@inproceedings{chen-etal-2026-tide,
title = "{TIDE}: Tree-Induced Fine-Grained Diagnostic Evaluation from Model Behaviors",
author = "Chen, Jiayi and
Li, Hui and
Shen, Yujiong and
Peng, Qiyuan and
Li, Yanxin and
Huang, Chenhao and
Liu, Shaofan and
Zhang, Guoqiang and
Ye, Junjie and
Dou, Shihan and
Zhang, Ming and
Pan, Maxm and
Gui, Tao and
Zhang, Qi and
Huang, Xuanjing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1903/",
pages = "43364--43393",
ISBN = "979-8-89176-459-0",
abstract = "Benchmark performance is the dominant standard for comparing large language models (LLMs), typically summarized by a single aggregate score, such as accuracy, pass rate, or rubric score. This aggregate score provides limited information about a model{'}s underlying capabilities, yet understanding these capabilities is crucial for model development and evaluation. To this end, we propose Tree-Induced Diagnostic Evaluation (TIDE), which recasts diagnostic evaluation as behavior-driven decision-tree induction to perform fine-grained capability diagnosis. TIDE recursively builds a hierarchical capability tree from benchmark samples and model scores: at each node, an LLM judge proposes candidate splits and performs sample-to-group classification, and behavior-based information gain selects among them. We apply TIDE to five models across four benchmarks to demonstrate its effectiveness, and find fine-grained capability contrasts invisible at the aggregate level: Claude Opus 4.6 achieves 100{\%} on microeconomic market reasoning where GPT-5.4 scores 83.3{\%}, but trails GPT-5.4 on contract law reasoning with 77.8{\%} versus 88.9{\%}. We hope TIDE can be extended to past and future benchmarks so that the rich behavioral signal in evaluation results is fully leveraged for understanding model capabilities."
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<abstract>Benchmark performance is the dominant standard for comparing large language models (LLMs), typically summarized by a single aggregate score, such as accuracy, pass rate, or rubric score. This aggregate score provides limited information about a model’s underlying capabilities, yet understanding these capabilities is crucial for model development and evaluation. To this end, we propose Tree-Induced Diagnostic Evaluation (TIDE), which recasts diagnostic evaluation as behavior-driven decision-tree induction to perform fine-grained capability diagnosis. TIDE recursively builds a hierarchical capability tree from benchmark samples and model scores: at each node, an LLM judge proposes candidate splits and performs sample-to-group classification, and behavior-based information gain selects among them. We apply TIDE to five models across four benchmarks to demonstrate its effectiveness, and find fine-grained capability contrasts invisible at the aggregate level: Claude Opus 4.6 achieves 100% on microeconomic market reasoning where GPT-5.4 scores 83.3%, but trails GPT-5.4 on contract law reasoning with 77.8% versus 88.9%. We hope TIDE can be extended to past and future benchmarks so that the rich behavioral signal in evaluation results is fully leveraged for understanding model capabilities.</abstract>
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%0 Conference Proceedings
%T TIDE: Tree-Induced Fine-Grained Diagnostic Evaluation from Model Behaviors
%A Chen, Jiayi
%A Li, Hui
%A Shen, Yujiong
%A Peng, Qiyuan
%A Li, Yanxin
%A Huang, Chenhao
%A Liu, Shaofan
%A Zhang, Guoqiang
%A Ye, Junjie
%A Dou, Shihan
%A Zhang, Ming
%A Pan, Maxm
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F chen-etal-2026-tide
%X Benchmark performance is the dominant standard for comparing large language models (LLMs), typically summarized by a single aggregate score, such as accuracy, pass rate, or rubric score. This aggregate score provides limited information about a model’s underlying capabilities, yet understanding these capabilities is crucial for model development and evaluation. To this end, we propose Tree-Induced Diagnostic Evaluation (TIDE), which recasts diagnostic evaluation as behavior-driven decision-tree induction to perform fine-grained capability diagnosis. TIDE recursively builds a hierarchical capability tree from benchmark samples and model scores: at each node, an LLM judge proposes candidate splits and performs sample-to-group classification, and behavior-based information gain selects among them. We apply TIDE to five models across four benchmarks to demonstrate its effectiveness, and find fine-grained capability contrasts invisible at the aggregate level: Claude Opus 4.6 achieves 100% on microeconomic market reasoning where GPT-5.4 scores 83.3%, but trails GPT-5.4 on contract law reasoning with 77.8% versus 88.9%. We hope TIDE can be extended to past and future benchmarks so that the rich behavioral signal in evaluation results is fully leveraged for understanding model capabilities.
%U https://aclanthology.org/2026.emnlp-main.1903/
%P 43364-43393
Markdown (Informal)
[TIDE: Tree-Induced Fine-Grained Diagnostic Evaluation from Model Behaviors](https://aclanthology.org/2026.emnlp-main.1903/) (Chen et al., EMNLP 2026)
ACL
- Jiayi Chen, Hui Li, Yujiong Shen, Qiyuan Peng, Yanxin Li, Chenhao Huang, Shaofan Liu, Guoqiang Zhang, Junjie Ye, Shihan Dou, Ming Zhang, Maxm Pan, Tao Gui, Qi Zhang, and Xuanjing Huang. 2026. TIDE: Tree-Induced Fine-Grained Diagnostic Evaluation from Model Behaviors. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 43364–43393, Budapest, Hungary. Association for Computational Linguistics.