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This paper establishes foundational principles for deterministic encapsulation of generative models in traditional computational systems, defining four primitives and two anti-patterns to de-risk AI integration.
This paper formalizes calibration for probabilistic label ranking, introducing a hierarchy of calibration notions and showing that common models are poorly calibrated. It further demonstrates applications to RLHF reward models, where calibration correlates with but is not identical to accuracy.
CobwebTM is a low-parameter lifelong hierarchical topic modeling approach that adapts the Cobweb algorithm to continuous document embeddings, enabling unsupervised topic discovery and dynamic hierarchical organization without predefining topic counts. The method combines incremental symbolic concept formation with pretrained representations to achieve strong topic coherence while avoiding catastrophic forgetting.
OpenAI presents implicit generation and generalization methods for energy-based models (EBMs) that use Langevin dynamics for iterative refinement to generate samples without explicit generator networks. The approach offers advantages including adaptive computation time, flexibility in learning disconnected data modes, and built-in compositionality through product of experts.