Human Behavior Modeling

Latest papers

44 papers16 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Early Prediction of Future Behavioral Strategy from Process Traces
May 28, 2026Robert Kasumba, Dennis Barbour, Chien-Ju HoLatent Variable Models Human Behavior Modeling
Division of Computational and Data Sciences Washington University in Saint Louis Saint Louis, MO 63130 · Department of Biomedical Engineering Washington University in Saint Louis Saint Louis, MO 63130 · Department of Computer Science Washington University in Saint Louis Saint Louis, MO 63130
BEHAVE: Real-Time Modeling of Human Systems as Observable Complex Dynamical Systems and Operational Objects for Physical AI
May 12, 2026Helene MalyutinaDynamical Systems Pedestrian Behavior Modeling
Independent Researcher, Collective Dynamics Lab
How Well Do Large Language Models Capture Human Personality?
May 12, 2026Aanisha Bhattacharyya, Yaman Kumar Singla, Rajiv Ratn Shah +2Persona-Conditioned Generation Representation Collapse
Adobe Media and Data Science Research (MDSR) · IIIT-Delhi · SUNY at Buffalo
Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners
May 8, 2026Botos Csaba, Sreejan Kumar, Austin Tudor David Andrews +6Game-Playing Agents Human Behavior Simulation
University of Oxford · Columbia University · New York University +2
Post-training makes large language models less human-like
May 8, 2026Marcel Binz, Elif Akata, Abdullah Almaatouq +76LLM Evaluation LLM Alignment
Think-Aloud Reshapes Automated Cognitive Model Discovery Beyond Behavior
May 6, 2026Hanbo Xie, Akshay K. Jagadish, Lan Pan +1Risky Decision-Making Computational Cognitive Modeling
School of Psychology, Georgia Institute of Technology · Princeton AI Lab, Princeton University · Center of Excellence for Computational Cognition, Georgia Institute of Techonology
Hierarchical Multi-Persona Induction from User Behavioral Logs: Learning Evidence-Grounded and Truthful Personas
Apr 28, 2026Nayoung Choi, Haeyu Jeong, Changbong Kim +2Personality Modeling in Language Models User Modeling
Department of Computer Science, Emory University, Atlanta, GA, USA · Naver Corporation, South Korea
LLMs Reading the Rhythms of Daily Life: Aligned Understanding for Behavior Prediction and Generation
Apr 26, 2026Fanjin Meng, Jingtao Ding, Nian Li +2LLM Alignment Human Behavior Modeling
Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China
Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teaming
Apr 21, 2026Alex Cuellar, Michael Hagenow, Julie ShahHuman Behavior Modeling Human-Robot Collaboration
MIT CSAIL · UW Madison Department of Computer Science
Improving Human Performance with Value-Aware Interventions: A Case Study in Chess
Apr 15, 2026Saumik Narayanan, Raja Panjwani, Siddhartha Sen +1Human Behavior Modeling AI-Assisted Decision Making
Washington University in St. Louis · Microsoft Research · Georgetown University
Context-Value-Action Architecture for Value-Driven Large Language Model Agents
Apr 7, 2026TianZe Zhang, Sirui Sun, Yuhang Xie +3Human Behavior Modeling LLM Agents
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University · Yuanpei College, Peking University · School of Psychological and Cognitive Sciences, Peking University +2
Alignment Makes Language Models Normative, Not Descriptive
Mar 17, 2026Eilam Shapira, Moshe Tennenholtz, Roi ReichartLLM Alignment Preference Alignment
Technion – Israel Institute of Technology
HugAgent: A Human Simulation Benchmark for Individual-Level Reasoning
Oct 16, 2025Chance Jiajie Li, Zhenze Mo, Yuhan Tang +11Cognitive Modeling Human Behavior Simulation
1MIT Media Lab · 8Northeastern University · 5MIT CEE +7
Exploring the Limits of Large Language Models as a Reflection of Human Cognition: An Illustration in the Context of Moral Judgement
Aug 9, 2025Sarah Schröder, Thekla Morgenroth, Ulrike Kuhl +2LLM Reliability Large Language Model-Based Social Simulation
Faculty of Technology, Bielefeld University · Department of Psychological Sciences, Purdue University

May 28, 2026J/K move · Enter open · S save

Early Prediction of Future Behavioral Strategy from Process Traces

Robert Kasumba, Dennis Barbour, Chien-Ju Ho

Division of Computational and Data Sciences Washington University in Saint Louis Saint Louis, MO 63130 · Department of Biomedical Engineering Washington University in Saint Louis Saint Louis, MO 63130 · Department of Computer Science Washington University in Saint Louis Saint Louis, MO 63130

Latent Variable Models Human Behavior Modeling Human Behavior Prediction

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Adaptive systems often need to make task-specific decisions about people from limited evidence: a tutor may need to anticipate how a learner will approach a new problem, a game may need to adapt when a player enters a new level, and a human-AI system may need to infer whether a partner will persist with a plan or switch goals. These decisions depend on person-level tendencies that shape how people solve related tasks, but such tendencies are difficult to infer from standard behavioral evidence. One approach is to use aggregate outcome summaries, such as scores, completion rates, or productivity; these summaries are compact and available across tasks, but can collapse distinct behavioral processes into similar outcomes. Another approach is to use process-level traces, which record how behavior unfolds; however, process modeling within one task can entangle stable person-level tendencies with task-specific layout and affordances. In this work, we study early cross-task behavioral inference: whether partial source-task process traces can reveal transferable person-level structure that predicts strategy in a held-out target task. We introduce a Process-Level Latent Variable Model (PLVM), which encodes task-specific traces and fuses them into a shared person-level latent representation for cross-task prediction. In PowerWash Simulator, a naturalistic telemetry dataset of human gameplay, PLVM uses partial traces from two cleaning tasks to predict locally persistent Zone Planner behavior versus frequent Zone Hopper behavior in the held-out Fire Station level. Controlled simulations with known latent types show that cross-task fusion helps when source tasks reveal complementary dimensions of a shared latent process. These results suggest that process-level cross-task modeling can support early prediction of target-task strategy when observing sufficient target-task behavior is impractical.