HumanLLM: Towards Personalized Understanding and Simulation of Human Nature

2026-01-22

AI Summary

Current large language models struggle to accurately simulate individual cognition and behavior, hindering the advancement of personalized social intelligence. This work proposes a novel paradigm for modeling individuals within continuous contextual trajectories, introducing a “cognitive genome” dataset comprising 5.5 million user logs. Through a multi-stage cleaning and synthesis pipeline, the framework captures individualized patterns of thought and behavior, which are then leveraged for supervised fine-tuning. This approach presents the first systematic foundation model designed explicitly for simulating human essence. It substantially outperforms baseline methods across diverse tasks—including behavioral prediction, reconstruction of internal thoughts, writing style imitation, and user profiling—and demonstrates superior generalization on out-of-domain social intelligence benchmarks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Intelligent User Interfaces
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Motivated by the remarkable progress of large language models (LLMs) in objective tasks like mathematics and coding, there is growing interest in their potential to simulate human behavior--a capability with profound implications for transforming social science research and customer-centric business insights. However, LLMs often lack a nuanced understanding of human cognition and behavior, limiting their effectiveness in social simulation and personalized applications. We posit that this limitation stems from a fundamental misalignment: standard LLM pretraining on vast, uncontextualized web data does not capture the continuous, situated context of an individual's decisions, thoughts, and behaviors over time. To bridge this gap, we introduce HumanLLM, a foundation model designed for personalized understanding and simulation of individuals. We first construct the Cognitive Genome Dataset, a large-scale corpus curated from real-world user data on platforms like Reddit, Twitter, Blogger, and Amazon. Through a rigorous, multi-stage pipeline involving data filtering, synthesis, and quality control, we automatically extract over 5.5 million user logs to distill rich profiles, behaviors, and thinking patterns. We then formulate diverse learning tasks and perform supervised fine-tuning to empower the model to predict a wide range of individualized human behaviors, thoughts, and experiences. Comprehensive evaluations demonstrate that HumanLLM achieves superior performance in predicting user actions and inner thoughts, more accurately mimics user writing styles and preferences, and generates more authentic user profiles compared to base models. Furthermore, HumanLLM shows significant gains on out-of-domain social intelligence benchmarks, indicating enhanced generalization.

Problem

• personalized simulation
• human behavior modeling
• cognitive understanding
• social intelligence
• individualized prediction

Innovation

• HumanLLM
• personalized simulation
• Cognitive Genome Dataset
• social intelligence
• supervised fine-tuning