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This paper introduces a statistically grounded framework using Bernoulli Naïve Bayes and χ²-guided binarization for interpretable clinical classification, achieving competitive AUC scores on benchmark datasets while providing explicit decision rules and calibrated risk estimates.
SciReasoner is a multimodal scientific foundation model that enables interpretable structural reasoning across proteins, molecules, and crystals, achieving state-of-the-art performance on 67 out of 86 benchmarks.
CALM is a framework for learning interpretable associations between brain regions and genetic pathways from completely unpaired datasets, enabling biomarker discovery for neuropsychiatric disorders like autism without requiring paired multimodal data.
GNBAN is a new graph-based neural architecture for long-horizon retail demand forecasting that combines heterogeneous graph learning with an interpretable basis-decomposition forecasting head, achieving 4-5% improvement on Walmart and Favorita benchmarks.
ThinkDeception proposes a novel framework that leverages multimodal large language models and a progressive reinforcement learning strategy with chain-of-thought reasoning for interpretable deception detection, achieving new state-of-the-art results on standard benchmarks.
A fine-grained study of narrative features in web-scale LLM pretraining data, introducing NarraBERT and NarraDolma to measure narrative patterns and their distribution across sources.
This paper introduces CDPR, a new approach for learning highly accurate and interpretable rule sets for classification using submodular maximization, achieving over 2.5x improvement in coverage compared to existing methods.
This paper evaluates the extent of undisclosed LLM-generated content in parliamentary texts from the UK and Sweden by training an interpretable text classifier, finding a steady increase in undisclosed LLM use from 2022 onwards.
Introduces ERP-XTTN, a cross-attention architecture for interpretable ERP classification across subjects without calibration. Evaluated on multiple datasets, it achieves competitive performance with black-box models while providing transparent routing insights.
This paper proposes an interpretable decision layer for AI-augmented classrooms that combines teacher and student feedback to rank course topics needing attention without using grades. The approach surfaces isolated learners and aligns with instructor concerns in a preliminary study.
This paper introduces Ex-ToxiCN-MM, the first Chinese harmful meme explanation dataset, along with a knowledge base C-HarmKB and an attribution analysis framework RIKE, to improve interpretable detection of harmful memes by considering cultural context and ambiguity.
This paper proposes an operational criterion for interpretable text representations based on inter-annotator agreement and label disentanglement, and introduces LLM-assisted Feature Discovery (LFD), a method that uses cross-LLM agreement screening and residual predictive gain to select clear, label-disentangled features. Experiments show LFD matches predictive performance while producing more interpretable features, validated by human audits.
OceanCBM is a concept bottleneck model for spatiotemporal prediction and mechanistic interpretability in ocean forecasting, using mixed supervision to predict mixed layer heat content while imposing soft physical structure. The model achieves interpretable, physically grounded representations without sacrificing predictive skill.