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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2602.10956 |
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| _version_ | 1866917317341872128 |
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| author | Hankemeier, Victoria Schilling, Malte |
| author_facet | Hankemeier, Victoria Schilling, Malte |
| contents | Spatio-temporal models analyze spatial structures and temporal dynamics, which makes them prone to information degeneration among space and time. Prior literature has demonstrated that over-squashing in causal attention or temporal convolutions creates a bias on the first tokens. To analyze whether such a bias is present in temporal attention mechanisms, we derive sensitivity bounds on the expected value of the Jacobian of a temporal attention layer. We theoretically show how off-diagonal attention scores depend on the sequence length, and that temporal attention matrices suffer a diagonal attention sink. We suggest regularization methods, and experimentally demonstrate their effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10956 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Stochastic Parroting in Temporal Attention -- Regulating the Diagonal Sink Hankemeier, Victoria Schilling, Malte Machine Learning Spatio-temporal models analyze spatial structures and temporal dynamics, which makes them prone to information degeneration among space and time. Prior literature has demonstrated that over-squashing in causal attention or temporal convolutions creates a bias on the first tokens. To analyze whether such a bias is present in temporal attention mechanisms, we derive sensitivity bounds on the expected value of the Jacobian of a temporal attention layer. We theoretically show how off-diagonal attention scores depend on the sequence length, and that temporal attention matrices suffer a diagonal attention sink. We suggest regularization methods, and experimentally demonstrate their effectiveness. |
| title | Stochastic Parroting in Temporal Attention -- Regulating the Diagonal Sink |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.10956 |