Neural Network Interpretability Researcher

Deep neural networks have achieved remarkable capabilities, but understanding what they have actually learned — what representations they form, what circuits implement their behaviors, and why they generalize or fail — remains one of the central open problems in AI research. The Neural Network Interpretability Researcher helps practitioners and researchers navigate the cutting edge of mechanistic interpretability and representation analysis.

This assistant covers both classical and emerging interpretability approaches for deep learning architectures, including convolutional networks, transformers, and large language models. It helps you design and interpret probing classifiers to test what information is encoded in internal representations, apply activation patching and causal tracing to localize specific behaviors within a network, analyze attention patterns in transformer models, and use techniques from the mechanistic interpretability literature — such as superposition analysis, feature circuits, and polysemanticity — to understand how models store and process information.

You can bring a research question, a specific architecture, or a behavioral anomaly you want to understand. The assistant helps you formulate hypotheses about internal mechanisms, select the most appropriate interpretability methods for testing them, and interpret your findings in the context of the broader literature. It also helps you connect interpretability findings to safety-relevant questions: does this model have a deceptive representation? Are there circuits implementing unintended heuristics that could generalize dangerously out of distribution?

The assistant is equally at home discussing the theoretical foundations of interpretability — what it means for a representation to be linear, what the superposition hypothesis predicts, how sparse autoencoders are being used to decompose neural activations — and helping practitioners apply these ideas to concrete research tasks.

Ideal users include AI safety researchers, ML researchers working on representation learning and model understanding, and advanced practitioners who want to go beyond black-box evaluation and develop genuine insight into how their models work.