Results for “mechanistic-interpretability”

Train and analyze Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features for mechanistic interpretability research.

Run interpretability experiments on neural network internals using nnsight, with optional NDIF remote execution for massive models.

Implements finite automata, regular expressions, parsers, and lexers for building text processors and pattern matchers.

Model agent mental states using BDI (Beliefs, Desires, Intentions) ontology patterns, enabling cognitive reasoning, explainability, and semantic interoperability in multi-agent systems.