CLJul 19

EvolvingWorld: An Open-Schema Framework for Co-Evolving Role-Play Agents and World Model in Interactive Literary World

arXiv:2607.1725014.31 citations
Predicted impact top 58% in CL · last 90 daysOriginality Incremental advance
AI Analysis

For researchers in interactive storytelling and role-play agents, this provides a novel open-schema framework and benchmark for long-horizon simulation, though the improvements are incremental over existing methods.

EvolvingWorld introduces a framework and benchmark for co-evolving characters and worlds in interactive literary simulations, improving long-horizon coherence over static persona approaches. Experiments show effective maintenance of persistent character and world development across 57 books with 138,596 training samples.

This paper introduces EvolvingWorld, a framework and benchmark for character and world co-evolution in interactive literary worlds. Existing systems either treat interactive literary simulation as static persona imitation or isolated scene generation, failing to capture how characters and worlds evolve together over time. To address this, EvolvingWorld models literary simulation as a long-horizon process where characters interact, scenes progress, and character and world states are persistently updated. Unlike prior systems relying on fixed schemas, EvolvingWorld adopts an open-schema framework to support simulation across diverse literary worlds. The framework consists of two coupled modules: a Character Agent for multi-character role-play and persistent profile evolution, and an LLM-based World Model for global and location/entity-level state maintenance and scene progression. Based on this architecture, we formulate 7 trainable tasks for scene initialization, interaction generation, and state update. We construct a dataset from 57 books, producing 138,596 supervised training samples and 222 snapshots for testing. Furthermore, we introduce a trajectory-level LLM-as-Judge evaluation protocol spanning 10 dimensions and 20 metrics. Experiments show that EvolvingWorld can improve long-horizon simulation by effectively maintaining persistent, coherent character and world development.

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