Adaptive Token Pruning for Transformers in Real-Time Monitoring Applications
Bibliographic record
Abstract
The deployment of Transformer-based architectures in real-time monitoring environments faces significant challenges due to the quadratic computational complexity associated with the self-attention mechanism. While Vision Transformers (ViT) and their sequential counterparts have demonstrated superior performance in anomaly detection and pattern recognition, their latency often exceeds the stringent requirements of industrial control systems, autonomous surveillance, and edge-based IoT frameworks. This paper introduces an Adaptive Token Pruning (ATP) mechanism designed to dynamically reduce the computational burden of Transformer networks during inference. By evaluating the semantic importance of tokens via attention weights relative to the classification token, our proposed method selectively discards redundant background information while preserving critical feature representations. We present a learnable thresholding policy that adjusts the pruning aggressiveness based on input complexity, ensuring optimal trade-offs between accuracy and throughput. Extensive experiments demonstrate that the proposed approach reduces Floating Point Operations (FLOPs) by approximately 40% while maintaining accuracy within a 0.5% margin of the baseline models on standard monitoring datasets.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".