Universal Weighted-Knowledge Bases for Task-Unaware Semantic Communication Systems
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Bibliographic record
Abstract
In the upcoming sixth-generation (6G) networks, semantic communication has made remarkable strides, where the transceivers utilizing local knowledge bases (KBs) to encode and recover semantic information. In this paper, we propose a universal weighted-KB (UW-KB) endowed with a sample confidence function for an end-to-end (E2E) task-unaware semantic communication system, where both the KB and semantic coding networks at the transceivers are incomplete in the initial stages. This intelligent UW-KB is shaped by receiver feedback during training, autonomously assigning weights to samples to mitigate biases in KB data, which significantly improves the efficiency of semantic coding networks. Simulation results demonstrate the effectiveness of our UW-KB in addressing KB data bias, providing valuable insights to bolster the robustness of task-unaware semantic communication systems.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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