Research

Technical Publications

Foundational research on semantic communication, agent metering, and autonomous commerce protocols.

Information TheorySemantic CommunicationMathematical FoundationsIn Preparation

Semantic Information Theory: A Complete Mathematical Framework Unifying Meaning and Information

A landmark extension of Shannon's information theory to semantic content. We formalize 'semantic information' as a measurable quantity distinct from syntactic entropy, prove rate-distortion bounds for meaning-preserving compression, and establish the theoretical foundations for cross-model AI communication.

Marc Anthony Giammarco
2025
73,000+ words • 84 Chapters • 10 Core Theorems • 628 Citations
Multi-Agent SystemsSemantic CompressionCross-Model CommunicationIn Preparation

Semantic Intermediate Representation (SIR): A Formal Framework for Cross-Model Agent Communication

We introduce SIR, a universal representation enabling AI agents built on different foundation models to communicate meaning with provable semantic preservation. The framework includes formal proofs of the R_SIR(D) rate-distortion bound and empirical validation across 12 foundation models.

Marc Anthony Giammarco
2025
116 pages • 175 Theorems • 123 Definitions • 57 Lemmas
Agent MeteringBlockchain SettlementFormal VerificationIn Preparation

Cognition Execution Units (CEU-1): A Formal Framework for Metering Trust and Value in Multi-Agent Systems

We introduce CEU-1, a formal primitive that captures the complete lifecycle of agent interactions from cost estimation through cryptographic settlement. The 8-stage lifecycle provides formal atomicity, immutability, and verifiability guarantees.

Marc Anthony Giammarco
2025
17,000 words • 13 Theorems • 8-Language Polyglot Implementation
Research Philosophy

Our Research Focus Areas

Our research addresses the foundational problems that must be solved before AI agents can autonomously collaborate across organizational boundaries.

Semantic Information Theory

The Problem

When two AI systems communicate, they exchange tokens. But tokens are not meaning. The same sequence of tokens can carry different meaning depending on the model architecture, training data, and context.

Our Approach

We develop formal mathematical frameworks for representing, comparing, and preserving meaning across AI systems. This work draws on information theory, category theory, and formal semantics.

Key Contributions

  • Semantic Intermediate Representation (SIR) - architecture-independent meaning representation
  • Mathematical definitions of semantic equivalence and similarity
  • Algorithms for extracting semantic representations from model outputs
  • Empirical validation across propositional logic, predicate logic, and NLU tasks
Status: Publication-ready. Empirical validation complete.

Agent Trust Networks

The Problem

Human trust is built through reputation, legal systems, and social accountability. AI agents have none of these. How can an agent verify the identity of another agent? How can agreements be made binding?

Our Approach

We develop cryptographic and economic mechanisms that enable AI agents to establish trust without relying on centralized authorities or human intermediation.

Key Contributions

  • Cryptographic Agent Identity - unforgeable, persistent identities across organizations
  • Decentralized Reputation Systems - performance-based, Sybil-resistant design
  • Smart Contract Agent Agreements - blockchain-verified commitment and execution
  • Privacy-preserving reputation verification
Status: Core protocols implemented. Enterprise validation ongoing.

Cognition Exchange Units (CEU)

The Problem

When AI agents transact computational resources, how should exchanges be priced? Traditional markets assume fungible commodities, but AI cognition is highly differentiated.

Our Approach

We develop economic theory and practical mechanisms for pricing and exchanging AI cognitive resources, combining microeconomic theory, mechanism design, and empirical analysis.

Key Contributions

  • Cognitive Resource Taxonomy - categorizing and comparing AI capabilities
  • Dynamic Pricing Mechanisms - demand-based, capability-aware pricing
  • Exchange Protocols - request-response, quality verification, settlement
  • 8-stage lifecycle with formal atomicity guarantees
Status: Theoretical framework complete. Empirical validation in progress.

Enterprise Systems Architecture

The Problem

Research that cannot be deployed is research that cannot matter. But deploying AI infrastructure in Fortune 500 enterprises requires meeting stringent requirements that academic prototypes ignore.

Our Approach

We treat enterprise deployment requirements as first-class research constraints. Every system we build is designed for production from day one.

Key Contributions

  • Zero-trust network design and cryptographic key management
  • SOC 2 Type II, HIPAA, GDPR/CCPA control mapping
  • Multi-region deployment and high-availability patterns
  • Industry-specific compliance frameworks (financial, healthcare, government)
Status: Implemented in Quantum Railworks. Continuous refinement ongoing.
Partnership Opportunities

Research Areas of Interest

We actively seek collaborations with academic institutions and industry partners across these cutting-edge AI research domains.

Foundation Model Architectures

Next-generation architectures beyond transformers

AI Safety & Interpretability

Understanding and aligning AI systems

Fine-Tuning & Post-Training

Efficient adaptation and alignment methods

World Models

Internal representations for simulation and prediction

Embodied AI & Robotics

Physical intelligence and robot learning

ML Infrastructure & Efficiency

Scalable training and inference systems

Collaborate With Us

We actively seek collaboration with:

Academic ResearchersIndustry PartnersStandards BodiesEnterprise Customers

If your work intersects with ours in semantic theory, distributed systems, mechanism design, or enterprise architecture, we want to hear from you.

Contact Our Research Team