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.
PRIMESCIENTIST is a research-agent method that allocates experiment budgets across competing executable plans in a tree instead of pursuing a single trajectory.
Experiment results update branch values, and the allocation policy explores more when resources are plentiful while concentrating on stronger branches as the budget shrinks.
On 12 FIRE-Bench tasks, it achieved 10.3% higher average reward than AutoResearch while using 50.
6% fewer research attempts under the same token budget.Across the full evaluation, it used fewer attempts on 23 of 24 tasks.
PRIMESCIENTIST shows that research agents should choose where to spend their experiments, rather than keep pushing the same idea until the budget runs out.
Instead of following a single trajectory, it keeps competing executable plans in a tree. Experiment results update branch values, while the allocation policy explores more when resources are plentiful and concentrates on stronger branches as the budget shrinks.
On 12 FIRE-Bench tasks, it delivered 10.3% higher average reward than AutoResearch while using 50.6% fewer research attempts under the same token budget. Across the full evaluation, it used fewer attempts on 23 of 24 tasks.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include PRIMESCIENTIST, AutoResearch, FIRE-Bench, agents.
Source attribution: curated from rohanpaul_ai (@rohanpaul_ai).