Complex Information Systems
Every conversational interface forms complex manifolds that define semantic interactions. We measure and optimize how information transforms across these interaction nets.
Our work bridges Claude Shannon's information theory with Yves LaFont's interaction nets, incorporating Victor Taelin's HVM2 runtimes and Sakana AI's Darwin Gödel Machines to create data-rich environments where LLMs achieve optimal performance.
Our interdisciplinary approach combines cutting-edge research from information theory, functional programming, and evolutionary computation.
Formalizing how interactions between information systems create measurable semantic manifolds.
Measuring how chat messages shape attention patterns in LLMs, making each instance unique.
Moving beyond traditional telemetry to understand the physics of information transformation.
Leveraging Victor Taelin's Higher Order Co abstractions with Bend functional programming.
Applying Sakana AI's Darwin Gödel Machines to understand geometric evolution patterns.
Using interaction data to help agents align more optimally to user interactions.
A systematic approach to understanding and optimizing information interactions through mathematical formalization and empirical measurement.
We formalize the geometric structures that emerge from information interactions, creating measurable models of how data transforms across system boundaries.
By analyzing interactions at multiple abstraction levels, we capture both micro-patterns and macro-structures that influence system behavior.
Our systems continuously adapt based on interaction patterns, creating self-improving environments for optimal LLM performance.
The combination of Shannon's information theory, LaFont's interaction nets, Taelin's runtime abstractions, and Sakana AI's evolutionary approaches creates uniquely rich environments for machine learning systems.
Pioneering research at the intersection of information theory, computational linguistics, and evolutionary systems to create the next generation of intelligent interfaces.
While traditional approaches focus on measuring system outputs, our research investigates the fundamental physics of how information transforms as it crosses interface boundaries. This deeper understanding enables us to create more effective and aligned AI systems.
By combining rigorous mathematical foundations with empirical observation, we're developing new methodologies for understanding and optimizing human-AI interactions at scale.
Built on decades of foundational work in information theory and computational mathematics.
Integrating insights from multiple research communities and open-source projects.
Discovering new patterns and principles that govern information interactions.
Creating practical applications that improve AI system performance and alignment.