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This paper proposes a multimodal anomaly detection framework for fault detection in mechanical systems using self-supervised cross-modal reconstruction and adaptive thresholding to improve robustness under distribution shifts.
Introduces SearchOS, a multi-agent framework for robust open-domain information-seeking that externalizes search progress into explicit states via a novel Search-Oriented Context Management (SOCM) system, achieving state-of-the-art results on WideSearch and GISA benchmarks.
Robust-TO addresses the Blind Trust Problem in video reasoning by integrating per-frame trustworthiness into an agentic framework, improving accuracy under realistic perturbations through calibrated evidence weighting and reliability-aware reasoning.
Proposes Cascade-KDE, a training-free framework that uses two-dimensional kernel density estimation and truncated expectation to restore time-series corrupted by out-of-distribution impulse outliers while preserving local structure and derivative features.