Third-Person Appraisal Agent: Simulating Human Emotional Reasoning in Text with Large Language Models (2025)

Abstract: Emotional reasoning is essential for improving human-AI interactions, particularly in mental health support and empathetic systems. However, current approaches, which primarily map sensory inputs to fixed emotion labels, fail to understand the intricate relationships between motivations, thoughts, and emotions, thereby limiting their ability to generalize across flexible emotional reasoning tasks. To address this, we propose a novel third-person appraisal agent that simulates human-like emotional reasoning through three phases: Primary Appraisal, Secondary Appraisal, and Reappraisal. In the Primary Appraisal phase, a third-person generator powered by a large language model (LLM) infers emotions based on cognitive appraisal theory. The Secondary Appraisal phase uses an evaluator LLM to provide feedback, guiding the generator in refining its predictions. The generator then uses counterfactual reasoning to adjust its process and explore alternative emotional responses. The Reappraisal phase utilizes reinforced fine-tuning (ReFT) by employing a reflective actor-critic framework to further enhance the model’s performance and generalization. This process uses reward signals and learns from appraisal trajectories without human annotations. Our approach outperforms baseline LLMs in various emotional reasoning tasks, demonstrating superior generalization and interpretability. To the best of our knowledge, this is the first cognition-based architecture designed to enhance emotional reasoning in LLMs, advancing AI towards human-like emotional understanding.

## introduction emotional reasoning is a critical cognitive process focused on understanding and interpreting emotions by analyzing the intricate relationships between a speaker's motivations, thoughts, and emo-tional expressions. this capability is essential in fields such as mental health support systems and empathetic conversational ai, as enhancing a model's ability to comprehend human emotions can significantly advance human-ai interaction. however, existing studies @xcite @xcite @xcite @xcite @xcite @xcite @xcite primarily focus on feature extraction-based approaches that map sensory inputs to a fixed set of emotion labels, which offer limited interpretability regarding the underlying reasons for emotion predictions, thereby reducing their transparency across a range of emotion reasoning tasks. to address this, emotional analysis must evolve beyond static labels and adopt human-like cognitive reasoning, establishing connections between emotions and their underlying causes. this leads to a critical research question: how can we develop emotion reasoning approaches that more closely mimic human understanding of emotions in various contexts? ## problem description: formulating as a generative task we propose a generative approach for zero-shot emotion reasoning and prediction based on textual input. each utterance is associated with a specific speaker and a set of emotional categories that may vary depending on the emotional types present in different dialogue datasets. the objectives are twofold: (1) generate an appraisal a i for each utterance u i , and (2) infer an emotion label ŷi based on this appraisal. for contextual understanding, we define a window of length l to gather the dialogue context c i for each utterance u i . this context consists of the current utterance and the preceding l -1 utterances, along with their corresponding speaker information. therefore, we frame the generative task using a general question-and-answer prompt template, as illustrated below: question: given the dialogue context c i , predict an emotion label for the target utterance u i . choose from happy, sad, neutral, angry, excited, frustrated. answer: generate an appraisal a i for the target utternace u i and then produce the final prediction ŷi . ## experiments & results in this section, we present three major experiments designed to evaluate the performance of our proposed model. the experiments are structured as follows: (1) a comparative analysis against llm baseline models; (2) an ablation study assessing the agentic workflow; (3) a comparative analysis of two vrl-based strategies for evaluating the effectiveness of the secondary appraisal phase and (4) a qualitative analysis of the model's appraisal performance on the dailydialog dataset. baselines: for comparison, we use mistral-7b-instruct-v0.3, gemma1.1-7b-instruct, llama3.1-8b-instruct, and mistral-nemo-instruct-2407-bnb-4bit as baseline models. note: mistral-nemo-instruct-2407 is a 12b-parameter llm. evaluation metrics: we report value accuracy for all three datasets, including iemocap, dailydialog and wassa2023 datasets. for the wassa2023 dataset, the accuracy for empathy and distress scores is computed based on the absolute difference between the predicted and gold-standard values, with a prediction considered correct if the absolute difference is less than or equal to 2. ## conclusion we introduce a novel agentic workflow that enables the training of a model capable of enhancing emotional reasoning capabilities without human annotations. specifically, this workflow allows the model to iteratively refine its emotional reasoning through reinforcement learning, even with a limited number of demonstration samples. our approach advances the development of explainable ai by training the model to perform emotion reasoning in a way that more closely aligns with human emotional understanding.