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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.
ReGround is introduced, a large-scale dataset for reviewer comment grounding that links 10,267 reviewer comments to 16,274 evidence in the original anonymous submission of 3,656 papers and exposes grounding reviewer comments as a difficult and practically important problem for scientific document understanding.
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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