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.
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.
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