arXiv paper proposes evaluating AI agents on resilience across repeated interactions
A new arXiv preprint argues that measuring whether an agent completes a single task is insufficient for judging fitness in long-running deployments. The authors propose evaluating agents on how well they hold up as challenges accumulate, including shifting conditions, repeated interactions, and reliance on human collaborators in shared workflows. The work frames resilience and considerate participation as dimensions that need dedicated benchmarks.