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PersonaX: Multimodal Datasets with LLM-Inferred Behavior Traits

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Multimodal DatasetsLLM-Inferred Behavior TraitsCausality
Abstract:

Understanding human behavior traits is central to applications in human-computer interaction, computational social science, and personalized AI systems. Such understanding often requires integrating multiple modalities to capture nuanced patterns and relationships. However, existing resources rarely provide datasets that combine behavioral descriptors with complementary modalities such as facial attributes and biographical information. To address this gap, we present PersonaX, a curated collection of multimodal datasets designed to enable comprehensive analysis of public traits across modalities. PersonaX consists of (1) CelebPersona, featuring 9444 public figures from diverse occupations, and (2) AthlePersona, covering 4181 professional athletes across 7 major sports leagues. Each dataset includes behavioral trait assessments inferred by three high-performing large language models, alongside facial imagery and structured biographical features. We analyze PersonaX at two complementary levels. First, we abstract high-level trait scores from text descriptions and apply five statistical independence tests to examine their relationships with other modalities. Second, we introduce a novel causal representation learning (CRL) framework tailored to multimodal and multi-measurement data, providing theoretical identifiability guarantees. Experiments on both synthetic and real-world data demonstrate the effectiveness of our approach. By unifying structured and unstructured analysis, PersonaX establishes a foundation for studying LLM-inferred behavioral traits in conjunction with visual and biographical attributes, advancing multimodal trait analysis and causal reasoning.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces PersonaX, a multimodal dataset combining LLM-inferred behavioral traits with facial imagery and biographical features for public figures and athletes. It resides in the 'Personality and Social Trait Assessment' leaf, which contains seven papers focused on extracting personality dimensions from multimodal signals. This leaf sits within the broader 'Trait Inference and Profiling from Multimodal Data' branch, indicating a moderately populated research direction. The taxonomy shows six sibling papers in the same leaf, suggesting active but not overcrowded exploration of personality assessment via LLMs and multimodal inputs.

The taxonomy reveals neighboring work in 'Demographic and Attribute Profiling' (four papers inferring age, gender, or social roles) and 'Affective and Behavioral State Recognition' (covering transient emotions and engagement). PersonaX diverges from demographic-only inference by targeting stable behavioral traits rather than demographic attributes alone. The scope note for its leaf explicitly excludes transient state recognition, positioning the work at the intersection of stable trait modeling and multimodal integration. Nearby branches address agent behavior generation and cross-modal reasoning, but PersonaX focuses on passive trait inference rather than interactive systems or sensory alignment.

Among thirty candidates examined, the dataset contribution (Contribution A) shows no clear refutation across ten candidates, suggesting relative novelty in combining LLM-inferred traits with facial and biographical data at this scale. The two-level analysis framework (Contribution B) encountered one refutable candidate among ten examined, indicating some methodological overlap in structured-unstructured integration. The identifiability theory (Contribution C) found two refutable candidates among ten, pointing to more substantial prior work in causal representation learning for multimodal settings. The limited search scope means these findings reflect top-thirty semantic matches, not exhaustive coverage of the field.

Based on the examined candidates, the dataset appears more distinctive than the theoretical contributions, though the search scale constrains definitive claims. The taxonomy context suggests the work occupies a moderately active niche within trait inference, with clear boundaries separating it from demographic profiling and state recognition. Acknowledging the top-thirty search scope, the analysis captures immediate neighbors but may miss relevant work in adjacent subfields or emerging methodologies.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: Multimodal analysis of LLM-inferred human behavior traits. The field encompasses diverse approaches to understanding and modeling human characteristics through the integration of language models with multimodal signals. The taxonomy reveals five major branches: Trait Inference and Profiling from Multimodal Data focuses on extracting stable personality and social attributes from combined text, audio, and visual cues; Affective and Behavioral State Recognition targets dynamic emotional and mental states; Interactive Agent Behavior Generation and Adaptation emphasizes creating responsive systems that exhibit human-like traits; Cross-Modal Representation and Reasoning addresses the alignment and translation of information across sensory modalities; and Multimodal Fusion Architectures and Methodologies develops technical frameworks for combining heterogeneous data streams. Representative works such as DriveMLM[1] and MotionLLM[7] illustrate how domain-specific applications leverage these branches, while studies like LaMI[5] and Multimodal Emotion Fusion[6] demonstrate cross-branch integration of fusion techniques with affective recognition. Within Trait Inference and Profiling, a particularly active line of work explores personality and social trait assessment from rich multimodal inputs. PersonaX[0] situates itself in this cluster by proposing methods to infer nuanced behavioral traits through LLM-guided analysis of combined modalities, closely aligning with efforts like Agentic Trait Recognition[27] and Hierarchical Personality Assessment[28] that similarly emphasize structured trait extraction. Compared to Chain-of-Thought Demographics[3], which leverages reasoning chains for demographic inference, PersonaX[0] appears to prioritize broader personality dimensions over purely demographic attributes. Meanwhile, works such as Traits Run Deep[18] and Trimodal Persona[46] explore the stability and consistency of inferred traits across contexts, raising open questions about how temporal dynamics and cross-situational variability should be modeled. The interplay between static trait profiling and dynamic state recognition remains a central tension, with ongoing debates about whether LLMs can reliably capture the subtleties of human individuality or risk oversimplifying complex behavioral patterns.

Claimed Contributions

PersonaX multimodal datasets with LLM-inferred behavior traits

The authors introduce PersonaX, consisting of CelebPersona (9444 public figures) and AthlePersona (4181 professional athletes), each integrating LLM-inferred behavioral trait assessments, facial imagery, and structured biographical features to enable cross-modal trait analysis.

10 retrieved papers
Two-level analysis framework combining structured and unstructured methods

The authors propose a dual-level analysis approach: at the structured level, they apply statistical independence tests to examine trait-modality relationships; at the unstructured level, they develop a causal representation learning framework specifically designed for multimodal, multi-measurement data.

10 retrieved papers
Can Refute
Identifiability theory for multimodal multi-measurement causal representation learning

The authors establish theoretical identifiability guarantees for their causal representation learning framework, extending prior work to handle the unique setting of multimodal observations with multiple measurements per modality, supported by formal theorems on subspace and component-wise identifiability.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

PersonaX multimodal datasets with LLM-inferred behavior traits

The authors introduce PersonaX, consisting of CelebPersona (9444 public figures) and AthlePersona (4181 professional athletes), each integrating LLM-inferred behavioral trait assessments, facial imagery, and structured biographical features to enable cross-modal trait analysis.

Contribution

Two-level analysis framework combining structured and unstructured methods

The authors propose a dual-level analysis approach: at the structured level, they apply statistical independence tests to examine trait-modality relationships; at the unstructured level, they develop a causal representation learning framework specifically designed for multimodal, multi-measurement data.

Contribution

Identifiability theory for multimodal multi-measurement causal representation learning

The authors establish theoretical identifiability guarantees for their causal representation learning framework, extending prior work to handle the unique setting of multimodal observations with multiple measurements per modality, supported by formal theorems on subspace and component-wise identifiability.

Table of Contents

PersonaX: Multimodal Datasets with LLM-Inferred Behavior Traits | Novelty Validation