Information Theory Guides Agentic LM System Design
Analysis
This paper introduces an information-theoretic framework to analyze and optimize agentic language model (LM) systems, which are increasingly used in applications like Deep Research. It addresses the ad-hoc nature of designing compressor-predictor systems by quantifying compression quality using mutual information. The key contribution is demonstrating that mutual information strongly correlates with downstream performance, allowing for task-independent evaluation of compressor effectiveness. The findings suggest that scaling compressors is more beneficial than scaling predictors, leading to more efficient and cost-effective system designs.
Key Takeaways
- •Introduces an information-theoretic framework for analyzing agentic LM systems.
- •Uses mutual information to quantify compression quality in a task-independent manner.
- •Demonstrates a strong correlation between mutual information and downstream performance.
- •Suggests scaling compressors is more effective than scaling predictors.
- •Enables more efficient and cost-effective system designs.
Reference / Citation
View Original"Scaling compressors is substantially more effective than scaling predictors."