# Beyond Context to Cognitive Appraisal: Emotion Reasoning as a Theory of Mind Benchmark for Large Language Models

arXiv: https://arxiv.org/abs/2506.00334 · HTML: https://mlsanity.com/paper/2506.00334/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning.
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 31 May 2025.

## Placed in

- LLM reasoning / [Audio-language model reasoning](https://mlsanity.com/lr/audio-language-model-reasoning/)

## Builds on (most cited first, 2 of 2)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions](https://mlsanity.com/paper/2310.15421/) (2023)

## Built on by (most cited first, 6 of 6)

- [CAREBench: Evaluating LLMs' Emotion Understanding by Assessing Cognitive Appraisal Reasoning](https://mlsanity.com/paper/2605.17176/) (2026)
- [Expressing Social Emotions: Misalignment Between LLMs and Human Cultural Emotion Norms](https://mlsanity.com/paper/2604.16757/) (2026)
- [Categorical Emotions or Appraisals - Which Emotion Model Explains Argument Convincingness Better?](https://mlsanity.com/paper/2511.07162/) (2025)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)
- [Chiaroscuro for Emotions: A Contrastive Emotion Benchmark Grounded in Appraisal Theory](https://mlsanity.com/paper/2609.03394/) (2026)
- [Emotion Experience, Expression, and Perception: Emotion Analysis on Multimodal Social Media Posts](https://mlsanity.com/paper/2609.18385/) (2026)

## Abstract (arXiv)

Datasets used for emotion recognition tasks typically contain overt cues that can be used in predicting the emotions expressed in a text. However, one challenge is that texts sometimes contain covert contextual cues that are rich in affective semantics, which warrant higher-order reasoning abilities to infer emotional states, not simply the emotions conveyed. This study advances beyond surface-level perceptual features to investigate how large language models (LLMs) reason about others' emotional states using contextual information, within a Theory-of-Mind (ToM) framework. Grounded in Cognitive Appraisal Theory, we curate a specialized ToM evaluation dataset1 to assess both forward reasoning - from context to emotion- and backward reasoning - from emotion to inferred context. We showed that LLMs can reason to a certain extent, although they are poor at associating situational outcomes and appraisals with specific emotions. Our work highlights the need for psychological theories in the training and evaluation of LLMs in the context of emotion reasoning.