arXiv:2601. 14590v3 Announce Type: replace Abstract: Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction.
arXiv:2607. 22045v1 Announce Type: new Abstract: Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's prediction to a desired outcome.
arXiv:2607. 17508v1 Announce Type: cross Abstract: We introduce Retrieval-Augmented Interpretable Learning (RAIL), a probabilistic meta-learning framework for zero-shot generation of task-specific interpretable models that synthesizes coefficient-space structure from natural-language task descriptions and a memory of previously learned task-specific predictors.
arXiv:2608. 00076v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) increasingly support high-stakes decision making by combining complementary information from images and text.
The paper introduces FCA‑Guided Counterfactual (FCA‑CF) explanations for multi‑modal breast cancer diagnosis, leveraging a Formal Concept Analysis lattice as a hard structural constraint to generate counterfactuals. On the TCGA‑BRCA dataset, FCA‑CF achieves perfect validity (100% prediction flips), the lowest average feature changes (2.37), and competitive proximity (0.900), outperforming four established counterfactual methods. Ablation studies show the lattice constraint and a greedy refinement phase are key to its sparsity and validity.
arXiv:2604. 23054v2 Announce Type: replace-cross Abstract: Predicting the outcomes of prospective clinical trials remains a major challenge.
arXiv:2607. 20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge, especially for smaller locally deployable models.
arXiv:2606. 18049v1 Announce Type: new Abstract: Decision-making with deep learning-based time series forecasting requires not only accurate predictions but also actionable insights.
The paper introduces FCA‑Guided Counterfactual (FCA‑CF) explanations for multi‑modal breast cancer diagnosis, leveraging a Formal Concept Analysis lattice as a hard structural constraint to search for counterfactuals. On the TCGA‑BRCA dataset, FCA‑CF achieves perfect validity (100% prediction flips), the lowest average feature changes (2.37), and competitive proximity (0.900) compared to four other methods. Ablation studies show the lattice constraint drives sparsity, while a greedy refinement phase further improves results.
iFlip is an iterative refinement method for generating counterfactual examples using large language models. It incorporates three feedback types—model confidence, feature attribution, and natural language—to guide successive edits. Experiments show iFlip outperforms five state‑of‑the‑art baselines, achieving a 57.8% higher validity rate and improving model performance through counterfactual data augmentation.
arXiv:2605. 22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging.
Decision-making with deep learning-based time series forecasting requires not only accurate predictions but also actionable insights. However, current architectures do not inherently provide such information.
arXiv:2603. 14158v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are entering clinical workflows, yet evaluations rarely assess how clinician reasoning shapes model behavior during clinical interactions.