Tag
ClockRoPE introduces random Fourier rotations to model temporal periodicity in sequential recommendation, theoretically grounded and validated via online A/B tests at a major video-sharing platform.
This paper presents an LLM-based pipeline for analyzing mental health changes from sequentially ordered social media posts, participating in the CLPsych 2026 shared task. It performs post-level assessment and user-level temporal modeling to capture shifts in psychological well-being.
The paper presents World Value Model (WVM), a generalist robotic value model that combines world models with value estimation to accurately assess task progression and improve robotic policy learning from mixed-quality data, achieving state-of-the-art results on standard benchmarks and a new suboptimal data benchmark.
This paper evaluates deep learning models (LSTM, TCN, Transformer) on the WESAD dataset for multimodal emotion recognition from physiological signals, showing that an ensemble achieves 98.91% accuracy.
Introduces Random Attention (RA), a lightweight temporal modeling module for mobile sleep staging that uses fixed random projections for similarity-based aggregation, achieving competitive performance with minimal additional parameters.
This paper proposes a rolling-window framework for customer churn prediction in non-contractual service environments, using 30-day behavioral windows to enable continuous risk assessment. Evaluated on real-world data, the feature-based model achieves 87.6% accuracy and 0.94 ROC-AUC, while the sequence-based model reaches 96.1% recall.
TBD-VLA introduces a discrete vision-language-action framework that combines block diffusion with autoregressive generation to achieve efficient temporal action modeling and faster inference, significantly outperforming prior VLA approaches in simulation and real-world manipulation tasks.
EvoMD-LLM reformulates reactive molecular dynamics trajectories as symbolic temporal sequences, enabling LLMs to model species evolution over time through fine-tuning and temporal scaffolding, achieving up to 66.14% accuracy and interpretable predictions.
This paper presents DreamerNLplus, a hybrid framework combining LLMs, DeBERTa, Random Forest, rule-based methods, and RAG to model mental health dynamics from social media timelines for the CLPsych 2026 shared task, achieving top rankings in subtasks for temporal summarization and change detection.
EEG-tGAT is a temporally augmented Graph Attention Network that improves affordance classification from interaction sequences by incorporating temporal attention and dropout mechanisms. The model enhances GATv2 for sequential data where temporal dimensions are semantically non-uniform.