Agentic System as Compressor: Quantifying System Intelligence in Bits

Imagine you're building a system that needs to handle complex tasks like playing chess, answering questions, or even generating stories. Traditionally, these systems operate in isolation, making predictions based on fixed inputs without considering the broader context or the tools available to them. This can lead to inefficiencies, where the system struggles to adapt to new challenges or environments, resulting in what's called residual uncertainty — essentially, the leftover confusion after making a prediction.

In this paper, the authors tackle this issue by proposing a new way to think about how these systems can be more agentic, meaning they can interact with their environment, use tools, and adapt their strategies. They introduce a concept based on the idea that 'compression is intelligence,' suggesting that a more capable system can achieve its goals using fewer resources. By operationalizing this idea with techniques like arithmetic coding and evaluating it across various tasks, they show that incorporating agentic components can lead to a significant reduction in the complexity of the tasks at hand.

What sets this work apart from previous studies is its focus on measuring the effectiveness of these agentic systems in a structured way. The results indicate that by integrating these components, the systems can better manage their uncertainty and perform tasks more efficiently. For builders, this means that when designing systems that need to adapt and learn from their environment, considering how to reduce complexity through agentic interactions could lead to more effective solutions.

This work introduces a new analytical framework for evaluating agentic systems, extending existing methods.

The experiments cover diverse settings and provide solid evidence for the proposed measures.