@inproceedings{li-etal-2026-coreflect,
title = "{C}o{R}eflect: A Reflective Co-Evolution Framework for Improving Conversational Evaluation",
author = "Li, Yunzhe and
Feng, Richie Yueqi and
Wei, Tianxin and
Hsu, Chin-Chia",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1990/",
pages = "42407--42425",
ISBN = "979-8-89176-462-0",
abstract = "Evaluating multi-turn conversational systems remains a fundamental challenge. Conventional pipelines rely on manual rubrics and fixed context, a static approach that limits coverage and fails to capture emergent dialogue behaviors. To address this, we introduce CoReflect (A Reflective Co-Evolution Framework for Improving Conversational Evaluation), which unifies dialogue simulation and evaluation into an adaptive, iterative process. CoReflect employs a conversation planner that generates structured templates to guide a user simulator through diverse, goal-directed dialogues. Subsequently, a reflective analyzer processes these dialogues to identify systematic behavioral patterns and automatically refine the evaluation rubrics. Crucially, the insights from the conversation analysis are fed back into the planner to update conversation templates for subsequent iterations. This co-evolution loop ensures that the complexity of test cases and the diagnostic precision of rubrics improve in tandem. By minimizing human intervention, CoReflect provides a scalable and self-refining methodology that allows evaluation protocols to adapt alongside the rapidly advancing capabilities of dialogue models."
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<abstract>Evaluating multi-turn conversational systems remains a fundamental challenge. Conventional pipelines rely on manual rubrics and fixed context, a static approach that limits coverage and fails to capture emergent dialogue behaviors. To address this, we introduce CoReflect (A Reflective Co-Evolution Framework for Improving Conversational Evaluation), which unifies dialogue simulation and evaluation into an adaptive, iterative process. CoReflect employs a conversation planner that generates structured templates to guide a user simulator through diverse, goal-directed dialogues. Subsequently, a reflective analyzer processes these dialogues to identify systematic behavioral patterns and automatically refine the evaluation rubrics. Crucially, the insights from the conversation analysis are fed back into the planner to update conversation templates for subsequent iterations. This co-evolution loop ensures that the complexity of test cases and the diagnostic precision of rubrics improve in tandem. By minimizing human intervention, CoReflect provides a scalable and self-refining methodology that allows evaluation protocols to adapt alongside the rapidly advancing capabilities of dialogue models.</abstract>
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%0 Conference Proceedings
%T CoReflect: A Reflective Co-Evolution Framework for Improving Conversational Evaluation
%A Li, Yunzhe
%A Feng, Richie Yueqi
%A Wei, Tianxin
%A Hsu, Chin-Chia
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F li-etal-2026-coreflect
%X Evaluating multi-turn conversational systems remains a fundamental challenge. Conventional pipelines rely on manual rubrics and fixed context, a static approach that limits coverage and fails to capture emergent dialogue behaviors. To address this, we introduce CoReflect (A Reflective Co-Evolution Framework for Improving Conversational Evaluation), which unifies dialogue simulation and evaluation into an adaptive, iterative process. CoReflect employs a conversation planner that generates structured templates to guide a user simulator through diverse, goal-directed dialogues. Subsequently, a reflective analyzer processes these dialogues to identify systematic behavioral patterns and automatically refine the evaluation rubrics. Crucially, the insights from the conversation analysis are fed back into the planner to update conversation templates for subsequent iterations. This co-evolution loop ensures that the complexity of test cases and the diagnostic precision of rubrics improve in tandem. By minimizing human intervention, CoReflect provides a scalable and self-refining methodology that allows evaluation protocols to adapt alongside the rapidly advancing capabilities of dialogue models.
%U https://aclanthology.org/2026.findings-emnlp.1990/
%P 42407-42425
Markdown (Informal)
[CoReflect: A Reflective Co-Evolution Framework for Improving Conversational Evaluation](https://aclanthology.org/2026.findings-emnlp.1990/) (Li et al., Findings 2026)
ACL