PRIMESCIENTIST agent cuts research attempts with tree-based planning
The key insight is that budget allocation strategy, not raw model capability, drives research-agent performance — treating experiments as a resource-allocation problem rather than a fixed pipeline. Teams building autonomous research or discovery agents can apply this explore-then-concentrate policy to cut token costs while improving output quality. It compares directly against AutoResearch on FIRE-Bench, giving a concrete benchmark baseline for evaluating competing agentic research frameworks.
