Deriving Character Logic from Storyline as Codified Decision Trees

• URL: http://arxiv.org/abs/2601.10080v1
• Date: Thu, 15 Jan 2026 05:12:43 GMT
• Title: Deriving Character Logic from Storyline as Codified Decision Trees
• Authors: Letian Peng, Kun Zhou, Longfei Yun, Yupeng Hou, Jingbo Shang,
• Abstract summary: Role-playing (RP) agents rely on behavioral profiles to act consistently across diverse narrative contexts.<n>We propose Codified Decision Trees (CDT), a data-driven framework that induces an executable and interpretable decision structure from large-scale narrative data.
• Score: 67.01182739162142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• TriCEGAR: A Trace-Driven Abstraction Mechanism for Agentic AI [5.1181001367075]
  TriCEGAR is a trace-driven abstraction mechanism that automates state construction from execution logs.<n>We describe a framework-native implementation that captures typed agent lifecycle events and builds abstractions from traces.<n>We also show how run likelihoods enable anomaly detection as a guardrailing signal.
  arXiv  Detail & Related papers  (2026-01-30T14:01:47Z)
• TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAG [71.06073770344732]
  Agentic retrieval-augmented generation (RAG) formulates question answering as a multi-step interaction between reasoning and information retrieval.<n>We present TreePS-RAG, an online, tree-based RL framework for agentic RAG that enables step-wise credit assignment while retaining outcome-only rewards.
  arXiv  Detail & Related papers  (2026-01-11T14:07:30Z)
• ACT: Agentic Classification Tree [33.65390081055222]
  We present the Agentic Classification Tree (ACT), which extends decision-tree methodology to unstructured inputs by formulating each split as a natural-language question.<n> Experiments on text benchmarks show that ACT matches or surpasses prompting-based baselines while producing transparent and interpretable decision paths.
  arXiv  Detail & Related papers  (2025-09-30T15:54:08Z)
• Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation [69.45495166424642]
  We develop a robust and discriminative QA benchmark to measure temporal, causal, and character consistency understanding in narrative documents.<n>We then introduce Entity-Event RAG (E2RAG), a dual-graph framework that keeps separate entity and event subgraphs linked by a bipartite mapping.<n>Across ChronoQA, our approach outperforms state-of-the-art unstructured and KG-based RAG baselines, with notable gains on causal and character consistency queries.
  arXiv  Detail & Related papers  (2025-06-06T10:07:21Z)
• Codifying Character Logic in Role-Playing [37.92922713921964]
  This paper introduces Codified Profiles for role-playing, a novel approach that represents character logic as structured, executable functions for behavioral decision-making.<n>Our experiments demonstrate the significant benefits of codified profiles in improving persistence, updatability, and behavioral diversity.
  arXiv  Detail & Related papers  (2025-05-12T16:12:42Z)
• ReTreever: Tree-based Coarse-to-Fine Representations for Retrieval [64.44265315244579]
  We propose a tree-based method for organizing and representing reference documents at various granular levels.<n>Our method, called ReTreever, jointly learns a routing function per internal node of a binary tree such that query and reference documents are assigned to similar tree branches.<n>Our evaluations show that ReTreever generally preserves full representation accuracy.
  arXiv  Detail & Related papers  (2025-02-11T21:35:13Z)
• Semantic or Covariate? A Study on the Intractable Case of Out-of-Distribution Detection [70.57120710151105]
  We provide a more precise definition of the Semantic Space for the ID distribution. We also define the "Tractable OOD" setting which ensures the distinguishability of OOD and ID distributions.
  arXiv  Detail & Related papers  (2024-11-18T03:09:39Z)
• Detecting and Identifying Selection Structure in Sequential Data [53.24493902162797]
  We argue that the selective inclusion of data points based on latent objectives is common in practical situations, such as music sequences. We show that selection structure is identifiable without any parametric assumptions or interventional experiments. We also propose a provably correct algorithm to detect and identify selection structures as well as other types of dependencies.
  arXiv  Detail & Related papers  (2024-06-29T20:56:34Z)
• TapTree: Process-Tree Based Host Behavior Modeling and Threat Detection Framework via Sequential Pattern Mining [0.29465623430708915]
  This paper presents TapTree, an automated process-tree based technique to extract host behavior by compiling system events' semantic information. In our evaluation against a recent benchmark audit log dataset (DARPA OpTC), TapTree employs tree pattern queries and sequential pattern mining techniques to deduce the semantics of connected system events.
  arXiv  Detail & Related papers  (2023-12-10T15:12:55Z)
• Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets [13.94542147252982]
  We present Hierarchical Surrogate Rules (HSR), an algorithm that generates hierarchical rules based on user-defined parameters. We also contribute SuRE, a visual analytics (VA) system that integrates HSR and interactive surrogate rule visualizations. We evaluate the algorithm in terms of parameter sensitivity, time performance, and comparison with surrogate decision trees.
  arXiv  Detail & Related papers  (2022-01-19T17:03:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.