PRIMESCIENTIST teaches research agents to allocate experiments, +10.3% reward with 50.6% fewer attempts
rohanpaul_ai · x · 2026-09-27
- Core idea: research agents should decide where to spend experiments rather than pushing one idea until the budget runs out. PRIMESCIENTIST keeps competing executable plans in a tree; results update branch values while the allocation policy explores broadly when resources are plentiful and concentrates on stronger branches as budget shrinks.
- Results: on 12 FIRE-Bench tasks it delivered 10.3% higher average reward than AutoResearch with 50.6% fewer research attempts under the same token budget, using fewer attempts on 23 of 24 tasks overall.
Related event: PrimeScientist Uses MCTS to Allocate Research Budget, Boosting Reward 10.3%(2 posts)→
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