SMDS analysis finds that temporal reasoning features in LLMs form distinct manifolds such as circles, lines, and clusters that reflect semantics, remain stable across models, support reasoning, and reshape with context.
In The Eleventh International Conference on Learning Representations
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Hypothesis-Driven Feature Manifold Analysis in LLMs via Supervised Multi-Dimensional Scaling
SMDS analysis finds that temporal reasoning features in LLMs form distinct manifolds such as circles, lines, and clusters that reflect semantics, remain stable across models, support reasoning, and reshape with context.