arXiv Machine Learning September 18, 2026 By Rongchao Xu, Dahai Yu, Lin Jiang, Guang Wang

ZeroHAT: Behavior-Conditioned Zero-Shot Human Activity Trace Generation

Read the original on arXiv Machine Learning →

ZeroHAT is a new framework for generating synthetic human activity traces (HATs) in a target region without any real data from that region. It transfers behavioral patterns learned from real HATs in source regions and adapts them using publicly available contextual information about the target region. The system includes a consistency-aware intent extractor, a cross-region behavioral cloning module, and a behavior-conditioned activity realization module, and it outperforms the strongest baseline by 4.5–6.4× in downstream utility and improves fidelity by 15.6–40.8% across ten cities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Sep 2

Measuring the Behavioral Fidelity of Long-Horizon Human Activity Simulations

arXiv:2609.01257v1 Announce Type: new Abstract: As LLM-based human simulators are increasingly used for policy, evaluation, and training, they must faithfully reproduce real behavioral patterns. Whil...

By Yi Fei Cheng, Fan Yang, Iremsu Bas, Koichiro Niinuma, Narishige Abe, David Lindlbauer

llms

arXiv AI

Aug 7

LUNAR: Benchmarking Personalized Large Language Models on UNiversal User BehAvioR Logs

arXiv:2608. 05246v1 Announce Type: new Abstract: Existing personalized LLM benchmarks primarily rely on textual personas or isolated behavioral signals, providing limited evaluation of cross-domain behavioral personalization, where responses must be grounded in heterogeneous daily-life activities.

By Jiahao Zhang, Yongzhi Tong, Zelin Fu, Pengde Zhao, Yanmei Jiang, Jiang Feng, Min Yang

llms efficiency benchmarks safety

arXiv AI

Jun 12

TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation

arXiv:2606. 12657v1 Announce Type: new Abstract: Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation.

By Siyu Li, Toan Tran, Lingyi Zhao, Khurram Shafique, Li Xiong

llms agents fine-tuning benchmarks safety

arXiv AI

Jun 10

Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints

arXiv:2606. 10314v1 Announce Type: new Abstract: Although the study of human trajectory anomalies is critical for advancing spatial data mining, empirical research remains severely hindered by a pervasive lack of ground-truth datasets.

By Yueyang Liu, Joon-Seok Kim, Andreas Z\"ufle

llms agents safety

arXiv AI

Sep 2

User Representation via Cross Multi-source Behavior Pre-training for Mobile Games

The paper introduces CM-PTM, a Cross Multi-source Behavior Pre-Training Model designed to learn mobile game user representations from device-level behavioral logs. It uses hierarchical cascaded mask‑then‑predict tasks to first identify the source of the next behavior and then refine predictions at the app‑action level, thereby modeling cross‑source dependencies and fine‑grained dynamics. Experiments on large real‑world mobile datasets show that CM‑PTM captures users’ endogenous interests and improves performance on downstream mobile game recommendation tasks.

By Chengqi Yang, Yiran Qiao, Feng Liu, Xingyu Lou, Zijun Zhou, Xiaoyun Mo, Changwang Zhang, Jiayuan Xu, Jun Wang, Xiang Ao

efficiency

arXiv Machine Learning

Jul 2

ActivityNarrated: An Open-Ended Narrative Paradigm for Wearable Human Activity Understanding

arXiv:2604. 00767v2 Announce Type: replace Abstract: Wearable human activity recognition (HAR) has made steady progress, yet much of this progress remains grounded in fixed-window, closed-set classification benchmarks.

By Lala Shakti Swarup Ray, Mengxi Liu, Alcina Pinto, Deepika Gurung, Daniel Geissler, Paul Lukowoicz, Bo Zhou

llms benchmarks safety

One email a morning, machine-written

Related stories

Measuring the Behavioral Fidelity of Long-Horizon Human Activity Simulations

LUNAR: Benchmarking Personalized Large Language Models on UNiversal User BehAvioR Logs

TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation

Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints

User Representation via Cross Multi-source Behavior Pre-training for Mobile Games

ActivityNarrated: An Open-Ended Narrative Paradigm for Wearable Human Activity Understanding