The paper investigates whether neural networks retain a case-based structure in their learned representations, enabling the decomposition of decision margins into contributions from individual training cases. By linking neural networks to Case-Based Decision Theory (CBDT), the authors identify conditions under which this recovered structure can be interpreted within CBDT and demonstrate that the resulting interpretation is consistent with the network’s original decisions. Experiments on a controlled CBDT setting and three real-world decision tasks confirm the viability of this approach.
arXiv:2607. 11347v1 Announce Type: new Abstract: Neural networks increasingly guide decisions in high-stakes domains such as medical diagnosis, credit approval, and energy bidding.
Neural networks increasingly combine data across populations, time periods, and operating conditions to improve generalization. This raises a reliability question: whether a model refitted on pooled data preserves an action ordering supported by both sources.
arXiv:2607. 27255v1 Announce Type: cross Abstract: Neural networks increasingly combine data across populations, time periods, and operating conditions to improve generalization.
arXiv:2606. 04045v1 Announce Type: cross Abstract: Representation learning is often described as preserving the information in an input that is relevant for prediction.
NeuroRule is a knowledge distillation framework that transforms high‑capacity neural networks into explainable rule‑sets. It adapts the EVOTER rule‑set evolution infrastructure to evolve propositional logic expressions that capture the neural network’s performance. The approach includes a conciseness objective to enhance explainability and demonstrates viability even without access to the original training data.
arXiv:2605.18202v2 Announce Type: replace-cross Abstract: Neuro-Symbolic Concept-based Models (NeSy-CBMs) are a family of architectures that integrate neural networks with symbolic reasoning for enha...
arXiv:2609.06862v1 Announce Type: new Abstract: Superposition refers to neural networks representing more features than they have dimensions. It offers a possible explanation for polysemantic neurons...
arXiv:2607. 21671v1 Announce Type: new Abstract: Neural network compression and interpretability remain open challenges in modern deep learn- ing, where billion-parameter architectures deliver impressive accuracy at the cost of trans- parency, computational efficiency, and reliable uncertainty quantification.
arXiv:2602. 21889v2 Announce Type: replace-cross Abstract: Predictions from ML models support human decision making in several fields, including high-stakes ones such as healthcare and the judiciary.
arXiv:2607. 08641v1 Announce Type: new Abstract: Over the last few years, there has been an increased interest in making machine learning models more interpretable.
The article argues that large language models (LLMs) are inadequate for consequential quantitative tasks such as pricing, risk assessment, and medical triage because language is a lossy representation of quantitative data that cannot be reversed. It formalizes this limitation as a property of the training representation rather than model capacity and identifies three essential properties—reproducibility, traceable lineage to source records, and calibrated uncertainty—that language substrates cannot provide. The authors propose a new class of models, Large Quantitative Models (LQMs), designed to meet these requirements.
The paper presents a necessary and sufficient condition for provable compositional generalization in neural networks, identifying two key principles: structural alignment and unambiguous minimized representations. It rigorously proves this condition, verifies it in Lean 4, and demonstrates its applicability in few-shot settings, including the SCAN jump task. The authors also outline an algorithmic approach and illustrate it with a minimal example, all derived purely from mathematical analysis without empirical validation.