The paper introduces the concept of adaptive personalization under preference epistemic uncertainty, arguing that conversational AI must adjust its response strategy as human preferences evolve. A study shows people prefer state‑contingent strategies when uncertainty changes, and the authors build a multilingual benchmark to test language models on this task. Results reveal a recognition‑generation gap: models can choose the right strategy from options but fail to generate it correctly, defaulting to exploit‑first behavior; targeted training on internal strategy representations improves performance on out‑of‑distribution personas.
By Priyanka Dey, Rajarshi Das, Vikas Bahirwani, Qi Zeng, Julia Kruk, Qi Qi
arXiv:2609.00251v1 Announce Type: new
Abstract: As people increasingly interact with LLM assistants in daily life, continually adapting to individual preferences has become essential for effective lo...
By EunJeong Hwang, Kushan Mitra, Dan Zhang, Hannah Kim, Estevam Hruschka
arXiv:2606. 18636v1 Announce Type: cross Abstract: Recent advancements in Large Language Models (LLMs) have empowered home assistants with natural language interaction capabilities.
By Yingyu Shan, Zeming Liu, Silin Li, Boao Qian, Jiashu Yao, Yuhang Guo, Haifeng Wang
arXiv:2606. 04798v1 Announce Type: new Abstract: Sensor-based Human Activity Recognition (HAR) models often degrade on unseen users due to domain shifts caused by individual movement patterns and sensor placement.
By Maximilian Burzer, Till Riedel, Michael Beigl, Tobias R\"oddiger
arXiv:2607. 14240v1 Announce Type: new Abstract: Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature of real-world human-AI interactions.
By Valerie Chen, Cleotilde Gonzalez, Anita Williams Woolley, Michael Lee, Tongshuang Wu, Vincent Conitzer, Aarti Singh
Recent advancements in Large Language Models (LLMs) have empowered home assistants with natural language interaction capabilities. However, current assistants overlook the progressive omission that occurs in human dialogue as shared context accumulates, leading to more elliptical expressions for efficient communication.
arXiv:2607. 18034v1 Announce Type: new Abstract: Smart home assistants interpret a wide range of user commands, from explicit device control to underspecified and preference dependent requests.
By Eu Jin Lim, Zhaoxing Li, Sebastian Stein
The paper introduces active client selection strategies for federated learning in autonomous vehicle trajectory prediction, addressing challenges of high scene uncertainty and heterogeneous complexity across different driving environments. It proposes uncertainty-aware selectors that use per-client negative log-likelihood and aleatoric uncertainty, as well as a joint selector that balances scene complexity and uncertainty to prioritize informative clients. Experiments on the Argoverse dataset show that federated models outperform local training, with uncertainty-aware selection speeding convergence and improving key metrics, while the joint selector yields the best generalization under strong heterogeneity.
By Yiming Xie, Muzi Peng, Fei Miao, Ningfang Mi, Lili Su
NextMe-800 is an approximately 800‑hour first‑person video dataset collected from a single volunteer over 126 days, featuring 1 Hz images, gaze, and audio. The data are captioned at five hierarchical abstraction levels—from atomic actions to major activities—enabling personalized action anticipation as an open‑vocabulary K‑step sequence prediction task. The authors also introduce NextAct, a 1,500‑point benchmark that combines NextMe‑800 with the multi‑person EgoLife dataset, and evaluate models using an embedding‑based soft edit distance to assess how well personal behavior can be anticipated across abstraction levels and prediction horizons.
By Zhaoxu Meng, Yiming Sun, Mingyuan Gao, Jiachang Zhang, Zhuhan Dai, Yipeng Du, Zheng Lian, Jian-Qiao Zhu
The paper introduces a new framework for multi-resident data association in smart homes, framing the problem as a Markov Decision Process and solving it with Behavior Cloning. Two policy architectures are proposed: a lightweight Transformer treating association as multi-label classification over event histories, and a fine-tuned Large Language Model that generates conditional text to leverage semantic knowledge. Experiments on three datasets show the approach outperforms existing baselines, especially for concurrent multi-resident events, and the LLM policy hints at cross-environment transfer potential.
By Xi Chen (M-PSI), Julien Cumin (LIG, M-PSI), Fano Ramparany (LIG, M-PSI), Dominique Vaufreydaz (LIG, M-PSI)
arXiv:2606. 09958v1 Announce Type: cross Abstract: In mixed-traffic environments where autonomous and human-driven vehicles may co-exist, motion planning for autonomous vehicles requires anticipating the future behaviors of surrounding human drivers.
By Ming Cheng, Hao Chen, Ziyi Yang, Ziluowen Luo, Senzhang Wang
arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.
By Jinseok Chung, Minkyoung Song, Hyunji Jung, Namhoon Lee