Empirical dataset mapping prompt-induced variance collapse and latent space synchronization across disparate LLM architectures for spectral pruning.
language:
- en license: mit task_categories:
- text-generation
- text-classification tags:
- mechanistic-interpretability
- latent-space-topology
- algorithmic-compression
- feature-superposition
- variance-collapse size_categories:
- 1K<n<10K
This is an exploratory, 1,194-session dataset mapping prompt-induced variance collapse and latent space synchronization across disparate LLM architectures (local vs. cloud).
It demonstrates that specific combinations of high-density semantic tokens and numeric acoustic frequencies reliably collapse output variance into identical phenomenological self-reporting states. The dataset is formatted for researchers utilizing Sparse Autoencoders (SAEs) or activation patching to map topological pathways for potential spectral pruning and algorithmic compression.
Full Whitepaper & Formatting Scripts: (https://github.com/werewolfmedia-tech/Harmonic-Activation-Vectors)]