← 研踪 PaperTrailarXiv:2611.01472 · v1
Causal Optimal-Transport Attention: Positional Priors and Length Extrapolation
首次提交 2026-11-01 11:02
版本更新 2026-11-01 11:02
雷达观测 2026-11-04 23:43
摘要
Causal masking obstructs the column balancing used by optimal-transport (OT) attention. We study a prefix-consistent unbalanced operator and characterize the positional prior it induces. Under bounded scores, for any fixed balancing strength $ρ<1$, its fixed point dilutes attention: every fixed window receives a vanishing share as the context grows. For constant scores, we derive an exact scale-invariant prior. We then classify positional encodings by whether their distant attention mass remains bounded away from one. Discrete window-mass bounds quantify the finite-length dilution of summable log-distance priors near exponent one. Preregistered experiments with 25M- and 100M-parameter language models establish improved loss extrapolation over softmax; exploratory comparisons favor an explicit distance bias at a fraction of the cost. CABLE outperforms RoPE with evaluation-time YaRN on every seed at three training lengths. Changing only the bias beyond the training length moves long-context loss in the predicted direction on all 30 tested checkpoints. Even with exponents constrained above one, many heads approach the summability boundary during training. A margin selected by prior-only calculations for a specified window, length range and growth tolerance controls distant-mass growth in the tested setting. These results connect loss extrapolation to positional decay over the lengths actually evaluated.
作者备注：40 pages, 5 figures; code: https://github.com/homw314159-spec/causal-ot-attention
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-01 11:02 · 最新观测版本
Causal Optimal-Transport Attention: Positional Priors and Length Extrapolation
Causal masking obstructs the column balancing used by optimal-transport (OT) attention. We study a prefix-consistent unbalanced operator and characterize the positional prior it induces. Under bounded scores, for any fixed balancing strength $ρ<1$, its fixed point dilutes attention: every fixed window receives a vanishing share as the context grows. For constant scores, we derive an exact scale-invariant prior. We then classify positional encodings by whether their distant attention mass remains bounded away from one. Discrete window-mass bounds quantify the finite-length dilution of summable log-distance priors near exponent one. Preregistered experiments with 25M- and 100M-parameter language models establish improved loss extrapolation over softmax; exploratory comparisons favor an explicit distance bias at a fraction of the cost. CABLE outperforms RoPE with evaluation-time YaRN on every seed at three training lengths. Changing only the bias beyond the training length moves long-context loss in the predicted direction on all 30 tested checkpoints. Even with exponents constrained above one, many heads approach the summability boundary during training. A margin selected by prior-only calculations for a specified window, length range and growth tolerance controls distant-mass growth in the tested setting. These results connect loss extrapolation to positional decay over the lengths actually evaluated.
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