@inproceedings{zhang-etal-2026-sentient,
title = "Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models",
author = "Zhang, Bang and
Ma, Ruotian and
Jiang, Qingxuan and
Wang, Peisong and
Chen, Jiaqi and
Xie, Zheng and
Chen, Xingyu and
Wang, Yue and
Ye, Fanghua and
Li, Jian and
Yang, Yifan and
Tu, Zhaopeng and
Li, Xiaolong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1905/",
pages = "38196--38224",
ISBN = "979-8-89176-395-1",
abstract = "Assessing how well a large language model (LLM) understands human, rather than merely text, remains an open challenge.To bridge the gap, we introduce Sentient Agent as a Judge(SAGE), an automated evaluation framework that measures an LLM{'}s higher-order social cognition.SAGE instantiates a ``Sentient Agent'' {--} an LLM-powered agent that simulates human-like emotional changes and inner thoughts to provide a more realistic evaluation of the tested model in multi-turn conversations.At every turn, the agent reasons about (i) how its emotion changes, (ii) how it feels, and (iii) how it should reply, yielding a numerical emotion trajectory and interpretable inner thoughts.Experiments on 100 supportive-dialogue scenarios show that the final Sentient emotion score correlates strongly with Barrett-Lennard Relationship Inventory (BLRI) ratings and utterance-level empathy metrics, validating psychological fidelity. Human evaluation further demonstrates 85.3{\%} consistency between the agent{'}s emotional reasoning and human judgments. We also build a public Sentient Leaderboard covering 18 commercial and open-source models that uncovers substantial gaps (up to 4$\times$) between frontier systems (GPT-4o-Latest, Gemini2.5-Pro) and earlier baselines, gaps not reflected in conventional leaderboards (e.g. Arena). SAGE thus provides a principled, scalable, and interpretable tool for tracking progress toward genuinely empathetic and socially adept language agents."
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<abstract>Assessing how well a large language model (LLM) understands human, rather than merely text, remains an open challenge.To bridge the gap, we introduce Sentient Agent as a Judge(SAGE), an automated evaluation framework that measures an LLM’s higher-order social cognition.SAGE instantiates a “Sentient Agent” – an LLM-powered agent that simulates human-like emotional changes and inner thoughts to provide a more realistic evaluation of the tested model in multi-turn conversations.At every turn, the agent reasons about (i) how its emotion changes, (ii) how it feels, and (iii) how it should reply, yielding a numerical emotion trajectory and interpretable inner thoughts.Experiments on 100 supportive-dialogue scenarios show that the final Sentient emotion score correlates strongly with Barrett-Lennard Relationship Inventory (BLRI) ratings and utterance-level empathy metrics, validating psychological fidelity. Human evaluation further demonstrates 85.3% consistency between the agent’s emotional reasoning and human judgments. We also build a public Sentient Leaderboard covering 18 commercial and open-source models that uncovers substantial gaps (up to 4\times) between frontier systems (GPT-4o-Latest, Gemini2.5-Pro) and earlier baselines, gaps not reflected in conventional leaderboards (e.g. Arena). SAGE thus provides a principled, scalable, and interpretable tool for tracking progress toward genuinely empathetic and socially adept language agents.</abstract>
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%0 Conference Proceedings
%T Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models
%A Zhang, Bang
%A Ma, Ruotian
%A Jiang, Qingxuan
%A Wang, Peisong
%A Chen, Jiaqi
%A Xie, Zheng
%A Chen, Xingyu
%A Wang, Yue
%A Ye, Fanghua
%A Li, Jian
%A Yang, Yifan
%A Tu, Zhaopeng
%A Li, Xiaolong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-sentient
%X Assessing how well a large language model (LLM) understands human, rather than merely text, remains an open challenge.To bridge the gap, we introduce Sentient Agent as a Judge(SAGE), an automated evaluation framework that measures an LLM’s higher-order social cognition.SAGE instantiates a “Sentient Agent” – an LLM-powered agent that simulates human-like emotional changes and inner thoughts to provide a more realistic evaluation of the tested model in multi-turn conversations.At every turn, the agent reasons about (i) how its emotion changes, (ii) how it feels, and (iii) how it should reply, yielding a numerical emotion trajectory and interpretable inner thoughts.Experiments on 100 supportive-dialogue scenarios show that the final Sentient emotion score correlates strongly with Barrett-Lennard Relationship Inventory (BLRI) ratings and utterance-level empathy metrics, validating psychological fidelity. Human evaluation further demonstrates 85.3% consistency between the agent’s emotional reasoning and human judgments. We also build a public Sentient Leaderboard covering 18 commercial and open-source models that uncovers substantial gaps (up to 4\times) between frontier systems (GPT-4o-Latest, Gemini2.5-Pro) and earlier baselines, gaps not reflected in conventional leaderboards (e.g. Arena). SAGE thus provides a principled, scalable, and interpretable tool for tracking progress toward genuinely empathetic and socially adept language agents.
%U https://aclanthology.org/2026.findings-acl.1905/
%P 38196-38224
Markdown (Informal)
[Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models](https://aclanthology.org/2026.findings-acl.1905/) (Zhang et al., Findings 2026)
ACL
- Bang Zhang, Ruotian Ma, Qingxuan Jiang, Peisong Wang, Jiaqi Chen, Zheng Xie, Xingyu Chen, Yue Wang, Fanghua Ye, Jian Li, Yifan Yang, Zhaopeng Tu, and Xiaolong Li. 2026. Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 38196–38224, San Diego, California, United States. Association for Computational Linguistics.