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LLMs can be steered to better logical reasoning by exploiting a shared, low-dimensional "logical subspace" learned by aligning natural language and symbolic reasoning views.
Achieve 10% accuracy gains in tabular data prediction, not by brute-forcing bigger models, but by cleverly scaffolding LLM reasoning with interpretable decision trees.
