RESEARCH LAB
Our research team aims to fully understand model capabilities, advance Mechanistic Interpretability techniques, and develop better tooling for AI systems.
WHAT WE DO
Understanding Model Capabilities
We define understanding as the ability to extract the maximal insight about a model’s behavior for a given level of explanation complexity.
We begin by examining a single model performing a specific task. Think of it like your mental map of your friends: you might not know everything about them, but you still know which friend to ask for legal, technical, or ethical advice. Similarly, we aim to grasp what a model "knows" and how it will behave, even without full transparency into its internal workings.
We begin by examining a single model performing a specific task. Think of it like your mental map of your friends: you might not know everything about them, but you still know which friend to ask for legal, technical, or ethical advice. Similarly, we aim to grasp what a model "knows" and how it will behave, even without full transparency into its internal workings.
OUR APPROACH
Model Mapping.
A new interpretability Framework
This innovative approach transforms large, complex models into smaller predictors. These components retain just enough information to estimate how the original model performs on specific tasks.
This is the foundation that allows us to build systems that steer AI toward safe and effective behavior.
This is the foundation that allows us to build systems that steer AI toward safe and effective behavior.
Model mapping echoes how humans learn. We don’t memorize every detail. We build simplified mental models that still allow us to make accurate predictions: the same principle physicists use equations to describe messy reality.
By distilling models into compact, meaningful representations, we can study performance, safety, and ethical behavior without getting lost in their complexity.
OUR DIRECTION
Towards a safer and more reliable AI.
Today Martian tools deliver improved quality answers at lower cost to our clients. Our research pathways aim to deliver a step change in mechanistic interpretability (MI) tooling and AI safety, delivering real-world benefits.By advancing the scientific foundations of model analysis, we aim to deliver practical benefits: more reliable AI outputs, improved safety, and higher-fidelity alignment with human values.
Chain-of-thought faithfulness
Ethical, legal, and latency-aware routing
Uncertainty quantification
