Adaptive Token Pruning for Transformers in Real-Time Monitoring Applications
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
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 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