AgentRxiv: Towards Collaborative Autonomous Research
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.
67e22206ddc9b120cbde6fbe
Samuel Schmidgall
67e22206ddc9b120cbde6fbf
Michael Moor
admin_assigned
2025-03-25T09:34:00.877Z
6438d1d843d932c462404500
https://cdn-avatars.huggingface.co/v1/production/uploads/6438d1d843d932c462404500/6UrtJbed9N5ETbImgIh-C.png
Michael Moor
user
mdmoor
AgentRxiv: Towards Collaborative Autonomous Research
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.
AgentRxiv facilitates collaboration among LLM agent laboratories, enabling iterative improvements and higher performance across various benchmarks.
LLM agent laboratories preprint server reasoning techniques prompting techniques MATH-500 benchmark improvement