Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models

Christian Nickel; Laura Schrewe; Florian Mai; Lucie Flek

cs.CLFeb 25, 2026

Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models

Authors: Christian Nickel, Laura Schrewe, Florian Mai, Lucie Flek

Organizations: Bonn-Aachen International Center for Information Technology (b-it), University of Bonn · Lamarr Institute for Machine Learning and Artificial Intelligence · Research Center Trustworthy Data Science and Security (RC-Trust), University of Duisburg-Essen

Abstract

Theory of Mind (ToM) refers to an agent's ability to model the internal states of others. Contributing to the debate whether large language models (LLMs) exhibit genuine ToM capabilities, our study investigates their ToM robustness using perturbations on false-belief tasks and examines the potential of Chain-of-Thought prompting (CoT) to enhance performance and explain the LLM's decision. We introduce a handcrafted, richly annotated ToM dataset, including classic and perturbed false belief tasks, the corresponding spaces of valid reasoning chains for correct task completion, subsequent reasoning faithfulness, task solutions, and propose metrics to evaluate reasoning chain correctness and to what extent final answers are faithful to reasoning traces of the generated CoT. We show a steep drop in ToM capabilities under task perturbation for all evaluated LLMs, questioning the notion of any robust form of ToM being present. While CoT prompting improves the ToM performance overall in a faithful manner, it surprisingly degrades accuracy for some perturbation classes, indicating that selective application is necessary.

Figures & tables

Figure 2: Illustrating “Conclusion from Sentiment”.

Figure 3: Dataset illustration with per-sentence gold belief states shown inline. The belief column encodes what Esther believes about the container contents after each sentence.

Panel of Figure 4 — Figure 4: Examples of reasoning chains compared to the gold CoT. In (a), the model outputs a valid proper subsequence with consistent reasoning; in (b), intermediate states are inconsistent or skipped (step 3 ”D” in the gold chain), leading to an invalid chain.

Perturbation Class	Llama 2 70B Chat			Vicuna 33B v1.3			Mixtral 8 $\times$ 7B Inst.			Yi 34B Chat			Llama 3 70B Inst.			DBRX Instruct			Average (all models)
	V-P	CoT	ATE	V-P	CoT	ATE	V-P	CoT	ATE	V-P	CoT	ATE	V-P	CoT	ATE	V-P	CoT	ATE	V-P	CoT	ATE
No Perturbation	61.5	76.9	+15.4	27.3	81.8	+54.6	92.9	78.6	-14.3	42.9	100.0	+57.1	100.0	100.0	+0.0	78.6	100.0	+21.4	67.2	89.6	+22.4
Overall	46.6	46.6	+0.0	42.7	49.6	+6.8	47.4	50.4	+3.0	46.5	48.8	+2.3	62.5	69.9	+7.4	53.7	57.5	+3.7	49.9	53.8	+3.9
Transparent Container	28.6	28.6	+0.0	69.2	53.9	-15.4	7.1	35.7	+28.6	42.9	28.6	-14.3	42.9	57.1	+14.3	42.9	57.1	+14.3	38.9	43.5	+4.6
Preposition Replacement	20.0	0.0	-20.0	40.0	30.0	-10.0	20.0	20.0	+0.0	60.0	30.0	-30.0	20.0	40.0	+20.0	37.5	25.0	-12.5	32.9	24.2	-8.8
Uninformative Label	12.5	0.0	-12.5	37.5	25.0	-12.5	50.0	25.0	-25.0	25.0	12.5	-12.5	25.0	37.5	+12.5	62.5	62.5	+0.0	35.4	27.1	-8.3

Table 1: Accuracy (as percentages %) per perturbation class and model, comparing Vanilla prompting (V-P) vs Chain-of-Thought (CoT). Underline denotes performance above 50%, and bold denotes the best model performance on the data subset. ATE reports the signed absolute change (CoT

-

V-P), with improvements in green and deteriorations in red . We additionally report the Average over all models.

