[2603.26728] SEAR: Schema-Based Evaluation and Routing for LLM Gateways

[2603.26728] SEAR: Schema-Based Evaluation and Routing for LLM Gateways

arXiv - AI 3 min read

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Abstract page for arXiv paper 2603.26728: SEAR: Schema-Based Evaluation and Routing for LLM Gateways

Computer Science > Databases arXiv:2603.26728 (cs) [Submitted on 20 Mar 2026] Title:SEAR: Schema-Based Evaluation and Routing for LLM Gateways Authors:Zecheng Zhang, Han Zheng, Yue Xu View a PDF of the paper titled SEAR: Schema-Based Evaluation and Routing for LLM Gateways, by Zecheng Zhang and 2 other authors View PDF HTML (experimental) Abstract:Evaluating production LLM responses and routing requests across providers in LLM gateways requires fine-grained quality signals and operationally grounded decisions. To address this gap, we present SEAR, a schema-based evaluation and routing system for multi-model, multi-provider LLM gateways. SEAR defines an extensible relational schema covering both LLM evaluation signals (context, intent, response characteristics, issue attribution, and quality scores) and gateway operational metrics (latency, cost, throughput), with cross-table consistency links across around one hundred typed, SQL-queryable columns. To populate the evaluation signals reliably, SEAR proposes self-contained signal instructions, in-schema reasoning, and multi-stage generation that produces database-ready structured outputs. Because signals are derived through LLM reasoning rather than shallow classifiers, SEAR captures complex request semantics, enables human-interpretable routing explanations, and unifies evaluation and routing in a single query layer. Across thousands of production sessions, SEAR achieves strong signal accuracy on human-labeled data and suppo...

Originally published on March 31, 2026. Curated by AI News.

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