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DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs

Ye Tian, Xiaoyuan Ren, Zihao Wang, Onat Gungor, Xiaofan Yu, Tajana Rosing

22nd IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS) (published), 2025 , pp. 372–380

Published Paper (DOI) · Project Website · Paper PDF

DailyLLM is a lightweight LLM system for context-aware activity log generation and summarization using multimodal smartphone and smartwatch sensors. It uses multi-task LoRA fine-tuning to balance model capability and system efficiency. The paper reports four minutes to summarize a two-hour activity window on Raspberry Pi 5.

Recommended citation: Tian, Y., Ren, X., Wang, Z., Gungor, O., Yu, X., & Rosing, T. (2025). DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs. IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS), pp. 372–380. https://doi.org/10.1109/MASS66014.2025.00060 https://arxiv.org/abs/2507.13737

LifeAgentBench: Benchmarking LLMs for Long-Horizon, Cross-Dimensional Lifestyle Health Reasoning

Ye Tian*, Zihao Wang*, Onat Gungor, Xiaoran Fan, Tajana Rosing

Conference on Empirical Methods in Natural Language Processing (EMNLP), main conference (accepted), 2026

Project Website · Project Code · Paper PDF

*Co-first authors.

LifeAgentBench contains 22,573 questions spanning single-domain retrieval to long-horizon, cross-dimensional reasoning over structured lifestyle health records. The latest benchmark study evaluates 13 LLMs. LifeAgent is a training-free, tool-augmented baseline using question decomposition, iterative retrieval, and deterministic computation.

Recommended citation: Tian, Y., Wang, Z., Gungor, O., Fan, X., & Rosing, T. (2026). LifeAgentBench: Benchmarking LLMs for Long-Horizon, Cross-Dimensional Lifestyle Health Reasoning. Conference on Empirical Methods in Natural Language Processing (EMNLP), main conference. Accepted. Tian and Wang contributed equally. https://arxiv.org/abs/2601.13880

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