Language Identification using Markov Chains, Hidden Markov Models, and Neural HMMs. Explores probabilistic and deep learning methods for sequence modeling, with ablation studies on context, hidden states, and optimization.

Linear-time sequence modeling that replaces attention's O(n²d) complexity with O(nd) summation-based aggregation. Demonstrates constraint-driven emergence: how functional representations can develop from optimization pressure and architectural constraints alone, without explicit pairwise interactions.