Evaluating Large Language Models in a Persistent Text-Based World: A $99 Proof of Concept

CrucibleBench introduces a MUD-based behavioral evaluation framework for large language models (LLMs) using persistent text worlds to measure social interaction and trust dynamics. The $99 proof-of-concept demonstrates that LLM judges significantly affect rankings, highlighting the need for per-subject audits in AI benchmarking.

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