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.
arXiv:2609.38397v1 Announce Type: new Abstract: Virtual clients offer a cost-effective approach to support applications such as A/B testing, recommender system development, and interface evaluation....
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...
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.
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.
MotiveMob is a motivation-driven autoregressive framework for generating human mobility trajectories. It models the "why-where-when" decision process by first predicting a plausible motivation for each movement step and then jointly generating the next location and arrival time. The approach is evaluated under user-level and temporal distribution shifts, including seasonal changes and COVID-19 disruptions, and consistently outperforms pretraining- and prompting-based baselines in distributional fidelity.
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.
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.
The paper introduces GPlan, a generative framework for Generative Spatiotemporal Intent Sequence Recommendation (GSISR) that integrates large language model reasoning into lightweight models. It employs Progressive Implicit CoT Distillation to compress reasoning into latent tokens for low-latency inference, and Spatiotemporal Counterfactual DPO to align plans with real-world constraints, improving sequence coherence and context responsiveness. Experiments and online A/B tests confirm the effectiveness of these techniques, and the authors provide an implementation and dataset on GitHub.
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.
The paper demonstrates that fine‑tuning large language models (LLMs) on local tourist trajectory data can predict visitor movements under varying conditions. Using 566 trajectories from Wakayama Castle Park, Japan, the authors fine‑tuned Llama‑3.1‑8B, achieving 49.1% accuracy for next point‑of‑interest predictions and maintaining strong performance even on undersampled scenarios such as rainy days. This shows that LLMs can serve as high‑fidelity, context‑aware behavior models for tourist prediction and enable counterfactual analysis of mobility interventions.
arXiv:2607. 19949v1 Announce Type: new Abstract: Smartphone personal assistants reason over longitudinal personal data, yet evaluating them requires context-rich evaluation data whose correct answers are known, and real device traces are too privacy-sensitive to share.
The paper presents a comprehensive benchmark for Domain Generalization (DG) in smartphone-based Human Activity Recognition (HAR), running over 410,000 experiments across multiple architectures, training objectives, initialization strategies, and architectural tweaks. It finds that individual DG components offer limited, highly conditional improvements, while combined configurations often yield stronger, sometimes super‑additive gains that depend on the model and shift scenario. The study also highlights that current source‑validation selection captures only a fraction of the potential oracle performance, underscoring the need for joint DG design and robust model‑selection methods.