Martian

Founded 2022

Evaluate and route queries across different language models based on model interpretability

You can use Martian to understand, measure, and steer large language model behavior in production. Developed by former researchers from DeepMind, Anthropic, and Meta, it applies mechanistic interpretability and model science to real-world AI applications. You can test model performance with dedicated benchmarks like Code Review Bench, implement online reinforcement learning for coding agents using ARES, and evaluate feature geometry through steering techniques. It allows teams to run the most effective language models for their specific workloads while maintaining deep visibility into complex black-box neural networks.

What Martian does

Route prompts between different foundation modelsEvaluate code generation and code review benchmarksAnalyze model feature geometry and representation spacesApply steering vectors to guide model generationTrain coding agents with online reinforcement learningInspect internal activations across transformer layersRoutes prompts dynamically to the best performing language model for each taskMeasures internal model representations using mechanistic interpretability methodsTests coding agent capabilities using the Code Review Bench evaluation frameworkTrains software engineering agents with ARES online reinforcement learningSteers model outputs directly through mathematical feature geometry techniquesMonitors neural network behavior beyond traditional static evaluations