SOURCE-LINKED INTELLIGENCE

REFLEX with Jev for Efficient Selective Control in LLM Agents

arXiv · AI, language, vision and robotics · article · Sep 22, 2026 · UTC

LLM agents often use generative models for bounded decisions, raising the question of when these decisions can be handled more efficiently without reducing task success. We study REFLEX, an agent architecture that uses Jev as a fast, typed decision layer and calls a strong LLM when confidence is low, or generation is required. On a frozen 100-task benchmark, REFLEX achieves 95% success with 72.7% fewer strong-model calls than a strong-only agent, with reductions persisting across three fallback families. Controlled interventions show that reliability depends on action-set size and near-valid a

Read original source ↗ Open in workspace

recordType

paper

region

Global