TIDE: Tree-Induced Fine-Grained Diagnostic Evaluation from Model Behaviors
Read the original on arXiv Machine Learning →TIDE (Tree-Induced Diagnostic Evaluation) transforms diagnostic evaluation into a behavior-driven decision-tree induction process, enabling fine-grained capability diagnosis of large language models. By recursively building a hierarchical capability tree from benchmark samples and model scores, TIDE identifies nuanced contrasts between models that aggregate metrics miss—such as Claude Opus 4.6 outperforming GPT-5.4 on microeconomic market reasoning while falling behind on contract law reasoning. The method leverages LLM judges to propose splits and uses behavior-based information gain to select them, demonstrating its effectiveness across five models and four benchmarks.
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