Jev won't fix your router

Jev is presented as a fast, low-cost system for returning typed classifications with probabilities, making it potentially attractive for model routing, but experiments on coding-agent traffic suggest that text-only difficulty estimates do not reliably select the best model. After testing prompt clustering, capability taxonomies, turn-type classification, and LLM-generated complexity labels, the authors found that intent or turn type can be inferred from text, whereas task difficulty largely cannot, with complexity labels performing near chance against downstream outcomes. Their deployed router instead predicts an agent’s next action using session history, including prior tool calls, results, errors, current intent, harness type, and uncertainty about hidden session state; these trajectory-based features substantially outperform text-only approaches. Jev could therefore reduce the cost and latency of serving an already effective classifier, but it does not address the central routing challenge of collecting and representing the contextual state that determines what model is appropriate, leading the authors to emphasize detailed instrumentation and outcome tracking over prompt-based difficulty scoring.

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