmark or methodology used to establish 'near human-level agreement,' nor does it report inter-annotator agreement scores (e.g., Cohen's kappa or Krippendorff's alpha) that would allow independent verification of that claim. The causal attribution of the 0.25% daily active user lift specifically to SAGE—versus concurrent product changes—is not fully elaborated. Additionally, the generalizability of the framework beyond LinkedIn's specific search context and data distribution remains an open question.
What different sources said
- arXiv cs.AICenter
SAGE: Scalable AI Governance & Evaluation
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