Modern AI systems can perform well while still behaving in ways that are difficult to predict, explain, or control. Mechanistic interpretability provides visibility into why decisions are made, enabling teams to diagnose failures, assess risk, and apply meaningful human oversight.

Without interpretability, teams are left reacting to symptoms rather than understanding root causes.

Cobalt enables teams to understand unexpected behavior, identify failure modes, detect spurious correlations, and surface patterns that traditional evaluations miss. These insights support more informed audits, targeted improvements, and safer deployment of AI systems.

The goal is to understand why behavior occurs before deciding how to intervene.

Topological data analysis reveals structure in complex, high-dimensional data without requiring predefined labels or assumptions. When applied to AI model internals, it makes it possible to surface patterns, clusters, and transitions that traditional metrics and evaluations often miss.

This empowers teams to discover unexpected behaviors and failure modes early, and to explore model behavior across multiple levels of resolution rather than relying on fixed tests alone.

Cobalt uses Cross-Layer Transcoders (CLTs) to identify features that persist and interact across multiple layers of a model.

Rather than treating each layer in isolation, CLTs link activations across layers into coherent features that better reflect how information flows through the model. This makes them particularly useful for circuit-level analysis and causal tracing.

Cobalt uses Sparse Autoencoders (SAEs) to extract interpretable features from a model’s internal activations.

Modern language models operate in high-dimensional spaces that are difficult to inspect directly. SAEs allow Cobalt to decompose these activations into a large set of sparse, reusable features that correspond to internal concepts the model relies on during computation.