@inproceedings{gan-etal-2026-brandeval,
title = "{B}rand{E}val: A Two-Track Multi-Agent Benchmark for Risk-Sensitive {LLM} Crisis Communication",
author = "Gan, Haotian and
Tang, Weidong and
Sun, Hanbin and
Luo, Yi and
Li, Xintian and
Wang, Jiayong and
Li, Wanyue and
Li, Yudong",
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.649/",
pages = "13631--13657",
ISBN = "979-8-89176-462-0",
abstract = "Evaluating LLMs for crisis communication requires more than judging isolated responses, because public feedback and competitor actions can reshape trust over time. Existing benchmarks remain largely static and single-turn, offering limited evidence about long-horizon risk-sensitive communication. We introduce \textbf{BrandEval}, a two-track benchmark for product-crisis communication, focusing on public brand responses to product-related safety, quality, service, and trust incidents. BrandEval-Static evaluates stakeholder-aware strategic analysis, while BrandEval-Dynamic stress-tests response policies in \textbf{BrandPolis}, a controlled multi-agent sandbox with consumer feedback, competitor responses, and trust-risk trajectories. We also construct \textbf{BrandSRD}, a Chinese crisis-response dataset, and instantiate a reference Strategic Agent using \textbf{Strategic Rationale (SR)}. Across proprietary and open-source models, we find that static strategic quality is informative but incomplete: it aligns with some end-state outcomes but does not reliably predict volatility and delayed recovery; dynamic stress tests further expose tail-risk failures under manipulative or weakly grounded policies. The code and dataset will be available at https://github.com/htgan-ai/BrandEval."
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<abstract>Evaluating LLMs for crisis communication requires more than judging isolated responses, because public feedback and competitor actions can reshape trust over time. Existing benchmarks remain largely static and single-turn, offering limited evidence about long-horizon risk-sensitive communication. We introduce BrandEval, a two-track benchmark for product-crisis communication, focusing on public brand responses to product-related safety, quality, service, and trust incidents. BrandEval-Static evaluates stakeholder-aware strategic analysis, while BrandEval-Dynamic stress-tests response policies in BrandPolis, a controlled multi-agent sandbox with consumer feedback, competitor responses, and trust-risk trajectories. We also construct BrandSRD, a Chinese crisis-response dataset, and instantiate a reference Strategic Agent using Strategic Rationale (SR). Across proprietary and open-source models, we find that static strategic quality is informative but incomplete: it aligns with some end-state outcomes but does not reliably predict volatility and delayed recovery; dynamic stress tests further expose tail-risk failures under manipulative or weakly grounded policies. The code and dataset will be available at https://github.com/htgan-ai/BrandEval.</abstract>
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%0 Conference Proceedings
%T BrandEval: A Two-Track Multi-Agent Benchmark for Risk-Sensitive LLM Crisis Communication
%A Gan, Haotian
%A Tang, Weidong
%A Sun, Hanbin
%A Luo, Yi
%A Li, Xintian
%A Wang, Jiayong
%A Li, Wanyue
%A Li, Yudong
%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 gan-etal-2026-brandeval
%X Evaluating LLMs for crisis communication requires more than judging isolated responses, because public feedback and competitor actions can reshape trust over time. Existing benchmarks remain largely static and single-turn, offering limited evidence about long-horizon risk-sensitive communication. We introduce BrandEval, a two-track benchmark for product-crisis communication, focusing on public brand responses to product-related safety, quality, service, and trust incidents. BrandEval-Static evaluates stakeholder-aware strategic analysis, while BrandEval-Dynamic stress-tests response policies in BrandPolis, a controlled multi-agent sandbox with consumer feedback, competitor responses, and trust-risk trajectories. We also construct BrandSRD, a Chinese crisis-response dataset, and instantiate a reference Strategic Agent using Strategic Rationale (SR). Across proprietary and open-source models, we find that static strategic quality is informative but incomplete: it aligns with some end-state outcomes but does not reliably predict volatility and delayed recovery; dynamic stress tests further expose tail-risk failures under manipulative or weakly grounded policies. The code and dataset will be available at https://github.com/htgan-ai/BrandEval.
%U https://aclanthology.org/2026.findings-emnlp.649/
%P 13631-13657
Markdown (Informal)
[BrandEval: A Two-Track Multi-Agent Benchmark for Risk-Sensitive LLM Crisis Communication](https://aclanthology.org/2026.findings-emnlp.649/) (Gan et al., Findings 2026)
ACL
- Haotian Gan, Weidong Tang, Hanbin Sun, Yi Luo, Xintian Li, Jiayong Wang, Wanyue Li, and Yudong Li. 2026. BrandEval: A Two-Track Multi-Agent Benchmark for Risk-Sensitive LLM Crisis Communication. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 13631–13657, Budapest, Hungary. Association for Computational Linguistics.