Can tiny recursive networks outperform massive language models?
Can a small network that recursively refines its reasoning on a latent state match or beat billion-parameter LLMs on hard reasoning puzzles? This challenges assumptions about scale and hierarchy in AI reasoning.
Can recurrent hierarchies achieve reasoning that transformers cannot? How do looped language models actually improve reasoning in depth? Can looped transformers generalize to unseen knowledge combinations? Why do transformers need explicit chain-of-thought reasoning? Can fixed points replace learned halt tokens in reasoning models?
Hierarchical dual-recurrence enables latent reasoning beyond transformer depth limits Looped transformers reapply feedforward stages through learned cyclic fixed points Looped transformers achieve compositional generalization through three-stage grokking Chain-of-thought is an architectural workaround, not true reasoning Fixed-point convergence outperforms learned halt tokens for adaptive inference
Do KANs maintain their advantages in deep architectures and large-scale training? Why does recursion on latent state drive generalization better than hierarchy? Can a single recursive network replace hierarchical dual-network architectures? How does upward distillation transfer knowledge from smaller to larger networks? Can a two-layer network outgeneralize billion-parameter models through recursion alone? Where do neural networks still fail at compositional generalization despite scaling? Why does recursion on latent states improve generalization more than scale? Can removing hierarchy from dual-recurrence models improve reasoning performance? What makes recursive depth more effective than parametric depth for puzzles? Can recursion alone drive generalization better than model scale? Can a tiny recursive network beat billion-parameter models on hard problems? Can looped architectures achieve reasoning abilities that fixed-depth models cannot?See all 14 inquiring lines on this note →