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Does model scaling alone produce compositional generalization without symbolic mechanisms?
A broader line of inquiry — a family of 33 specific questions the research asks around this. Follow one into its inquiring-line page, or move sideways to a related line below.
Questions in this line of inquiry 33
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- Does scaling data automatically produce compositional reasoning or just better feature encoding?
- Where do neural networks still fail at compositional generalization despite scaling?
- What role does query-level exposure play in enabling compositional generalization?
- Does scaling model size solve compositional generalization problems?
- Can scaling alone create compositional generalization without explicit binding mechanisms?
- How does scaling and training data enable compositional behavior without symbolic mechanisms?
- Can recursion alone drive generalization better than model scale?
- Does compositional generalization emerge suddenly or improve smoothly with scale?
- Can single-hop knowledge automatically compose into multi-hop capability?
- Why does recursion on latent states improve generalization more than scale?
- What makes recurrent depth enable compositional generalization across tasks?
- Why does scaling data and model size improve compositional generalization?
- Why does compositional reasoning fail to explain cross-domain transfer?
- What makes recursive structure different from other forms of compositional generalization?
- Does sparsity enforce compositional structure or merely amplify existing modularity?
- Why does gradient descent discover compositional structure without explicit pressure?
- Can granular function calling tasks learn composition from graph-sampled data?
- Why does comparison reasoning generalize better than composition reasoning?
- Do larger models develop more abstract features than smaller ones?
- What architectural alternatives can capture compositional structure beyond pooled cosine?
- How does evidence retrieval affect compositional reasoning in language models?
- How does co-occurrence statistics alone produce hierarchical concept organization?
- Why do frequent words rank higher in taxonomic abstraction hierarchies?
- How do latents at the same hierarchy level become more correlated than tokens?
- Can learned verifiers over token similarity replace dense compositional training?
- Can token probability distributions extend swarm composition across different model architectures?
- What test distinguishes genuine compositionality from fractured feature presence?
- What makes hierarchical reasoning effective for taxonomy induction?
- How do Bayesian models share statistical strength across sparse user datasets?
- What makes structured stochasticity more effective than unstructured randomness in reasoning?
- What sampling strategies prevent nonsensical combinations when composing taxonomy nodes?
- Why do text-to-image models fail at composing multiple concepts together?
- Why are polysemantic features concentrated in early neural network layers?