Context-Sensitive Daily Routines

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Knowing Is Not Enough: Information Retrievability as a Precondition to Effective LLM Oversight
Sep 2, 2026Xinyu Fu, Narayan Ramasubbu, Dennis GallettaLarge Language Models Fail Explainable AI Methods
Robinson College of Business Georgia State University 55 Park Place NE, Atlanta, GA 30303, USA · School of Business University of Pittsburgh 3950 Roberto and Vera Clemente Dr, Pittsburgh, PA 15260, USA
VibeLifeBench: Can Your Life Agent Be Proactive and Persistent in a Living World?
Aug 11, 2026Xiaohongshu IncArtificial Intelligence Assistants Life
Xiaohongshu Dots Studio · Evolvent AI
D2VBench: Benchmarking Large Language Models with Value Dilemmas in Daily Scenarios
Jul 22, 2026Siyi Hao, Yidi Cao, Linhao Yu +2Context-Sensitive Daily Routines Real-World
TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China · The International Joint Institute of Tianjin University, Fuzhou, China
Classifying daily activities needs posture, reconstructing them needs motion
Jul 14, 2026Arefeh Farahmandi, Gunnar BlohmLatent Motion Open-Vocabulary Action Recognition
Centre for Neuroscience Studies Queen’s University Kingson, Ontario, Canada
LightMem-Ego: Your AI Memory for Everyday Life
Jul 13, 2026Yijun Chen, Boyi Xiao, Yixian Zhao +10Multimodal Memory Conversational Memory
1Zhejiang University · 2South China University of Technology · 3Central China Normal University +1
WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs
Jul 12, 2026Mohannad Takrouri, Nicolas M. Cuadrado A., Martin TakáčSmart Homes Context-Sensitive Daily Routines
Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, United Arab Emirates
Generating Realistic Individual Activity Schedules via Activity Location Allocation Based on Simulated Travel Times
Jun 23, 2026Tatsuya Mitomi, Yahya Gamal, Esra Suel +3Mobility Journey
AI for Collective Intelligence Hub, University of Glasgow, Glasgow, UK · Fujitsu Limited, Kawasaki, Japan · Urban Analytics, Department of Geography, University of Zurich, Zurich, Switzerland +1
Music Playlist Captioning at Scale with Large Language Models
Jun 21, 2026Mathieu Delcluze, Léa Briand, Benjamin Chapus +2Dance-To-Music Generation Large Language Model Personalization
Deezer Research · SJTU Paris Elite Institute of Technology
Better Adherence, Richer Context: A Field Evaluation of LLM-Powered Conversational Voice Diaries for Sleep
Jun 17, 2026Amama Mahmood, Bokyung Kim, Honghao Zhao +4Sleep Context-Sensitive Daily Routines
The Johns Hopkins University, USA · Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, USA
The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology
May 25, 2026Jason Holmes, Federico Mastroleo, Mariana Borras-Osorio +17Clinical Trials Dose
Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States · Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy · Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy +2
Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions
May 19, 2026Gefei Lin, Rui Miao, Jennifer Sacheck +1Physical Activities Context-Sensitive Daily Routines
Department of Statistics, The George Washington University · Department of Mathematical Sciences, The University of Texas at Dallas · Department of Behavioral and Social Sciences, Brown University
HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities
May 10, 2026Shusaku Egami, Aoi Ohta, Tomoki Tsujimura +5Knowledge Graph Embeddings Knowledge Graphs
National Institute of Advanced Industrial Science and Technology (AIST) Koto-ku, Tokyo, Japan
Don't Learn the Shape: Forecasting Periodic Time Series by Rank-1 Decomposition
May 8, 2026Takato HondaTime Series Low-Rank Structure
Mellon Inc.
SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting
Apr 30, 2026Arnaud ZinflouImputation Accurate Short-Term Electricity Load Forecasting
Hydro-Québec Research Institute Canada
Supporting Family-School Partnerships with Robot-Facilitated Home-Based Activities
Apr 27, 2026Michael F Xu, Qiyao Yang, Heather Kirkorian +1Context-Sensitive Daily Routines
University of Wisconsin–Madison Madison, Wisconsin, USA
LLMs Reading the Rhythms of Daily Life: Aligned Understanding for Behavior Prediction and Generation
Apr 26, 2026Fanjin Meng, Jingtao Ding, Nian Li +2Behavioral Foundation Models Large Language Model Alignment
Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China
Generating Individual Travel Diaries Using Large Language Models Informed by Census and Land-Use Data
Sep 7, 2025Sepehr Golrokh Amin, Devin Rhoads, Fatemeh Fakhrmoosavi +2Journey User Simulation
School of Civil and Environmental Engineering, University of Connecticut, United States

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Knowing Is Not Enough: Information Retrievability as a Precondition to Effective LLM Oversight

Xinyu Fu, Narayan Ramasubbu, Dennis Galletta

Robinson College of Business Georgia State University 55 Park Place NE, Atlanta, GA 30303, USA · School of Business University of Pittsburgh 3950 Roberto and Vera Clemente Dr, Pittsburgh, PA 15260, USA

Large Language Models Fail Explainable AI Methods Context-Sensitive Daily Routines Preconditioner

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Large language models (LLMs) are increasingly embedded in organizational work, yet their errors often pass human review. Prior research locates such failures in users' capability to review LLM output or their engagement in doing so. We develop an alternative, retrieval-based account of human oversight and posit that error detection is more effective when oversight-relevant information is accessible to users at the moment of review. Across two randomized lab-in-the-field experiments with 640 customer-facing employees, we show that self-generated explanations improve error detection and strengthen recall of verification-relevant reasoning, while cues that reactivate such reasoning help sustain detection under repeated LLM use. Theoretically, we identify information retrievability as a distinct precondition for effective oversight and specify generative encoding and cue-supported reactivation as mechanisms that build and sustain it. Practically, lightweight onboarding self-explanations and daily retrieval cues can make human oversight more resilient as LLM use becomes routine.

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