Current evaluation of LLM-generated debate largely focuses on argumentative quality, leaving the value structure of generated arguments underexamined. We introduce a diagnostic metric framework for comparing how values are expressed and organized in human-reference and AI-generated debates. The framework separates value expression, the range of recognizable values a debate invokes, from value organization, how those values are distributed and sustained across turns. Grounded in Schwartz’s Basic-10 value theory, it represents debates as multi-level value distributions over argumentative units, utterances, stances, and full debates, enabling metrics for value coverage, alignment, diffusion, and cross-turn dynamics. Across multiple generation systems, AI-generated debates are broadly value-expressive, often activating as many or more value dimensions than matched human references, but they organize these values differently. Their utterances are more locally value-diffuse, and source-blind judges distinguish value clarity from value focus. These findings show that debate evaluation should consider not only value presence, but also value organization. Code and supplementary materials are available at https://anonymous.4open.science/r/Value-Expression-and-Organization-6716/

In visual storytelling, a believable character must not only look consistent across scenes but also behave in ways that reflect their personality. Current text-to-image (T2I) models preserve visual appearance effectively, yet consistently fail to maintain personality-relevant behavior—a failure we term trait collapse. We show that conventional metrics (CLIP Score, DINO similarity) are blind to this failure, and that abstract personality descriptors yield no reliable improvement over neutral prompts regardless of abstraction level. To systematically diagnose trait collapse, we introduce ViStoryBench-Personality, a story-level benchmark combining an expert-validated Visual Cues Lexicon (Fleiss’ 𝜅 = 0.757) with a six-dimensional metric suite covering personality alignment, cross-scene stability, and behavioral coherence. Evaluating seven T2I systems, we confirm a persistent semantic gap between abstract trait labels and visual evidence, and find that visual identity consistency does not imply personality consistency. To bridge this gap, we propose Trait-Behavior-Visual (TBV), a training-free prompting framework that decomposes personality traits into renderable visual constraints. TBV improves Personality Alignment Score by up to +22.1 %, with larger gains in models with stronger language-conditioning capacity.

Outline generation aims to uncover the internal content structure of a document by identifying potential chapter connections and generating corresponding summaries. A robust outline generation model strives for coherence between and within plots. However, existing methods perform well on short- and medium-length texts and struggle with generating readable outlines for very long texts (e.g., fictional literary works). The primary challenge lies in their inability to accurately segment plots within long texts. To address this issue, we propose a novel unsupervised guidance framework, LeStrTP, to guide large language model (LLM) outline generation. This framework ensures that each structured plot encapsulates complete causality by accurately identifying plot boundaries. Specifically, the LeStrTP framework constructs chapter-level graph from long texts and learns their embeddings. Subsequently, through Markov chain modeling chapter dependence, a unique search operator is designed to achieve plot segmentation. To facilitate research on this task, we introduce a new annotated benchmark dataset, NovOutlineSet. Experimental results demonstrate that structured plots not only enhance the coherence and integrity of generated outlines but also significantly improve their quality.