(Com)<sup>2</sup>Net: A Novel Communication and Computation Integrated Network Architecture
Bibliographic record
Abstract
With the wide deployment of computing capabilities and artificial intelligence (AI) techniques, Internet of intelligence is envisioned as a natural tendency in future networks. This technological revolution will foster abundant computing services with new requirements, such as distributed large AI model training and on-the-fly image rendering, which require dynamic collaboration of multi-dimensional resources from both the communication and computation perspectives. In this article, we introduce a communication and computation integrated network architecture, named (Com)2Net. Enormous computing traffic can be scheduled across space-air-ground domain, end-edge-cloud domain, and multi-data center domain, which facilitates ubiquitous connectivity and collaborative computation, thus supporting diverse advanced computing services. Furthermore, an intelligent resource adaption scheme is proposed to dynamically orchestrate multi-dimensional resources for massive concurrent computing tasks, in which quantum genetic algorithms are utilized to make the best joint decision for inter-domain traffic scheduling and intra-domain resource allocation. Finally, we present a case study, and discuss open research issues that are fundamental for efficient collaborative computation in (Com)2Net.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".