the Distilled Explanation Model
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DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks
arXiv:2605.31007v1 Announce Type: new Abstract: Anomaly detection in physiological sensor data from Wireless Body Area Networks (WBANs) can be caused by sensor faults, network disruptions, or missing data, leading to false alarms. Hence, it demands both high predictive accuracy and clinically interpretable explanations. Existing approaches rely either on black-box models that achieve strong performance but offer no transparency, or on post-prediction explanation methods such as SHAP and LIME.
Density-Guided Robust Counterfactual Explanations on Tabular Data under Model Multiplicity
Announce Type: new Abstract: Counterfactual explanations (CEs) are essential for actionable recourse, yet their reliability is often compromised in low-density regions, where classifiers exhibit high variance. Unlike existing methods that rely on expensive ensemble intersections to define stability, we propose \textit{DensityFlow}, a generative framework that constructs robust CEs by adhering to the high-confidence data manifold. Specifically, we model the counterfactual generation as...
TLRD: Teaching LLMs to Reason over Tabular Data with Tri-Level Rationale Distillation
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Decomposable Neuro Symbolic Regression
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LUNA-AD: Lightweight Uncertainty-Aware Language Model with Lifelong Learning for Autonomous Driving
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Human-Like Neural Nets by Catapulting
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Decoupled Smart Contract Audits: Lightweight LLM Framework via Distillation and Aggregation
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Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
arXiv:2605.29076v2 Announce Type: replace Abstract: LLMs have advanced text classification, yet existing paradigms face a trade-off: supervised (label only) fine-tuning is scalable but offers limited reasoning on complex text and lacks broader model transparency, while discrete prompt optimization offers human-readable instructions but struggles with performance and scalability. We introduce eXTC (eXplainable Text Classifier) with three progressive stages: (1) learning a Standard Operating...