E nigma T o M : Improve LLM s’ Theory-of-Mind Reasoning Capabilities with Neural Knowledge Base of Entity States (2025)

Abstract: Theory-of-Mind (ToM), the ability to infer others’ perceptions and mental states, is fundamental to human interaction but remains challenging for Large Language Models (LLMs). While existing ToM reasoning methods show promise with reasoning via perceptual perspective-taking, they often rely excessively on off-the-shelf LLMs, reducing their efficiency and limiting their applicability to high-order ToM reasoning. To address these issues, we present EnigmaToM, a novel neuro-symbolic framework that enhances ToM reasoning by integrating a Neural Knowledge Base of entity states (Enigma) for (1) a psychology-inspired iterative masking mechanism that facilitates accurate perspective-taking and (2) knowledge injection that elicits key entity information. Enigma generates structured knowledge of entity states to build spatial scene graphs for belief tracking across various ToM orders and enrich events with fine-grained entity state details. Experimental results on ToMi, HiToM, and FANToM benchmarks show that EnigmaToM significantly improves ToM reasoning across LLMs of varying sizes, particularly excelling in high-order reasoning scenarios.

## introduction theory-of-mind (tom), the ability to understand that others have perceptions and mental states different from one's own, is fundamental to effective communication and social interaction @xcite . tom reasoning can be first-order, involving the understanding of another's mental state, or higher-order, requiring recursive thinking about others' beliefs. higherorder tom reasoning is particularly vital in realworld contexts such as negotiation @xcite . as large language models (llms) become increasingly sophisticated in imitating human a vital prerequisite for human tom reasoning is perceptual perspective-taking (referred to as "perspective-taking" thereafter), which is the process of inferring the perception of other characters @xcite . in the case of tom reasoning with llms, perspectivetaking alleviates the reasoning burden of llms by identifying events that are observable by a given character and removing unobservable ones. centered around perspective-taking, numerous methods have been proposed. simulatedtom @xcite and discrete world models (dwm) @xcite perform perspective-taking by directly prompting llms. while one may appreciate these methods' simplicity, the quality of perspective-taking is largely dependent on the capability of llms. symbolictom, timetom, and per-cepttom took a neuro-symbolic approach. time-tom @xcite and percepttom @xcite utilize temporal and perceptual information of events to derive characters' perception by extracting common timestamps or perceived characters. however, accurately extracting perceived timestamps or perceivers becomes difficult as the length or complexity of the event trajectory increases. the most relevant work to ours is symbolictom, where perspective-taking is conducted by maintaining multiple belief graphs @xcite . however, symbolictom constructs belief graphs using less powerful models including wanli @xcite and openie @xcite , limiting its generalizability to tom tasks that involve complicated events. further, as noted by @xcite , symbolictom lacks efficiency as the depth of tom reasoning increases (see §3.4 for analysis). given the need for accurate and efficient perspective-taking in tom reasoning, we introduce entity-guided masking (enigmatom), a neurosymbolic framework enhancing llms' tom reasoning (figure 1 ). perspective-taking relies on reasoning about event implications, where information about the states of key entities is crucial @xcite . enigmatom employs a neural knowledge base (enigma) to generate structured entity-state information ( §3.1). this entity-state information supports spatial scene graph construction for perspective-taking ( §3.3) and event elicitation through knowledge injection ( §3.2). experiment results show that enigmatom improves the tom reasoning capabilities of a range of llms. furthermore, the iterative masking mechanism, grounded by theories from psychology @xcite , guarantees the efficacy of enigmatom across tom reasoning of varying orders. we summarize our contributions as follows: early work includes cyc, conceptnet, and dbpedia @xcite @xcite . @xcite introduced event2mind, an event-based knowledge graph that captures characters' intentions and reactions. subsequently, @xcite introduced atomic, a commonsense knowledge graph that models if-then relationships for simple events. to explore more complex events, @xcite introduced openpi, a dataset for entity state tracking in procedures. openpi was extended to openpi2.0 by introducing entity saliency scores and entity canonicalization @xcite . parallel efforts have developed neural models, including a gru-based encoder-decoder model for event2mind @xcite , a decoder-only transformer called comet for conceptnet and atomic @xcite , and fine-tuned gpt-2 for openpi @xcite . benchmarking llms' tom reasoning capabilities many tom benchmarks are inspired by the false beliefs test @xcite , including event-based benchmarks such as tomi @xcite , hitom @xcite , bigtom @xcite , and opentom @xcite , and dialogue-based datasets such as fan-tom @xcite . based on the smarties test @xcite , adv-csfb @xcite and tomchallenges @xcite assess llms' ability to reason about unexpected contents and unexpected transfers. tombench @xcite and epitome @xcite contain a suite of tom tasks that go beyond false beliefs and smarties test. mmtom-qa extends tom evaluation to multimodality @xcite and informativebench evaluates tom in multi-agent settings @xcite . ## the enigmatom framework before presenting the enigmatom framework, we define the general setup of tom reasoning tasks. tom task setup we focus on the widely studied tom task of reasoning about false beliefs @xcite , which is typically formulated as qa tasks. formally, given a context consisting of a sequence of events, @xmath0, which involves multiple characters, @xmath1, and a query regarding the belief of a particular character, q c , @xmath2, the goal is to derive the most likely belief, b c , from all potential beliefs, b c : further, e can be concise events as seen in the tomi dataset @xcite or utterances as seen in the fantom dataset @xcite . beyond directly querying a character's beliefs about the environment, one can also probe their beliefs regarding other characters' perceptions, thereby enabling the assessment of higher-order tom reasoning. the enigmatom framework figure 2 provides an overview of our framework, we use circled number ( n ⃝) to refer to components in the figure. at the core of enigmatom is a neural knowledge base (nkb) of entity states (enigma). given a sequence of events ( 1 ## experiments enigmatom is evaluated on three widely used tom benchmarks ( cot @xcite boosts llms' reasoning capabilities by prompting llms to explicitly list out their reasoning process. simtom @xcite conducts perspectivetaking by directly querying the llms about the mental states of characters. timetom † @xcite leverage the temporal information of events to conduct perspectivetaking. the final answer is obtained using a multiperspective belief-solving prompt. dwm @xcite conducts perspectivetaking by partitioning the events into chunks and querying the llms about characters' mental states after each chunk. percepttom † @xcite conducts perspective-taking by querying the llms about the characters' awareness of the events. to ensure a fair comparison with established methods, we conduct controlled experiments by controlling the format and answer space of all tom questions. in addition, we follow a realistic setting of tom reasoning by using only the sequence of events and tom questions from each dataset. ## conclusion in this work, we introduced enigmatom, a neurosymbolic framework designed to enhance the tom reasoning capabilities of llms. by leveraging a neural knowledge base of entity states through an iterative masking mechanism and knowledge injection, enigmatom accomplishes the bulk of tom reasoning via perspective-taking through symbolic reasoning, which alleviates llms' reasoning burden. experimental results across multiple benchmarks demonstrate that enigmatom outperforms existing methods, particularly excelling in high-order tom reasoning scenarios. our analysis highlights the effectiveness of the iterative masking mechanism in maintaining strong performance across varying depths of tom reasoning, as well as the critical role of fine-grained entity state knowledge in compressing key information in complex event sequences (as in fantom). furthermore, the framework's efficiency and scalability make it a promising solution for addressing the computational challenges associated with high-order tom reasoning tasks.