A multi-task scheme with synthetic anomalies from graph perturbations and two-phase training learns robust features for weakly supervised graph anomaly detection, outperforming competitors on public datasets.
Weakly supervised anomaly detection: A survey
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Hide-and-Seek uses contrastive objectives on trajectories to localize failure signals in VLA models from trajectory-level supervision alone.
GRADE models any LLM agent run as a graph with execution and graded dependency edge layers to enable failure prediction and fault localization across tool, coding, and web agent corpora.
citing papers explorer
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Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
A multi-task scheme with synthetic anomalies from graph perturbations and two-phase training learns robust features for weakly supervised graph anomaly detection, outperforming competitors on public datasets.
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Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring
Hide-and-Seek uses contrastive objectives on trajectories to localize failure signals in VLA models from trajectory-level supervision alone.
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GRADE: Graph Representation of LLM Agent Dependency and Execution
GRADE models any LLM agent run as a graph with execution and graded dependency edge layers to enable failure prediction and fault localization across tool, coding, and web agent corpora.