Rethinking Attention Weights as Bidirectional Coefficients
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
Attention mechanism has become an almost ubiquitous model architecture in deep learning. One of its distinctive features is to compute non-negative probabilistic distribution to re-weight input representations. This work reconsiders attention weights as bidirectional coefficients instead of probabilistic measures for potential benefits in interpretability and representational capacity. After analyzing the iteration process of attention scores through backwards gradient propagation, we proposed a novel activation function, TanhMax, which possesses several favorable properties to satisfy the requirements of bidirectional attention. We conduct a battery of experiments to validate our analyses and advantages of proposed method on both text and image datasets. The results show that bidirectional attention is effective in revealing input unit’s semantics, presenting more interpretable explanations and increasing the expressive power of attention-based model.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.005 | 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