--- title: PrimeScientist: research agents that choose where to... description: PrimeScientist introduces a new approach to research agents that dynamically allocates experiment budget across competing plans organized in a tree structure,... canonical: https://daily.dev/posts/primescientist-research-agents-that-choose-where-to-spend-their-experiment-budget-ls51khyuv twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: PrimeScientist: research agents that choose where to spend their experiment budget | daily.dev og:description: PrimeScientist introduces a new approach to research agents that dynamically allocates experiment budget across competing plans organized in a tree structure,... og:url: https://daily.dev/posts/primescientist-research-agents-that-choose-where-to-spend-their-experiment-budget-ls51khyuv og:image: https://api.daily.dev/og/posts/Ls51KhYUv.png og:image:alt: PrimeScientist: research agents that choose where to spend their experiment budget og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # PrimeScientist: research agents that choose where to spend their experiment budget **[Collections](https://daily.dev/sources/collections)** · 1 min read · 1 upvotes · 0 comments ## Summary PrimeScientist introduces a new approach to research agents that dynamically allocates experiment budget across competing plans organized in a tree structure, exploring broadly when resources are abundant and narrowing focus as budget shrinks. On 12 FIRE-Bench tasks, it achieved 10.3% higher average reward than AutoResearch while using 50.6% fewer research attempts, and used fewer attempts on 23 of 24 evaluated tasks. A further finding shows agents that explored broadly before tackling hard problems scored 74.54% versus 56.50% for agents that attacked hard problems directly, suggesting exploration order matters as much as compute budget. ## Content A new system called PrimeScientist rethinks how research agents allocate their effort. Instead of pushing a single idea until the budget runs out, it maintains competing executable plans in a tree structure. Experiment results update branch values, and the allocation policy shifts dynamically: explore widely when resources are plentiful, concentrate on stronger branches as the budget shrinks. The results are hard to argue with. On 12 FIRE-Bench tasks, PrimeScientist 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. There's also a finding worth sitting with: agents that explored widely before tackling hard cases averaged 74.54% on 4 tasks, versus 56.50% for agents that went straight at the hard problems. That's a meaningful gap, and it suggests the order and breadth of exploration matters as much as the raw compute budget. The core idea is simple but easy to overlook - letting the agent decide *where* to spend its experiments, rather than just *how many* to run. ## Questions this post answers ### What is PrimeScientist and how does it improve research agent performance over AutoResearch? PrimeScientist is a research agent system that maintains competing executable plans in a tree structure instead of pursuing a single idea until budget runs out, dynamically shifting between wide exploration and focused exploitation 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. _Track emerging approaches to agent budget allocation and research automation on daily.dev._ ### Does exploring multiple approaches before tackling hard problems actually improve agent performance? Yes, agents that explored widely before tackling hard cases averaged 74.54% on a set of 4 tasks, compared to 56.50% for agents that attacked hard problems directly. This roughly 18-point gap suggests that the order and breadth of exploration matters as much as the total compute budget available to a research agent. _Developers designing agent workflows can follow findings like this on daily.dev to inform exploration strategies._ ## Similar posts on daily.dev - [Let the AI Do the Experimenting](https://daily.dev/posts/let-the-ai-do-the-experimenting-skk91pa8h) · Towards Data Science · 2 upvotes · 0 comments - [AI agents can't yet do open-ended AI research](https://daily.dev/posts/ai-agents-can-t-yet-do-open-ended-ai-research-6znvssxya) · AI Snake Oil · 40 upvotes · 5 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents) [View this post on daily.dev](https://daily.dev/posts/primescientist-research-agents-that-choose-where-to-spend-their-experiment-budget-ls51khyuv) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"PrimeScientist: research agents that choose where to spend their experiment budget","url":"https://daily.dev/posts/primescientist-research-agents-that-choose-where-to-spend-their-experiment-budget-ls51khyuv","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/primescientist-research-agents-that-choose-where-to-spend-their-experiment-budget-ls51khyuv"},"datePublished":"2026-09-27T00:11:37.761Z","dateModified":"2026-09-27T00:12:11.190Z","description":"PrimeScientist introduces a new approach to research agents that dynamically allocates experiment budget across competing plans organized in a tree structure,...","image":"https://pbs.twimg.com/media/HTLn_11b0AA6vjS.jpg","thumbnailUrl":"https://pbs.twimg.com/media/HTLn_11b0AA6vjS.jpg","isAccessibleForFree":true,"articleSection":"Collections","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Collections","logo":"https://media.daily.dev/image/upload/s--fk_6ycEi--/f_auto,q_auto/v1780996001/logos/collections?_a=BAMAMiWQ0","url":"https://daily.dev/sources/collections"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/primescientist-research-agents-that-choose-where-to-spend-their-experiment-budget-ls51khyuv","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"ai-agents","timeRequired":"PT1M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Collections","item":"https://daily.dev/sources/collections"},{"@type":"ListItem","position":3,"name":"PrimeScientist: research agents that choose where to spend their experiment budget"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/primescientist-research-agents-that-choose-where-to-spend-their-experiment-budget-ls51khyuv#faq","mainEntity":[{"@type":"Question","name":"What is PrimeScientist and how does it improve research agent performance over AutoResearch?","acceptedAnswer":{"@type":"Answer","text":"PrimeScientist is a research agent system that maintains competing executable plans in a tree structure instead of pursuing a single idea until budget runs out, dynamically shifting between wide exploration and focused exploitation as the budget shrinks. 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