Fictional Worldbuilding: Multi-Agent LLM Collaboration with Hierarchical Context Compression and Iterative Review
Two experiments across 20 diverse worldbuilding tasks, using GPT-OSS 120B and DeepSeek v3.2 as LLM backends, demonstrate a 95.0% success rate. The system generated 56-103 self-consistent concepts per world in 18-31 minutes with zero-conflict delivery. The architectural patterns validated here, including layer-as-budget compression, semantic-locality scheduling, and separation of generation and review, transfer to the broader class of knowledge-intensive, multi-agent LLM applications.
- ▪Two experiments across 20 diverse worldbuilding tasks, using GPT-OSS 120B and DeepSeek v3.2 as LLM backends, demonstrate a 95.0% success rate.
- ▪The system generated 56-103 self-consistent concepts per world in 18-31 minutes with zero-conflict delivery.
- ▪The architectural patterns validated here, including layer-as-budget compression, semantic-locality scheduling, and separation of generation and review, transfer to the broader class of knowledge-intensive, multi-agent LLM applications.
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Computer Science > Artificial Intelligence arXiv:2607.09403 (cs) [Submitted on 10 Jul 2026] Title:Fictional Worldbuilding: Multi-Agent LLM Collaboration with Hierarchical Context Compression and Iterative Review Authors:Jingbo Chen, He Wang, Wei Yuan, Yuqiao Lai, Zhenyan Lu View a PDF of the paper titled Fictional Worldbuilding: Multi-Agent LLM Collaboration with Hierarchical Context Compression and Iterative Review, by Jingbo Chen and 4 other authors View PDF HTML (experimental) Abstract:Worldbuilding, the construction of coherent fictional worlds, is a foundational task in game design and literary creation.
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