Fictional Worldbuilding: Multi-Agent LLM Collaboration with Hierarchical Context Compression and Iterative Review
Quick Answer
This paper shows that The AutoWorldBuilder system utilizes multi-agent collaboration and hierarchical context compression to enhance fictional worldbuilding, achieving a 95.0% success rate across 20 tasks with LLMs like GPT-OSS 120B.
Quick Take
It effectively reduces token usage by 90% and improves proposal pass rates from 42% to over 85% through iterative review and conflict detection mechanisms.
Key Points
- Achieved 95.0% success rate in worldbuilding tasks using GPT-OSS 120B.
- Reduced token usage by approximately 90% with a four-layer context compression.
- Improved proposal pass rates from 42% to over 85% with iterative review.
- Utilized a DAG-based hybrid batch scheduler for semantic locality in task grouping.
- Supported zero-code extension with differentiated temperature configuration in agents.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Worldbuilding, the construction of coherent fictional worlds, is a foundational task in game design and literary creation. Large Language Models (LLMs) offer new possibilities for automated content generation, but their application to worldbuilding faces three challenges: context explosion that grows linearly with the building process, the tension between creative diversity and content consistency, and the absence of automated quality assurance. This paper presents AutoWorldBuilder, a multi-agent collaborative system that addresses these challenges through five integrated components: a structured concept network with conflict detection; a DAG-based hybrid batch scheduler that groups tasks by semantic locality; a four-layer context compression mechanism achieving approximately 90% token reduction; an iterative review system with specialized Auditor agents that improves proposal pass rates from 42% to over 85%; and a skill-driven agent architecture supporting zero-code extension with differentiated temperature configuration. 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.
| Comments: | 36 pages, 7 fig |
| Subjects: | Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.11; I.2.4 |
| Cite as: | arXiv:2607.09403 [cs.AI] |
| (or arXiv:2607.09403v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.09403 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jingbo Chen [view email]
[v1]
Fri, 10 Jul 2026 13:30:42 UTC (733 KB)
— Originally published at arxiv.org
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