Tag
The article introduces SAGE, a multi-agent LLM framework for time-series anomaly detection that uses specialized analyzers to improve interpretability and reliability. It demonstrates superior performance over baselines on three benchmarks and enhances diagnostic reporting through structured evidence consolidation.
This paper introduces MultiLinguahah, an unsupervised multilingual method for acoustic laughter segmentation using Isolation Forests on BYOL-A encoder representations. The authors demonstrate that their approach outperforms state-of-the-art supervised methods in non-English settings by treating laughter detection as an anomaly detection task.
This paper presents PCNet, a probabilistic circuit trained as a tractable density estimator on LLM residual streams to detect hallucinations as geometric anomalies. It also introduces PC-LDCD, a dynamic correction method that only intervenes on hallucinated tokens, achieving near-perfect detection and reduced corruption rates.
guardd is an open-source Linux endpoint detection tool that uses eBPF events and Isolation Forest to spot anomalous process/network behavior in 60-second windows, but struggles with browser-related false positives.
This paper introduces JuRe (Just Repair), a minimal denoising network for time series anomaly detection that matches or exceeds complex neural baselines on the TSB-AD and UCR benchmarks, demonstrating that a proper manifold-projection training objective is more important than architectural complexity.
ArtifactNet is a lightweight neural network framework that detects AI-generated music by analyzing codec-specific artifacts in audio signals, achieving F1=0.9829 on a new 6,183-track benchmark (ArtifactBench) with 49x fewer parameters than competing methods. The approach uses forensic physics principles to extract codec residuals through a bounded-mask UNet and compact CNN, with codec-aware training reducing cross-codec drift by 83%.