Agentic- T o M : Cognition-Inspired Agentic Processing For Enhancing Theory of Mind Reasoning (2025)

Abstract: The capacity to attribute mental states like beliefs, desires, and intentions to oneself and others, known as Theory of Mind (ToM), is fundamental to human social intelligence. As Large Language Models (LLMs) are increasingly integrated into complex interactive systems, developing their ToM capabilities is crucial. Such capabilities enable LLMs to understand and predict human behavior, leading to more intuitive and productive interactions. However, current models often struggle with sophisticated reasoning about others’ perspectives. In this work, we propose “Agentic-ToM”, showing that guiding LLMs by embedding psychologically-grounded functions for capabilities such as ‘perspective taking’ and mental state tracking markedly improves their proficiency in ToM tasks. We evaluate the approach on three diverse ToM datasets and show that this method significantly outperforms baselines across all tasks without requiring task-specific modifications.

## introduction theory of mind (tom), the capacity to attribute mental states such as beliefs, desires, and intentions to oneself and others, is fundamental to human social intelligence @xcite . as large language models (llms) are increasingly integrated into complex human-interactive systems @xcite @xcite , robust tom capabilities are crucial for enabling them to interpret human intentions, predict behavior, and engage in productive interactions @xcite . despite advancements, llms' proficiency in sophisticated tom reasoning remains contested (ullman, 2023; @xcite . while some models show near-human performance on specific benchmarks like tomi @xcite , significant shortcomings persist, especially in tasks requiring deeper inference or generalization to complex scenarios @xcite . llms often struggle with tracking evolving mental states, higher-order tom, and contextual cues (ullman, 2023). this limitation is particularly salient as we move towards llm-based agents, which will require tom to operate effectively in human environments, unlike simple llm chat interfaces. @xcite . addressing this gap is essential for aligning llm reasoning with human social understanding. here, cognitive psychology offers valuable insights. human tom often involves deliberate, structured reasoning-referred to as "system 2" thinking @xcite , particularly in complex social situations, rather than solely relying on intuitive processes ("system 1" thinking) @xcite . this includes active "perspective taking": simulating the other's viewpoint and explicit mental state tracking @xcite . this human capacity for structured, conscious reasoning about mental states motivates our work: if humans benefit from deliberate tom processing, llms might similarly benefit from guided, structured reasoning. in this paper, we propose agentic-tom, a method for enhancing llms' tom capabilities by framing them as agents and guiding their reasoning with psychology-inspired prompt functions, which we term cognitive tools. drawing on concepts such as deliberate perspective taking and systematic mental-state tracking, these tools can be invoked by the llm to explicitly analyze different agents' perspectives and maintain coherent representations of their knowledge and beliefs, thereby imposing a structured framework on their reasoning process. we evaluate our approach on three diverse tom datasets spanning seven tasks and show that it consistently outperforms baseline methods. additionally, our method demonstrates strong generalizability, yielding performance gains without requiring task-specific modifications to the core functions. ## related work machine theory of mind. the development of computational systems exhibiting theory of mind (tom) -the capacity to attribute and reason about mental states has been a persistent objective in artificial intelligence research. contemporary llms have exhibited substantial improvements, with performance metrics on established tom benchmarks like tomi @xcite and bigtom @xcite approaching or exceeding human accuracy. notwithstanding these advancements, the robustness of llm-based tom remains a subject of scrutiny @xcite . these concerns, alongside the saturation of existing benchmarks, have necessitated advancements in tom evaluation methodologies. these include evaluations of higher-order tom reasoning (e.g., iterated mental state attributions) @xcite , performance in naturalistic dialogue contexts @xcite , and the creation of comprehensive datasets for evaluating a wider spectrum of tomrelated abilities. recent benchmarks, such as open-tom @xcite , aim for more holistic assessments, for example, by evaluating llms' capabilities to understand mental states such as emotion or through diverse psychometrically-inspired tasks like faux-pas detection @xcite . this trend reflects a consensus that robust machine tom requires proficiency beyond classical falsebelief tasks, extending to a broader range of socialcognitive competencies. ## agentic-tom: our approach our approach leverages the structured output/function-calling mechanism of llms @xcite . we structure our functions as simple but specialized text prompts, which we refer to as cognitive tools. these are inspired by fundamental concepts in psychology (detailed in section 3). the llm autonomously decides which of these cognitive tools to invoke iteratively, including determining any necessary input arguments. invoking a cognitive tool returns a specific prompt that directs the llm's subsequent reasoning process. cognitive tool characteristics. each tool within our framework comprises two components: tool names and their corresponding descriptions are supplied to the llm via the system prompt. this information is also made available to the model during each api call that enables tool use (when a structured response parseable into a tool name and arguments is anticipated), consistent with formats such as the openai api (openai, 2023). cognitive tools. to guide the llm's reasoning in a manner that approximates human tom processes, we handcraft three default tools: (1) perspective-taking, (2) mental state modeling, and (3) reflection and synthesis 1 . operational framework. the overall method for employing these tools can be delineated into two primary stages: (1) planning, and (2) iterative tool invocation (demonstrated in figure 1 ). method: agentic-tom qwen-32b 71.4 (-6.9) 57.5 (+6.7) 53.3 (+7.5) 44.2 (+1.7) 52.0 (-3.3) 46.0 (+4.0) 50.6 (+5.6) 72.5 (+2.0) 77.2 (-0.7) 86.9 (-0.4) qwen-72b 76.3 (-4.5) 65.8 (-5.0) 57.5 (+6.7) 53.8 (+15.5) 67.0 (+4.0) 59.0 (+10.3) 58.6 (+13.6) 73.5 (+3.5) 71.8 (-3.7) 82.0 (+0.7) gpt-4o-mini 63.2 (-6.8) 56.7 (-5.0) 50.8 (+8.3) 37.6 (+3.4) 58.6 (-6.7) 39.3 (-13.4) 46.7 (+3.4) 73.5 (-1.5) 70.4 (-4.0) 79.4 (-4.9) gpt-4o 79.5 (+2. loop. this iterative process continues until the llm either calls a designated "answer" tool or reaches a predefined maximum number of allowed tool calls. each step within this iterative invocation involves two sub-stages: first, the model invokes a tool based on its established plan and the provided tool descriptions, subsequently receiving the specialized tool prompt designed to focus its attention on a particular tom-relevant reasoning process. second, the llm responds to this prompt, thereby executing the targeted reasoning. we discuss further efficiency improvements in appendix a. ## conclusion this paper introduces agentic-tom, a novel and generalizable method that enhances llms' tom by integrating psychologically inspired cognitive tools. these handcrafted functions enable llms to autonomously engage in structured reasoning, such as perspective-taking and mental state tracking, during inference. our comprehensive evaluation across multiple diverse tom tasks demonstrates that agentic-tom consistently and significantly outperforms existing baselines, with more capable llms showing the most pronounced improvements, all without requiring task-specific modifications. we also show that an ablation of our methodology involving a single prompt encompassing our planning and tool prompts also significantly outperforms baselines, but fails at tasks requiring more complex reasoning. our work represents a step towards llms that are autonomously capable of tom, and exhibit higher social intelligence by extension.