SensorFault-Bench is a new CPS-grounded benchmark showing that clean-MSE rankings of forecasting models often disagree with their robustness under standardized sensor-fault scenarios across four real datasets.
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
7 Pith papers cite this work. Polarity classification is still indexing.
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A computational argumentation framework evaluates LLM summaries of parliamentary debates by checking preservation of formal argument structures tied to contested proposals.
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
NEURON raises AUC from 0.74-0.77 to 0.84-0.88 on MIMIC-IV heart-failure mortality prediction while lifting human-aligned explanation scores from 0.50 to 0.85 by grounding SHAP values in SNOMED CT and patient notes via RAG-LLM.
A multi-modal DL model converts SD detection to a 2D spectrogram imaging task and fuses it with power vectors for improved accuracy and sub-second inference on variable EEG electrode placements.
Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.
citing papers explorer
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Benchmarking Sensor-Fault Robustness in Forecasting
SensorFault-Bench is a new CPS-grounded benchmark showing that clean-MSE rankings of forecasting models often disagree with their robustness under standardized sensor-fault scenarios across four real datasets.
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Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation
A computational argumentation framework evaluates LLM summaries of parliamentary debates by checking preservation of formal argument structures tied to contested proposals.
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Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
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NEURON: A Neuro-symbolic System for Grounded Clinical Explainability
NEURON raises AUC from 0.74-0.77 to 0.84-0.88 on MIMIC-IV heart-failure mortality prediction while lifting human-aligned explanation scores from 0.50 to 0.85 by grounding SHAP values in SNOMED CT and patient notes via RAG-LLM.
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Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach
A multi-modal DL model converts SD detection to a 2D spectrogram imaging task and fuses it with power vectors for improved accuracy and sub-second inference on variable EEG electrode placements.
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Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.
- Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions