REFLEX cuts strong-model calls with Jev
REFLEX adds Jev as a fast, typed decision layer for LLM agents, escalating to a stronger model only when confidence is low or generation is needed. It reached 95% task success while reducing strong-model calls by 72.7% on a frozen 100-task benchmark.
REFLEX makes a strong case for separating bounded decisions from open-ended generation, but its savings are workload-dependent rather than universal.
- –Jev handles fixed choices and confidence gating, while stronger LLMs handle uncertainty and text generation.
- –The 72.7% reduction persisted across three fallback strategies, suggesting the architecture is not tied to one escalation design.
- –Reliability falls with larger action spaces and near-valid alternatives, making calibration and conservative thresholds essential.
- –External evaluations found limited gains over cheap generative cascades when baseline routing is already highly accurate.
DISCOVERED
1h ago
2026-09-24
PUBLISHED
1h ago
2026-09-24
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