arXiv:2606.05553v1 Announce Type: new Abstract: Role-playing language agents (RPLAs) should play characters whose values and behavior evolve as the story progresses, not maintain a fixed persona. Existing benchmarks measure factual recall at a given chapter, not whether responses align with the character's psychological trajectory, especially in scenarios the source text never explores. We introduce ArcANE (Arc-Aware Narrative Evaluation), an automatically constructed benchmark spanning 17 novels and 80 principal characters. A Character Arc segments the narrative into phases along a psychologi
The proliferation of advanced large language models necessitates improved evaluation methods that move beyond static character representations to assess dynamic, story-driven psychological evolution.
This benchmark addresses a critical limitation in evaluating AI agents, pushing beyond factual recall to the nuanced, contextual understanding required for sophisticated narrative interaction and human-like role-playing.
The focus of testing for role-playing language agents shifts from fixed personas and factual accuracy to dynamic character development and psychological alignment across evolving narratives.
- · AI developers focused on narrative generation
- · Entertainment industry
- · AI agents research community
- · Gaming companies
- · AI models that cannot adapt character personas
- · Benchmarks focused solely on static recall
- · Developers neglecting character depth
Improved evaluation leads to the development of more sophisticated and believable AI agents capable of nuanced character progression.
The ability of AI to portray evolving, psychologically consistent characters unlocks new applications in interactive storytelling, education, and therapy.
As AI agents become more adept at complex character arcs, the line between human and artificial narrative authorship blurs, potentially reshaping creative industries and media consumption.
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