SP e C trum: A Grounded Framework for Multidimensional Identity Representation in LLM -Based Agent (2025)

Abstract: Existing methods for simulating individual identities often oversimplify human complexity, which may lead to incomplete or flattened representations. To address this, we introduce SPeCtrum, a grounded framework for constructing authentic LLM agent personas by incorporating an individual’s multidimensional self-concept. SPeCtrum integrates three core components: Social Identity (S), Personal Identity (P), and Personal Life Context (C), each contributing distinct yet interconnected aspects of identity. To evaluate SPeCtrum’s effectiveness in identity representation, we conducted automated and human evaluations. Automated evaluations using popular drama characters showed that Personal Life Context (C)—derived from short essays on preferences and daily routines—modeled characters’ identities more effectively than Social Identity (S) and Personal Identity (P) alone and performed comparably to the full SPC combination. In contrast, human evaluations involving real-world individuals found that the full SPC combination provided a more comprehensive self-concept representation than C alone. Our findings suggest that while C alone may suffice for basic identity simulation, integrating S, P, and C enhances the authenticity and accuracy of real-world identity representation. Overall, SPeCtrum offers a structured approach for simulating individuals in LLM agents, enabling more personalized human-AI interactions and improving the realism of simulation-based behavioral studies.

## introduction every man is more than just himself; he also represents the unique [..] point at which the world's phenomena intersect. to address this limitation, we introduce the spectrum framework (social identity, personal identity, and personal life context), a grounded approach for developing llm agents that effectively reflect the multidimensional nature of realworld individuals (see to validate the spectrum framework in representing one's self-concepts, we conducted both automated and human evaluations across different combinations of s, p, and c. for the automated evaluation, we created a dataset of popular drama characters and used the "guess who test" for character identification, along with the "twenty statement test" (tst) using four llms: gpt-4o, gpt-3.5 turbo, claude-3.5-sonnet, and claude-3-sonnet. in the human evaluations, 80 participants compared four types of agents-built on s, p, c, and spc-based on how well each reflected their own self-perceptions. the automated evaluation showed that personal life context (c) was the most effective in capturing character identity, outperforming s and p, and performing comparably to the spc combination. a follow-up experiment further tested whether c alone could infer s and p aspects. results indicated that c content alone could reasonably infer demographic information (s) and personality traits and values (p) of drama characters. building on these findings, we conducted a human evaluation to assess whether this pattern held for real-world individuals who would be less prominently represented in llms' training data. the results indicated that while c continued to outperform s and p, the spc combination provided a more comprehensive representation of self-concept for real-world individuals than c alone. inference tests that derived s and p from c showed lower overall accuracy compared to inferences made from drama characters. such divergence between automated and human evaluations highlights the complexity of representing human identity, emphasizing the need for the full combination of s, p, and c as a comprehensive approach to capture self-concepts in real-world individuals accurately. taken together, our evaluations validate the spectrum framework as an effective foundation for representing multidimensional self-concepts and highlight its potential to enhance human-ai interactions and social simulations through more personalized and authentic identity representation. our primary contributions are as follows: ## related work recent advancements in llms have significantly expanded their application across diverse academic fields such as social science @xcite , behavioral economics @xcite , and human-computer interaction @xcite , primarily through the creation and deployment of diverse agent personas. these llm-created personas aim to simulate complex human and social behaviors @xcite , enabling the development of increasingly personalized applications such as recommendation systems @xcite . existing frameworks for persona creation primarily emphasize isolated human traits, focusing mainly on socio-demographic characteristics @xcite @xcite for modeling specific human subpopulations @xcite . although researchers have expanded these frameworks to incorporate other dimensions such as personality traits @xcite @xcite and value systems @xcite @xcite , their approaches often yield biased or incomplete representations, as evidenced by homogeneous depictions of socially underrepresented groups @xcite @xcite @xcite . research in social psychology demonstrates that an individual's self-concept emerges from the dynamic interplay of multiple identity dimensions, including personal traits, social interactions, and lived experiences @xcite . this fundamental understanding highlights the critical importance of incorporating such multidimensional aspects in developing llm agents that authentically reflect realworld individuals and their behavioral and thought patterns @xcite . to address these limitations, we introduce the spectrum framework (see figure 