CrucibleBench is an independent research project that evaluates language model agents by placing them in a persistent MUD (multi-user dungeon) text world with hidden social objectives, NPC trust mechanics, and within-run state persistence. The Phase 1 proof-of-concept tested 13 models across 650 runs and surfaced a critical finding: a single LLM-judge component in the scoring stack reordered the leaderboard by up to six positions while aggregate reliability statistics remained silent. Three algorithmically-detected failure modes were identified — dialogue looping (14–66% of frontier runs), wrong-room interaction, and exploration paralysis. The project argues that MUD constraints (enumerable action space, explicit social feedback, persistent state) make agent behavior measurable in ways static benchmarks cannot. Phase 2 is in active development toward a validated, preregistered benchmark.
