HORIZON creates a cross-domain, long-horizon user modeling benchmark from Amazon Reviews that tests generalization across time, domains, and unseen users, exposing gaps in sequential and LLM-based recommendation models.
Cat: Beyond efficient transformer for content-aware anomaly detection in event sequences,
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
KRONE derives semantic execution hierarchies from flat logs to enable modular multi-level anomaly detection with hybrid local and nested-aware detectors plus limited LLM use, delivering 10% F1 gains and over 100x data efficiency on benchmarks and industrial data.
Krone-viz provides an interactive interface for hierarchical log decomposition, modular anomaly detection, and human-in-the-loop LLM explanation on system logs.
citing papers explorer
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HORIZON: A Benchmark for In-the-wild User Behaviour Modeling
HORIZON creates a cross-domain, long-horizon user modeling benchmark from Amazon Reviews that tests generalization across time, domains, and unseen users, exposing gaps in sequential and LLM-based recommendation models.
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KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction
KRONE derives semantic execution hierarchies from flat logs to enable modular multi-level anomaly detection with hybrid local and nested-aware detectors plus limited LLM use, delivering 10% F1 gains and over 100x data efficiency on benchmarks and industrial data.
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Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation
Krone-viz provides an interactive interface for hierarchical log decomposition, modular anomaly detection, and human-in-the-loop LLM explanation on system logs.