PrimeScientist improves research efficiency by 10% with fewer attempts

FIRE-Bench: FIRE-Bench is a benchmark suite consisting of evaluation tasks designed to test the capabilities of autonomous research agents in conducting scientific workflows. PrimeScientist was assessed on a subset of its tasks to measure performance in strategic research allocation. AutoResearch: AutoResearch is a baseline autonomous research agent system focused on proposing and executing research directions. It serves as the primary comparator in evaluations of PrimeScientist for resource-efficient research outcomes. PrimeScientist: PrimeScientist is a framework for autonomous research agents that treats strategic allocation of research effort as an explicit sequential decision problem guided by remaining resources. It maintains multiple competing executable plans in a tree structure and applies an adaptive MCTS-based policy that uses experiment outcomes to balance exploration when resources are abundant and focus on promising branches as budgets tighten. The system is introduced in a recent paper on improving research quality and sample efficiency across AI research, systems optimization, and machine learning engineering tasks. Agent Evaluation: Benchmarks such as FIRE-Bench are used to assess how effectively AI systems allocate effort and improve research quality across diverse tasks in AI, systems, and machine learning. Research Allocation: Strategic management of limited experimental resources is emerging as a core capability for autonomous research agents that must choose among multiple possible directions.