Exploring Scientific Principles and Laws of Artificial Intelligence, World Model, and Artificial General Intelligence (AGI) in Future Intelligence Networking: Paradigms, Architectures, and Innovations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it