ICML 2026 Papers

While recent training-free video anomaly detection (VAD) methods offer advantages such as interpretability and ease of deployment, they often suffer from computational inefficiency due to complex memory retrieval mechanisms or high-latency visual-language models (VLMs). To address this issue, we propose PRISM (Parameter-less Recognition Based on Intrinsic Statistical Modeling), a novel framework for efficient open-set anomaly detection with minimal computational cost. Built on a pre-trained multimodal embedding model, PRISM introduces differential amplification and whitening mechanisms to statistically suppress common-mode background noise in the embedding space, thereby improving the signal-to-noise ratio of anomalous events. Extensive experiments on three widely datasets demonstrate that PRISM achieves state-of-the-art performance among training-free methods while maintaining real-time inference capability. Furthermore, our statistical analysis offers a complementary perspective on why training-free methods may suffer from lower Average Precision (AP) on complex datasets such as XD-Violence.Code is released at https://github.com/ytC2026/ICML2026-PRISM.

Interpretable time series deep learning systems are often assessed by checking temporal consistency on explanations, implicitly treating this as evidence of robustness. We show that this assumption can fail: Predictions and explanations can be adversarially decoupled, enabling targeted misclassification while the explanation remains plausible and consistent with a chosen reference rationale. We propose TSEF (Time Series Explanation Fooler), a dual-target attack that jointly manipulates the classifier and explainer outputs. In contrast to single-objective misclassification attacks that disrupt explanation and spread attribution mass broadly, TSEF achieves targeted prediction changes while keeping explanations consistent with the reference. Across multiple datasets and explainer backbones, our results consistently reveal that explanation stability is a misleading proxy for decision robustness and motivate coupling-aware robustness evaluations for trustworthy time series tasks.

Cross multi-domain log anomaly detection aims to train a unified model applying in multiple heterogeneous systems, alleviating the annotation cost and scalability bottlenecks of traditional cross single-domain approaches. However, existing methods face two fundamental challenges: (i) geometric proximity alone is insufficient to certify normality, and (ii) forcibly aligning distributions across domains can induce negative transfer. To address these issues, we propose MPFM (Cross Multi-Domain Prototype Flow Matching for Log Anomaly Detection), grounded in the principle that anomalies are samples that cannot be stably generated by the normal data-generating mechanism. Specifically, MPFM employs a shared–private prototype system to disentangle cross-domain commonalities from domain-specific patterns, introduces domain-conditioned flow matching to perform anomaly detection by integrating structural and dynamical evidence, and further leverages prototype-drift-driven hard example mining to improve robustness near decision boundaries. Experiments on HDFS, BGL, Thunderbird, and Spirit demonstrate that MPFM delivers substantial gains under multi-domain joint training.