AgentRxiv: Towards Collaborative Autonomous Research
Description of Resource
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.