We perform surgery on neural networks. Mechanistic interpretability and ablation services to understand what your AI models know, how they decide, and what's hard to observe. Opening the black box.
What We Do
Identify and map the features your model has learned. Using sparse autoencoders and probing techniques, we surface likely representations of what concepts live inside your neural networks and how they're organized across layers.
Surgically remove or modify specific components to understand their function. We perform targeted ablations on attention heads, MLP neurons, and feature directions to isolate what each part contributes to model behavior.
Run a background check on your AI agents. We probe for indicators of deceptive patterns, hidden knowledge, and potential failure modes. Gain insight into model behavior before deployment.
Once you understand the internals, you can steer them. We develop activation steering vectors and representation engineering techniques to steer model behavior in targeted ways without retraining.
How We Work
Neural networks are becoming more interpretable. They're complex systems of learned features and circuits that can be understood, mapped, and modified. We apply rigorous mechanistic interpretability methods to surface the computational structure inside your models.
Mechanistic interpretability is a rapidly evolving field. Our work builds on the latest research from Anthropic, DeepMind, and the broader interpretability community. We apply the best available techniques while being transparent about their current limitations.
Define what behaviors or capabilities you want to understand. Form testable hypotheses about model internals.
Train sparse autoencoders and apply probing techniques to identify relevant features and their locations.
Perform targeted ablations and activation patching to establish causal relationships between components and behaviors.
Deliver interpretable maps of model internals with actionable recommendations for steering or modification.
Research
Malware is now using LLMs to rewrite itself at runtime, evading traditional detection. As AI models become embedded in our software supply chains, understanding their inner workings shifts from academic curiosity to security necessity.
Read moreThe foundational research on mechanistic interpretability. Understanding how transformers implement algorithms through reverse-engineering their weights and activations.
Read moreAnthropic's work on extracting interpretable features from Claude using sparse autoencoders. A breakthrough in understanding what concepts neural networks learn.
Read morePartnership
Neuron Surgery is a specialized interpretability practice operated by Polaris-N. We combine a specialized focus on mechanistic interpretability with practical security and AI deployment experience.
Visit Polaris-NGet in Touch
Ready to understand what's inside your models? Let's discuss how interpretability can improve your AI systems.