Precomputing the Future-Offset Average in TriAttention
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 prun...
| Authors | Amarnath Mukherjee |
|---|---|
| Primary category | cs.DS |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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