Cross-domain structural pattern
Latent assumptions in reasoning traces are converted into structured action space through decision tree mapping. Each tree path encodes environment assumptions (internal nodes) linked to action sequences (leaves), scored by scenario likelihood, goal utility, and execution cost. Uncertainty is handled by exploring assumption-action mappings rather than eliminating uncertainty through communication, transforming fragmented beliefs into rational action selection.
view paper→Loss scales inversely with depth due to functionally similar layers reducing error through ensemble averaging rather than compositional learning. Residual architecture bias combined with incompatible target functions produces inefficient yet robust regime. Improving efficiency requires architectural innovations encouraging compositional depth usage instead of redundant layer averaging.
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