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New COMPASS agentic router optimizes LLM selection for cost and accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed COMPASS, a novel agentic LLM router designed to optimize model selection for cost and accuracy. Unlike specialized routers that require frequent updates, COMPASS uses an LLM to follow a routing recipe, gather performance data via tools, and even inspect candidate answers before choosing a model. Evaluations on LLMRouterBench and DeepSWE benchmarks demonstrated that COMPASS achieved higher accuracy at a lower cost compared to existing routing methods, and it can adapt to new models without retraining. AI

IMPACT This agentic routing approach could significantly reduce operational costs for LLM deployments by optimizing model selection.

RANK_REASON The cluster contains a research paper detailing a new method for LLM routing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New COMPASS agentic router optimizes LLM selection for cost and accuracy

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The cluster contains a research paper detailing a new method for LLM routing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Amin Rezaei, Pouya Parsa ·

    COMPASS: Routing via Capability-Oriented Model Profiling with Adaptive Search, Selection, and Tool Use

    arXiv:2610.27822v1 Announce Type: new Abstract: LLM routing aims to select a capable model at low cost, but specialized routers can require updates as models and tasks change. We propose COMPASS, an agentic router in which an LLM follows a routing recipe, gathers performance and …

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