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This paper presents a solution to Carl Hempel's statistical ambiguity problem in inductive-statistical inference by introducing maximally specific causal relationships (MSCRs) and proving their predictions are consistent, with implications for Causal AI and machine learning.
This paper argues that current AI models are predictive but not explanatory, and proposes Mechanistic World Models as a new paradigm that places reusable mechanisms at the center of representation, computation, and learning to enable autonomous scientific discovery.
A preprint arguing that AI can map known biology but cannot perform 'kind formation'—the minting of new variables and constraints—which requires the embodied engagement of human scientists. The authors propose a curriculum to train scientists as 'detectors of the unparameterized' to complement AI in biological discovery.
This paper examines Ray Kurzweil's thesis of accelerating returns and argues that while quantitative capabilities may accelerate, genuine scientific discovery requires a different capacity: qualitative reasoning about conceptual frameworks. It proposes the Qualitative Engine for Science (QES) as a response to this gap.
This paper applies philosophy of science to argue that LLMs offer epistemic value as minimal models for how-possibly explanations in linguistics, but do not yet qualify as how-actually explanations of human language.
The article explores the concept of illusions of understanding in scientific practice, discussing how ambiguous language, incomplete causal accounts, and satisfying but incomplete explanations can lead scientists to overlook deeper understanding.