[2603.19687] Diminishing Returns in Expanding Generative Models and Godel-Tarski-Lob Limits

Computer Science > Logic in Computer Science arXiv:2603.19687 (cs) [Submitted on 20 Mar 2026] Title:Diminishing Returns in Expanding Generative Models and Godel-Tarski-Lob Limits Authors:Angshul Majumdar View a PDF of the paper titled Diminishing Returns in Expanding Generative Models and Godel-Tarski-Lob Limits, by Angshul Majumdar View PDF HTML (experimental) Abstract:Modern generative modelling systems are increasingly improved by expanding model capacity, training data, and computational resources. While empirical studies have documented such scaling behaviour across architectures including generative adversarial networks, variational autoencoders, transformer-based models, and diffusion models, the theoretical limits of capability growth in expanding generative systems remain poorly understood. In this paper we develop a general task-space framework for analysing expanding generative reasoning systems. Each system induces a subset of a global task space representing the tasks it can successfully solve, and system capability is measured by the probability mass of this solved-task set under a fixed task distribution. Within this framework we prove a structural result showing that, under mild assumptions, the marginal improvement in solved tasks must converge to zero as system capacity increases. Thus expanding generative systems may continue to gain capability, but the probability mass of newly solvable tasks necessarily diminishes asymptotically. We further provide a p...