InteractionNets

Complex Information Systems

Defining the Physics ofInformation Interactions

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.

Research Focus Areas

Our interdisciplinary approach combines cutting-edge research from information theory, functional programming, and evolutionary computation.

Interaction Nets

Formalizing how interactions between information systems create measurable semantic manifolds.

Attention Physics

Measuring how chat messages shape attention patterns in LLMs, making each instance unique.

Information Manifolds

Moving beyond traditional telemetry to understand the physics of information transformation.

HVM2 Runtimes

Leveraging Victor Taelin's Higher Order Co abstractions with Bend functional programming.

Evolution Geometries

Applying Sakana AI's Darwin Gödel Machines to understand geometric evolution patterns.

Agentic Alignment

Using interaction data to help agents align more optimally to user interactions.

Our Methodology

A systematic approach to understanding and optimizing information interactions through mathematical formalization and empirical measurement.

Semantic Manifold Mapping

We formalize the geometric structures that emerge from information interactions, creating measurable models of how data transforms across system boundaries.

Multi-Layer Analysis

By analyzing interactions at multiple abstraction levels, we capture both micro-patterns and macro-structures that influence system behavior.

Real-Time Optimization

Our systems continuously adapt based on interaction patterns, creating self-improving environments for optimal LLM performance.

Data-Rich Environments

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.

Information-theoretic foundations
Interaction net formalization
Functional runtime optimization
Evolutionary geometry patterns

About Our Work

Pioneering research at the intersection of information theory, computational linguistics, and evolutionary systems to create the next generation of intelligent interfaces.

Beyond Traditional Telemetry

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.

Information TheoryInteraction NetsFunctional ProgrammingEvolutionary Computation

Research Foundation

Built on decades of foundational work in information theory and computational mathematics.

Collaborative Approach

Integrating insights from multiple research communities and open-source projects.

Novel Insights

Discovering new patterns and principles that govern information interactions.

Real-World Impact

Creating practical applications that improve AI system performance and alignment.