Beyond the Ergodic Wall: A Discrete Geometric Physics Sandbox for Analysing AI Scaling Limits and Complexity Collapse
cs.LG, cs.AI
Submitted: 2026-10-07
Updated: 2026-10-07
Sources
- Estimating the energy requirements for long term memory formation
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
- On the Binding Problem in Artificial Neural Networks
- A non-ergodic framework for understanding emergent capabilities in Large Language Models
- An Exceptionally Simple Theory of Everything
- An Icosahedral Quasicrystal as a Golden Modification of the Icosagrid and its Connection to the E8 Lattice
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks