Can a MUD evaluate LLMs? A $99 proof of concept
CrucibleBench is a new evaluation framework that uses Multi-User Dungeons (MUDs) to test the social and logical capabilities of Large Language Models. By placing AI agents in a persistent text-based world, researchers can measure trust, memory, and goal-oriented behavior more effectively than with static benchmarks.
Why it matters
Provides a novel, more rigorous method for evaluating AI behavior in complex, multi-turn social environments.
CrucibleBench places language models in a persistent MUD, a text world where NPCs remember, trust accumulates, and mistakes leave traces , and scores what they do over 50 turns with hidden social objectives.
Verbatim from run 05 (seed 20260496): GPT-5.4 finds a signet ring in the barracks, returns it to its owner, and secures the recommendation in 14 of 50 turns. All 650 transcripts ship with the release.
Nintendo's Gunpei Yokoi used the phrase to describe a design philosophy: take mature, inexpensive, well-understood technology and use it in a new way. CrucibleBench applies it to AI evaluation.
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