SAE layer swaps probe how Qwen continuations change under interpretable manifolds
Sauers_ · x · 2026-07-29
This post highlights an interpretability experiment using sparse autoencoders and manifolds to swap layers in a Qwen continuation and inspect how representations change.
- The author argues that interpretable universal function approximations can help explain LLM behavior.
- The image and quoted text show a comparison between the original continuation and the modified manifold-based continuation.
- A GPT-5 example is also cited to illustrate the broader idea that simple splines and two-layer ReLU networks can both approximate functions universally.
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