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arXiv · 2601.10080

Deriving Character Logic from Storyline as Codified Decision Trees

Letian Peng·Kun Zhou·Longfei Yun·Yupeng Hou·Jingbo Shang
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Abstract

Role-playing (RP) agents rely on behavioral profiles to act consistently across diverse narrative contexts, yet existing profiles are largely unstructured, non-executable, and weakly validated, leading to brittle agent behavior. We propose Codified Decision Trees (CDT), a data-driven framework that induces an executable and interpretable decision structure from large-scale narrative data. CDT represents behavioral profiles as a tree of conditional rules, where internal nodes correspond to validated scene conditions and leaves encode grounded behavioral statements, enabling deterministic retrieval of context-appropriate rules at execution time. The tree is learned by iteratively inducing candidate scene-action rules, validating them against data, and refining them through hierarchical specialization, yielding profiles that support transparent inspection and principled updates. Across multiple benchmarks, CDT substantially outperforms human-written profiles and prior profile induction methods on $85$ characters across $16$ artifacts, indicating that codified and validated behavioral representations lead to more reliable agent grounding.

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Letian Peng, Kun Zhou, Longfei Yun, Yupeng Hou, Jingbo Shang. 2026-01-15. Deriving Character Logic from Storyline as Codified Decision Trees. https://arxiv.org/abs/2601.10080

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