Title: Setting the DC: Tool-Grounded D\&D Simulations to Test LLM Agents
Primary area: datasets and benchmarks
Keywords: Dungeons and Dragons, Large Language Models, Multi-agent

Abstract: Dungeons and Dragons (D\&D) has been considered to be an intellectually challenging game for strategy planning and role-playing. Large language models (LLMs) are increasingly deployed as autonomous or semi-autonomous agents, yet most evaluations still target single-turn QA or short-horizon tasks. Assessing agentic performance in rules-constrained, multi-step settings is challenging because style-conforming narration can diverge from task optimality. In this work, we present D\&D Agents, a benchmark built on a multi-agent Dungeons \& Dragons simulator. In our benchmark, LLMs use tools to query and update the game state, assuming the roles of referee ('Dungeon Master', DM), players, and adversarial monsters in tactically rich combat. This benchmarked setting requires long-horizon planning, compliance with game rules, varied agent personas, and grounded interaction with the game state. We evaluate transcripts and tool traces along six axes—Function Usage, Parameter Fidelity, Acting Quality, Tactical Optimality, State Tracking, and Function Efficiency—capturing both capability and reliability in closed-loop play. Our benchmark allows researchers to run identical seeded scenarios with auditable traces, making error analysis and algorithmic improvements (prompting, tool-use policies, memory) straightforward and comparable.