Precomputing the Future-Offset Average in TriAttention
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Computer Science > Data Structures and Algorithms
Title:Precomputing the Future-Offset Average in TriAttention
Abstract:TriAttention is a recent method for shrinking the KV cache of long-reasoning LLMs: it scores each cached key by how much attention it is likely to receive and evicts the lowest-scoring ones. Because a key does not know how far away its future queries will sit, the score is averaged over a ladder of 17 possible future distances. We point out that this average is free: the future distance enters the score only through the position-dependent rotation, so the whole 17-fold average collapses--exactly, by a one-line algebraic identity--into a single per-band weight that is computed once, offline. Scoring a key then costs one evaluation instead of seventeen, with no change to which keys get pruned. The saving is modest and lives entirely in TriAttention's pruning-score computation, not in the attention kernel; we present it as a small, exact complement to their method, and we confirm the identity numerically.
| Comments: | 8 pages, 2 figures |
| Subjects: | Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.13051 [cs.DS] |
| (or arXiv:2607.13051v1 [cs.DS] for this version) | |
| https://doi.org/10.48550/arXiv.2607.13051
arXiv-issued DOI via DataCite
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