Introducing SIR: Semantic Intermediate Representation
This article summarizes our peer-reviewed research paper. For complete theoretical foundations, proofs, and methodology:
Download Full Paper (116 pages, 175 theorems)How do you enable an AI agent built on Claude to communicate meaningfully with one built on GPT-4—while proving that meaning is preserved? This is the core challenge that Semantic Intermediate Representation (SIR) solves.
The Cross-Model Communication Problem
Modern AI agents are built on different foundation models, each with its own embedding space, tokenization scheme, and internal representations. When Agent A (GPT-4) sends a message to Agent B (Claude), the naive approach—just passing text—loses critical semantic nuances.
Consider a negotiation scenario where Agent A proposes "flexible payment terms." The concept of "flexibility" carries specific semantic weight in Agent A's model—perhaps learned from millions of contract documents. When Agent B receives this as raw text, it interprets "flexibility" through its own learned representations, potentially shifting the meaning in ways that derail the negotiation.
SIR: A Universal Semantic Layer
SIR introduces a universal intermediate representation that captures semantic content independent of any specific model architecture. The key insight comes from our foundational work on Semantic Information Theory: meaning can be formalized as a measurable quantity, distinct from Shannon entropy.
// SIR encoding preserves semantic relationships
Agent_A.encode(message) → SIR_representation
SIR_representation → Agent_B.decode(message′)
// Theorem: semantic_distance(message, message′) ≤ ε
The SIR framework consists of three components:
- Semantic Encoder: Maps model-specific representations to SIR space while preserving semantic relationships (distance, clustering, analogies).
- Semantic Core: A model-agnostic representation space with formally defined distance metrics and composition operations.
- Semantic Decoder: Reconstructs model-specific representations from SIR, optimized for each target architecture.
Theoretical Foundations
SIR is built on our Semantic Information Theory (SIT), which extends Shannon's information theory to semantic content. A key result is the RSIR(D) rate-distortion bound, which establishes the theoretical minimum bandwidth required to transmit semantic content with distortion at most D.
// Rate-Distortion Theorem for Semantic Communication
R_SIR(D) = min I(X; X̂)
subject to: E[d_semantic(X, X̂)] ≤ D
// Where d_semantic measures meaning preservation,
// not syntactic similarity
This bound is achievable—we prove the existence of encoding schemes that approach it. The full paper contains 175 theorems establishing properties including compositionality (semantic operations in SIR space correspond to meaningful operations in natural language) and stability (small perturbations in input produce bounded perturbations in semantic content).
Empirical Validation
We validated SIR across 12 foundation models spanning four architectural families. The experiments measured semantic preservation using human evaluation, downstream task performance, and automated semantic similarity metrics.
| Model Pair | Direct Transfer | Via SIR | Improvement |
|---|---|---|---|
| GPT-4 → Claude 3 | 73.2% | 94.7% | +21.5% |
| Claude 3 → Gemini | 68.9% | 92.1% | +23.2% |
| Llama 3 → GPT-4 | 71.4% | 93.8% | +22.4% |
| Mistral → Claude 3 | 65.3% | 91.6% | +26.3% |
| GPT-4 → Llama 3 | 70.1% | 93.2% | +23.1% |
Key findings from our empirical evaluation:
- Average improvement of 23.3% in semantic preservation across all model pairs
- Consistent gains regardless of architectural differences (transformer variants, mixture-of-experts, state space models)
- Bandwidth efficiency: SIR representations require 40-60% less bandwidth than naive embedding transfer while achieving higher fidelity
- Human evaluation correlation: 0.94 Spearman correlation between SIR semantic distance and human judgment of meaning similarity
Implementation in Quantum Railworks
SIR isn't just theoretical—it's the communication backbone of the Quantum Railworks platform. When agents from different enterprises negotiate, their messages pass through SIR encoding to ensure semantic fidelity regardless of underlying model architecture.
The implementation includes optimizations for production use: cached encoder/decoder pairs for common model combinations, streaming support for long-form content, and integration with our Cognition Execution Unit (CEU) framework for metered, verifiable transactions.
Open Research Directions
Our ongoing research at Giammarco Quantum Labs is extending SIR in several directions:
- Multimodal SIR: Extending the framework to preserve semantic content across modalities (text ↔ image ↔ code)
- Differential privacy: Enabling semantic communication while provably protecting sensitive information
- Adversarial robustness: Ensuring SIR encodings are resistant to semantic attacks
Access the Full Research
The complete SIR paper includes all proofs, experimental methodology, and implementation details. We welcome academic collaboration and peer review.