arXiv (All)
open access
CriticaLLM: Using LLMs as Autonomous Game Master for Tabletop Role-Playing Games
Large Language Models (LLMs) have shown impressive capabilities across many tasks, including processing large amounts of text, following instructions, and generating ideas. This allows the use of such models in various contexts, including games and Tabletop Role-Playing Games (TTRPGs), such as Dunge
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- LLM Agents as Static Level-k Players in Behavioural Games#1012249
- Large Language Models and Games: A Survey and Roadmap#328706
- RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data#925783
- First Make It Playable, Then Make It Good: Staged Interaction Learning for Small Dialogue-Game Agents#780971
- First Make It Playable, Then Make It Good: Staged Interaction Learning for Small Dialogue-Game Agents#779596
Record · ID 1356861
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive