Compressed-Domain Inference

1. Motivation

High-fidelity digital twins require referencing extremely large personal knowledge corpora—writings, communications, research, and media—accumulated over years or decades.

Traditional AI architectures assume that data must be fully materialized before inference begins. That assumption creates severe memory and latency bottlenecks when corpus size grows, limiting the feasibility of faithful digital twins operating over lifelong knowledge.

2. The Compressed-Domain Paradigm

Compressed-domain inference enables computation directly on compressed representations, rather than requiring full decompression prior to inference. Core principles include:

• Partial data access
• Bounded memory usage
• Minimal time-to-first-compute
• Deterministic representations

These properties allow systems to scale without proportional increases in infrastructure cost, supporting next-generation AI and digital twin architectures.

3. Systems Implications

The paradigm affects system design in several ways:

• Reduced memory footprint
• Reduced I/O bandwidth
• Lower energy consumption
• Faster interactive systems
• Improved reproducibility

These properties support persistent digital twins capable of operating continuously over large corpora without unsustainable resource growth.

4. Relationship to Digital Twins

Digital twins require access to decades of personal knowledge. Compressed-domain inference allows systems to reference this knowledge without repeatedly decompressing entire datasets.

The result is a more scalable and economically viable architecture for digital twins, aligned with governance and continuity objectives for bounded, auditable systems.

5. Research Direction

The Global Science League is exploring compressed-domain inference as part of its broader research into deterministic AI infrastructure, digital twin governance, and contribution-based intelligence economies. This work is presented as a research framework and systems architecture direction, not as a product or implementation specification.