This Blog was originally published as part of the posts from Martian

In the rapidly evolving landscape of artificial intelligence, we stand at a critical crossroads. The current paradigm of developing increasingly powerful monolithic frontier large language models (LLMs) faces fundamental limitations. While these generalist models have demonstrated impressive capabilities, they struggle with consistent factuality, domain expertise, bias, and hallucinations. Industry attempts to address these shortcomings have largely focused on internal model manipulation through prompt engineering, fine-tuning, or vector steering—essentially attempting to make these monoliths better at everything.

But what if this approach is fundamentally misguided?

The Problems with Monolithic AI

The current landscape suffers from several critical issues:

1. Winner-takes-all dynamics - The high costs of training frontier models leads to market concentration, with economic power concentrated in a few large corporations.

2. Misaligned safety incentives - Companies racing to release increasingly powerful models face pressure to underreport risks and rush products to market, creating systemic safety risks.

3. High barriers to entry - Specialized models struggle to gain market traction against generalist models, even when they excel in niche domains.

4. Limited user insight and control - Users have minimal visibility into how models "think" and limited ability to control characteristics like factuality, bias, or ethical reasoning.

5. Inefficient one-size-fits-all approach - Using powerful frontier models for all tasks, including simple ones, wastes resources and underperforms specialized alternatives.

A Better Approach: Expert Orchestration Architecture

Imagine an alternative architecture "Expert Orchestration" that intelligently selects from thousands of specialized models based on query requirements. 

This approach mirrors how humans navigate expertise in everyday life: we instinctively know which friend to consult about relationship problems versus legal matters versus car repairs. We make these routing decisions based on what these individuals excel at—not how their brains process information.

This framework has two key components:

1. Judges - Specialized evaluation models that assess various models' capabilities across dimensions that matter to users (factuality, domain expertise, ethics, bias, etc.).

2. Routers - Intelligent systems that direct queries to the most appropriate specialized models based on judge evaluations and user preferences.

The Benefits Are Substantial

Research shows this approach delivers several compelling advantages:

• Enhanced safety through decomposition - Complex requests can be broken into manageable steps executed by different models, reducing reliance on any single potentially misaligned model.

  
  

• Enhanced transparency and trust - Independent judges provide clear evaluations of model strengths and weaknesses across various characteristics.
  
  

• Democratized AI ecosystem - Specialized model developers can thrive by excelling in specific domains rather than competing with frontier models across all tasks.
  
  

• Superior performance at lower cost - Specialized models consistently outperform generalists in their domains of expertise, often at a fraction of the computational cost.

  
  

Join Us at the Mechanistic Router Interpretability Hackathon

Want to help build this future? Join us at the Apart x Martian Mechanistic Router Interpretability Hackathon from May 30 to June 1, 2025.

This hackathon will bring together researchers and developers to tackle core challenges in building effective model routing systems. You'll have the opportunity to:

• Develop techniques for evaluating model capabilities across various dimensions

• Create intelligent routing algorithms that match queries to specialized models

• Design frameworks for decomposing complex tasks across multiple models

• Build prototype systems demonstrating the power of this approach

Whether you're an AI researcher, ML engineer, or simply passionate about creating safer, more aligned, and democratic AI systems or simply passionate about creating more aligned, democratic AI systems, this hackathon offers a chance to contribute to a paradigm shift in how we approach artificial intelligence.

‍

Registration is now open! Visit the hackathon page to learn more and secure your spot.

‍Let's build a future where AI development prioritizes safety and alignment—not dominated by a race to create increasingly powerful monoliths, but flourishing through an ecosystem of specialized expertise orchestrated for maximum benefit to humanity