Beyond One World: Benchmarking Super Heros in Role-Playing Across
  Multiversal Contexts
Authors: name: Perapard Ngokpol, name: Kun Kerdthaisong, name: Pasin Buakhaw, name: Pitikorn Khlaisamniang, name: Supasate Vorathammathorn, name: Piyalitt Ittichaiwong, name: Nutchanon Yongsatianchot
Organization: Character-lab

Large language models (LLMs) are increasingly used as role-playing agents,
yet their capacity to faithfully and consistently portray version-specific
characters -- for example, superheroes across comic and cinematic universes --
remains underexplored. Superhero canons such as Marvel and DC provide a rich
testbed: decades of storytelling yield multiple incarnations of the same
character with distinct histories, values, and moral codes. To study this
problem, we introduce Beyond One World, a benchmark for character-grounded
roleplay spanning 30 iconic heroes and 90 canon-specific versions. The
benchmark comprises two tasks: (i) Canon Events, which probes factual recall of
pivotal life stages, and (ii) Moral Dilemmas, which confronts models with
ethically charged scenarios. We score responses for canonical accuracy and
reasoning fidelity under a framework that separates internal deliberation
("thinking") from outward decisions ("acting"). We further propose Think-Act
Matching, a metric that quantifies alignment between reasons and actions and
serves as a proxy for model trustworthiness. Experiments across reasoning- and
non-reasoning-oriented models yield three findings: (1) chain-of-thought
prompting improves narrative coherence in weaker models but can reduce
canonical accuracy in stronger ones; (2) cross-version generalization within a
character remains a major obstacle; and (3) models often excel at either
thinking or acting, but rarely both. Beyond One World exposes critical gaps in
multiversal consistency and reasoning alignment, offering a challenging
evaluation for role-playing LLMs.