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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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