A new arXiv paper argues that scientific studies of language agents need behavioral variables that can support hypotheses across different tasks and models. The authors frame this as a research problem in itself, rather than a side concern.
Their proposed solution is to learn and test a hierarchy of trajectory abstractions. This would give researchers a structured way to describe and compare agent behavior, though the abstract is truncated before concrete details are given.
The significance is in the framing: behavioral variables are treated as something to be explicitly designed and validated, not just assumed. This could help move language-agent research toward more cumulative, cross-task findings.