Exploring Scientific Principles and Laws of Artificial Intelligence, World Model, and Artificial General Intelligence (AGI) in Future Intelligence Networking: Paradigms, Architectures, and Innovations
Bibliographic record
Abstract
Intelligence Networking (IN) is an emerging paradigm that seeks to embed intelligence into every layer of the network, enabling intelligent decision-making and service delivery to be as seamless and efficient as accessing conventional information. This survey offers a comprehensive overview of IN, focusing on its evolution, foundational technologies, architectural frameworks, core applications, and theoretical underpinnings of intelligence. It aims to serve as a valuable reference for researchers exploring the principles, structures, and mathematical modeling of IN. We begin by tracing the evolution of networking paradigms to highlight the growing interdependence between networking and intelligence, establishing the basic logic for IN's emergence. We then introduce a layered IN architecture and examine enabling technologies and applications across each layer. In addition, we explore the definition of intelligence within the context of networking, discuss relevant world models, analyze first principles derived from this definition, and explore the intrinsic connections between network intelligence and Artificial General Intelligence (AGI). The survey concludes with a discussion of future research directions and potential technological breakthroughs needed to realize the full promise of IN.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".