Masked Attention Pooling Is Invariant to Padding and Permutation: A Formal Proof in Lean
Attention-based multiple instance learning (MIL) pools a variable-size bag of instance embeddings into one vector. In batched training, bags are padded to a common length, and the pooled output is expected not to depend on how much padding was added or on the order in which instances are stored. This expectation holds only if padded rows are removed from both the numerator and the softmax normalizer. Common implementation idioms meet it only under side conditions. Zeroing padded features alone does not work. Masking logits with $-\infty$ fails on empty bags and on nonfinite padding. Masking with a large negative constant is exact only after floating-point underflow. We define masked atten...
| Authors | Yusuf Khaled Sadek, Enes Yilmaz |
|---|---|
| Primary category | cs.LG |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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