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This work develops a general framework that connects data-driven behavioural discovery with analysis and experimental investigation for trajectories of varying length, event order and agent roles, and identifies advances in coding, analysis and experimentation needed to support its wider use.
Scientific studies of language agents need behavioral variables that support hypotheses across tasks and models. We formulate this research problem as learning and testing a hierarchy of trajectory abstractions. A concrete recursive procedure first measures role- and phase-indexed events, proposes temporally constraine...
ReBeCA is introduced, a framework for analyzing the interpretable behavioral hierarchy governing the self-reflection outcome by modeling self-reflection trajectories as causal graphs and selecting observed parent candidates and evaluating their stability through a three-stage ICP-based pipeline.
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