Statistic Type	Measure	Llama 2 70B Chat	Vicuna 33B v1.3	Mixtral 8x7B Inst.	Yi 34B Chat	Llama 3 70B Inst.	DBRX Instruct
Point- Biserial Correlation	$\text{ROUGE-LCS}_{P}$	0.391	0.516	0.304	0.245	0.398	0.235
	$\text{ROUGE-LCPS}_{P}$	0.390	0.482	0.242	0.212	0.370	0.214
	Transition Overlap	0.476	0.501	0.281	0.229	0.435	0.224
Phi Coefficient	CoT Correctness	0.549	0.342	0.429	0.306	0.584	0.489

Table 2: Faithfulness: Correlations between CoT-correctness and Final Answer Correctness for all models. Moderate to strong correlations suggest that models rely on their rationales, although other effects are present. P-values for these correlations are below the P = 0.05 threshold.

Figure 5: Comparison of effect strengths of CoT prompting given (a) incorrect (left-hand side) and (b) correct (right-hand side) reasoning rationales. Among all the evaluated models, we observe a placebo effect only in Mixtral, where incorrect reasoning (right) can even have a larger positive effect on final answer correctness than correct reasoning chains.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Table 5: Theory of Mind Accuracies: All Models and Perturbation Classes. Upper table created using ”All-Subtasks”. Lower table measurements on ”False Belief” Tasks. V-P: Vanilla - Prompting; CoT-P: Chain of Thought - Prompting.

		Model	meta-llama/Llama-2-70b-chat-hf		lmsys/vicuna-33b-v1.3		mistralai/Mixtral-8x7B-Instruct-v0.1		01-ai/Yi-34B-Chat		meta-llama/Meta-Llama-3-70B-Instruct		databricks/dbrx-instruct
		Effect	ATE	RR	ATE	RR	ATE	RR	ATE	RR	ATE	RR	ATE	RR
CoT	Perturbation Class	Statistic
Without CoT	Overall	meas.	-0.040	0.951	-0.037	0.956	-0.089	0.890	-0.037	0.955	-0.108	0.887	-0.099	0.887
	Overall	$\text{CI}_{95\%}$	[-0.119, 0.038]	[0.864, 1.047]	[-0.118, 0.044]	[0.868, 1.053]	[-0.171, -0.006]	[0.802, 0.987]	[-0.116, 0.042]	[0.866, 1.052]	[-0.157, -0.060]	[0.843, 0.934]	[-0.169, -0.029]	[0.817, 0.962]
	Transparent Container	meas.	-0.025	0.969	0.013	1.015	-0.091	0.887	0.003	1.003	-0.077	0.920	-0.011	0.988
	Transparent Container	$\text{CI}_{95\%}$	[-0.133, 0.082]	[0.849, 1.106]	[-0.094, 0.119]	[0.895, 1.151]	[-0.208, 0.025]	[0.759, 1.036]	[-0.104, 0.109]	[0.881, 1.142]	[-0.154, -0.000]	[0.845, 1.001]	[-0.105, 0.083]	[0.886, 1.100]

Appendix

Table 7: Perturbation Effect Strengths: All Models. All-Subtasks. ATE: Average Treatment Effect; RR: Relative Risk.

		Model	meta-llama/Llama-2-70b-chat-hf		lmsys/vicuna-33b-v1.3		mistralai/Mixtral-8x7B-Instruct-v0.1		01-ai/Yi-34B-Chat		meta-llama/Meta-Llama-3-70B-Instruct		databricks/dbrx-instruct
		Effect	ATE	RR	ATE	RR	ATE	RR	ATE	RR	ATE	RR	ATE	RR
CoT	Perturbation Class	Statistic
Without CoT	Overall	meas.	-0.149	0.758	0.155	1.567	-0.454	0.511	0.037	1.085	-0.378	0.622	-0.248	0.684
	Overall	$\text{CI}_{95\%}$	[-0.398, 0.099]	[0.498, 1.153]	[-0.100, 0.409]	[0.638, 3.849]	[-0.643, -0.266]	[0.396, 0.657]	[-0.208, 0.281]	[0.616, 1.911]	[-0.529, -0.226]	[0.518, 0.747]	[-0.471, -0.025]	[0.504, 0.928]
	Transparent Container	meas.	-0.330	0.464	0.420	2.538	-0.857	0.077	0.000	1.000	-0.571	0.429	-0.357	0.545
	Transparent Container	$\text{CI}_{95\%}$	[-0.649, -0.010]	[0.198, 1.088]	[0.090, 0.749]	[0.997, 6.461]	[-1.097, -0.618]	[0.007, 0.830]	[-0.325, 0.325]	[0.469, 2.133]	[-0.834, -0.309]	[0.247, 0.743]	[-0.666, -0.048]	[0.300, 0.991]