1 ), which enables structured and authentic persona representations through (a) identifying essential elements of multidimensional self-concept based on social science theories and research methodologies and (b) developing systematic pipelines for integrating diverse identity sources. moving beyond the dominant focus on isolated traits, our framework emphasizes the dynamic interactions between identity components to create llm-based agents that capture the rich complexity of real-world individuals. ## spectrum: framework for multidimensional identity representation in llm-based agents the spectrum framework is grounded in the concept of self-concept, which is an individual's set of beliefs and perceptions about themselves. this encompasses their beliefs, values, abilities, and attributes @xcite @xcite . in particular, researchers have posited that self-concept primarily comprises two core components: social identity and personal identity @xcite . ## automated evaluation with fictional characters to examine the viability of the framework in capturing and representing an individual's self-concept, we first conducted an automated evaluation using popular u.s. drama characters. specifically, we aimed to assess how well each component (s, p, and c), both individually and in combination, represents key aspects of an individual's identity. in doing so, we performed a comprehensive analysis to evaluate the contribution of each component by testing all possible combinations, resulting in seven distinct conditions (s, p, c, sp, sc, pc, and spc). the composition of the character profiles varied according to these conditions. this systematic approach allowed us to investigate the individual and combined effects of each element on the overall performance of the framework. building on this, we conducted experiments using fictional characters from popular u.s. tv dramas, assuming that these characters provided s, p, and c information as specified by our framework. specifically, we used the "guess who test" and the "twenty statements test" (tst), as outlined below. ## human evaluation with real-world individuals to assess whether the findings from the automated evaluation would be applied to real-world individuals, we conducted a human evaluation via prolific foot_1 with 80 u.s. participants (aged 18+), compensating each with $6 usd. participants accessed a dedicated research website and first completed a survey to provide their s, p, and c components. specifically, for c, participants were asked to write two short essays of approximately 450 characters each-one describing their typical weekday routines and the other their typical weekend routines-totaling around 900 characters. this length was chosen to align with the 732-1856 character range observed in the c data during the automated evaluation, ensuring consistency while accommodating variability in participant responses. next, following the knowledge injection process outlined in 3.1, the inputs for s and c were used as provided, while p data was processed to generate both an expert-level and an everyday-language overview of participants' personality and value systems. this data was then used to create four variations of agent personas (s, p, c, spc) using gpt-4o, the sota model at the time. each participant agent (s, p, c, spc) was tasked with writing short essays on four topics: selfintroduction, a vision of their life, strategies for managing stress, and how they define happiness (see c.1 and c.2). participants then evaluated four essays on each topic, each generated by a different agent variant, rating the perceived similarity (overlap) between the essay content and their selfconcepts on a scale from 0 to 100% while blinded to the agent condition (see c.3). they were also asked to provide brief, open-ended feedback on the essay they found most aligned and least aligned with their self-perception. ## conclusion in this paper, we introduced the spectrum framework, a grounded approach for generating authentic, multidimensional personas using llms. this framework integrates social identity (s), personal identity (p), and personal life context (c), drawing upon the concept of self-concept. we validated the spectrum framework using both automated and human evaluations. the automated evaluation demonstrated that c alone performed comparably to spc in characterizing the identities of popular drama characters. however, human evaluation involving real-world individuals revealed that the spc combination was superior to c alone in modeling real-world individuals. reverse inference from c to s and p elements further highlighted the limitations of relying solely on c, particularly when applied to real-world individuals. overall, these results suggest that incorporating personal life context (c)-encompassing daily routines and preferences-is essential for modeling individuals, serving as a rich foundation for identity representation. however, due to the complexity of human identity and the limitations of llm training data, a more accurate and authentic simulation of real-world individuals requires the broader integration of all identity components as in the spectrum framework. in conclusion, the spectrum framework presents a promising approach for generating authentic, multidimensional personas in llms by integrating comprehensive identity components. however, incorporating multi-sourced data beyond selfreported inputs could further enhance its effectiveness. we hope researchers and developers could build upon this framework as a foundation for creating llm-based personas for both academic and practical applications across various domains.