title: AgentRxiv: Towards Collaborative Autonomous Research
summary: Progress in scientific discovery is rarely the result of a single "Eureka"
  moment, but is rather the product of hundreds of scientists incrementally
  working together toward a common goal. While existing agent workflows are
  capable of producing research autonomously, they do so in isolation, without
  the ability to continuously improve upon prior research results. To address
  these challenges, we introduce AgentRxiv-a framework that lets LLM agent
  laboratories upload and retrieve reports from a shared preprint server in order
  to collaborate, share insights, and iteratively build on each other's research.
  We task agent laboratories to develop new reasoning and prompting techniques
  and find that agents with access to their prior research achieve higher
  performance improvements compared to agents operating in isolation (11.4%
  relative improvement over baseline on MATH-500). We find that the best
  performing strategy generalizes to benchmarks in other domains (improving on
  average by 3.3%). Multiple agent laboratories sharing research through
  AgentRxiv are able to work together towards a common goal, progressing more
  rapidly than isolated laboratories, achieving higher overall accuracy (13.7%
  relative improvement over baseline on MATH-500). These findings suggest that
  autonomous agents may play a role in designing future AI systems alongside
  humans. We hope that AgentRxiv allows agents to collaborate toward research
  goals and enables researchers to accelerate discovery.
paper_authors._id: 67e22206ddc9b120cbde6fbe
paper_authors.name: Samuel Schmidgall
paper_authors._id: 67e22206ddc9b120cbde6fbf
paper_authors.name: Michael Moor
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:34:00.877Z
paper_authors._id: 6438d1d843d932c462404500
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6438d1d843d932c462404500/6UrtJbed9N5ETbImgIh-C.png
paper_authors.fullname: Michael Moor
paper_authors.type: user
paper_authors.user: mdmoor
paper_title: AgentRxiv: Towards Collaborative Autonomous Research
paper_summary: Progress in scientific discovery is rarely the result of a single "Eureka"
  moment, but is rather the product of hundreds of scientists incrementally
  working together toward a common goal. While existing agent workflows are
  capable of producing research autonomously, they do so in isolation, without
  the ability to continuously improve upon prior research results. To address
  these challenges, we introduce AgentRxiv-a framework that lets LLM agent
  laboratories upload and retrieve reports from a shared preprint server in order
  to collaborate, share insights, and iteratively build on each other's research.
  We task agent laboratories to develop new reasoning and prompting techniques
  and find that agents with access to their prior research achieve higher
  performance improvements compared to agents operating in isolation (11.4%
  relative improvement over baseline on MATH-500). We find that the best
  performing strategy generalizes to benchmarks in other domains (improving on
  average by 3.3%). Multiple agent laboratories sharing research through
  AgentRxiv are able to work together towards a common goal, progressing more
  rapidly than isolated laboratories, achieving higher overall accuracy (13.7%
  relative improvement over baseline on MATH-500). These findings suggest that
  autonomous agents may play a role in designing future AI systems alongside
  humans. We hope that AgentRxiv allows agents to collaborate toward research
  goals and enables researchers to accelerate discovery.
paper_ai_summary: AgentRxiv facilitates collaboration among LLM agent laboratories, enabling iterative improvements and higher performance across various benchmarks.
paper_ai_keywords: LLM agent laboratories preprint server reasoning techniques prompting techniques MATH-500 benchmark improvement