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This synthesis of 45 papers published between January 2020 and June 2026 reveals an overreliance on multi-stage, text-centric pipelines, a predominance of active, screen-bound interactions, and a severe demographic skew toward young, university-campus-based cohorts in empirical evaluations.
Reconstruction-based detectors for multivariate time series anomaly detection typically learn a single entangled representation of normal behavior and flag anomalies based on reconstruction error. However, real-world anomalies are not only isolated spikes in a single channel but also disruptions in the relationships be...
Rapid earthquake magnitude estimation is central to earthquake early warning, yet many operational systems depend on dense regional seismic networks and region-specific calibration. This creates a spatial coverage barrier for high-risk areas with sparse sensing infrastructure. Single-station learning offers a lower-cos...
Group alignment adapts a language model to a demographic group to produce responses that reflect the group's opinions, values, and preferences. Sycophancy, a well-documented by-product of alignment, causes the model to over-agree with the user regardless of factual and objective information. However, existing group ali...
TimeNet provides the data and systems foundation needed to move beyond task- and dataset-specific TFMs toward models that can learn jointly across heterogeneous domains, modalities, temporal scales, and forms of supervision from a common data model.
PAR-AdvGAN is proposed, a progressive auto-regressive GAN framework that iteratively refines adversarial perturbations, by conditioning each iteration on the previous output, which produces stronger, more transferable attacks while maintaining low visual distortion and high generation speed.
Steering large language models typically relies on linear, context-independent interventions in activation space, an assumption that recent work has challenged and that can induce an information bottleneck when a fixed representation must encode many behavioral distinctions. We introduce MetaSteer, a method that learns...
This work proposes UPLOTS, a Unified, Prompt-guided Language model framework that leverages a single pre-trained transformer backbone guided by learned constraint prompts, enabling on-demand generation with precise pattern control in time-series generation.
Retrieval-Augmented Generation (RAG) systems empower large language models (LLMs) with external knowledge, yet struggle with efficiency-accuracy trade-offs when scaling to large knowledge graphs. Existing approaches often rely on monolithic graph retrieval, incurring unnecessary latency for simple queries and fragmente...
DynaTokens, a transformer-based token generator that dynamically produces fine-tuning tokens on demand, enabling task-adaptive prompt updates through shared generation weights is introduced, enabling task-adaptive prompt updates through shared generation weights.
TopoBrick is presented, a training-free framework for zero-shot building IoT (Internet-of-Things) forecasting that outperforms strong zero-shot foundation-model baselines and remains competitive with fully trained building-specific models.
Results show that spectral evolution provides a practical and principled signal for fast time-series diffusion sampling, and a training-free spectral-aware sampler that adaptively selects the denoising stride from band-level activity.
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