A really interesting paper on representation geometry in LLMs written by my friend @frankniujc :
“Hypothesis-Driven Feature Manifold Analysis in LLMs via SMDS” proposes a model-agnostic way to test geometric hypotheses about latent representations instead of assuming everything is just linear directions. They find that different concepts naturally form different structures like circles, lines, clusters, and that these manifolds remain surprisingly stable across model families/sizes while also dynamically reshaping with context. Very cool bridge between mechanistic interpretability and representation geometry. 🔥
Especially liked the framing that reasoning may operate over structured manifolds rather than isolated features.
Paper: openreview.net/pdf?id=vCKZ40Y…
Code: github.com/UKPLab/tmlr202…
#LLM #MechanisticInterpretability #AIResearch #RepresentationLearning #TMLR #Interpretability #DeepLearning
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