Original Summary

I've been building a small reasoning layer with Jev for an agentic browser-testing system. The idea was to explore whether an agent could intelligently decide when it actually needed an expensive/capable model and when it could route execution somewhere cheaper. I expected model selection to be the interesting/most effective part. Surprisingly, It wasn't. In the first experiment, one of the strongest results came from keeping the execution model fixed and adding guardrails around it. For example, a guarded GPT-5.5 completed 15/15 mission attempts while using 13.7% fewer model tokens than the unguarded Terra control. I then tested cross-provider selection across 120 isolated mission executions. The actual selection decision was cheap, roughly 621–724 tokens each turn, but the dynamically routed configurations didn't produce an overall token advantage in this workload. This changed the hypothesis I'm working from. As opposed to: "What's the cheapest model that can use on this step?" I'm increasingly interested in: "What's the cheapest successful trajectory through the entire task?" A stronger model that avoids retries, bad decisions, unnecessary exploration, and recovery seems to ultimately be cheaper than repeatedly handing work to a smaller model. I've put the methodology, condition-level results, caveats and data here: https://testronaut.app/research/jev-optimization-experiments-2026-10 There's also a less dry write-up: https://testronaut.app/blog/the-slide-rule-and-the-supercomputer I'm interested in what other variables people would isolate next?   submitted by   /u/shane-testronaut [link]   [comments]

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  • 发布时间：2026/10/11 06:36:02