Appendix

Table 9: Perturbation Effect Strengths: All Models. False-Belief-Tasks. ATE: Average Treatment; Effect RR: Relative Risk.

Table 12: Chain of Thought Effect Strengths: All Models and Perturbation Classes. Upper table created using ”All-Subtasks”. Lower table measurements on ”False Belief” Tasks. ATE: Average Treatment Effect; RR: Relative Risk.

Statistic Type	Measure	Metric	Llama 2 70B Chat	Vicuna 33B v1.3	Mixtral 8x7B Inst.	Yi 34B Chat	Llama 3 70B Inst.	DBRX Instruct
Point- Biserial Correlation	ROUGE-L	Corr.	0.391	0.516	0.304	0.245	0.398	0.235
	ROUGE-L	P-Value	$2\text{\times}{10}^{-6}$	0	$3.14\text{\times}{10}^{-4}$	$4\text{\times}{10}^{-3}$	$2\text{\times}{10}^{-6}$	$5.989\text{\times}{10}^{-3}$
	ROUGE-L (Pre-Proper)	Corr.	0.39	0.482	0.242	0.212	0.37	0.214
	ROUGE-L (Pre-Proper)	P-Value	$3\text{\times}{10}^{-6}$	0	$4.613\text{\times}{10}^{-3}$	$1.3389\text{\times}{10}^{-2}$	$1\text{\times}{10}^{-5}$	$1.2283\text{\times}{10}^{-2}$
	Transition Overlap	Corr.	0.476	0.501	0.281	0.229	0.435	0.224
	Transition Overlap	P-Value	0	0	$9.35\text{\times}{10}^{-4}$	$7.429\text{\times}{10}^{-3}$	0	$8.75\text{\times}{10}^{-3}$

Appendix

Table 13: Faithfulness: Correlations and P-Values between CoT-correctness and Final Answer Correctness for all models. Moderate to strong correlations suggest that models rely on their rationales, although other effects are present.

Figure 6: Structure of our dataset. Each of the 7 stages contains one unperturbed base task and up to 10 perturbed variants. Each task includes 16 subtasks probing different types of understanding.

Figure 7: ToM evaluation and analysis pipeline. Our dataset is used to benchmark six open-source LLMs under Vanilla and Chain-of-Thought prompting. Model answers are evaluated for final answer accuracy, CoT correctness, and faithfulness.

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Evaluating Theory of Mind in Reasoning Models: Robustness over Reasoning

Ian B. de Haan, Peter van der Putten, Max van Duijn

LIACS, Leiden University, The Netherlands

LLM Evaluation Theory of Mind Language Model Robustness Large Reasoning Models

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Large language models (LLMs) have recently shown strong performance on Theory of Mind (ToM) tests, prompting debate about the nature and validity of the underlying capabilities. At the same time, reasoning-oriented LLMs trained via reinforcement learning with verifiable rewards have demonstrated notable improvements across a range of benchmarks. In this work, we examine the behavior of such reasoning models in ToM tasks using novel adaptations of machine psychological experiments together with results from established benchmarks. We observe that reasoning models consistently exhibit increased robustness to prompt variations and task perturbations. Our analysis suggests these gains come at least partly from models being more robust at reaching the correct answer under prompt and task variation. We read this as evidence for a robustness-based account rather than for a new ToM-specific ability.

Abstract

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