attention_apply — LLMCORE score op

• Data kinds: attnmap × tokens → tokens

• Call: import fullseye as fs; fs.ledger.attention_apply(weights, value) -> 'np.ndarray' (to call the implementation directly, import llmcore; llmcore.attention_apply(weights, value) -> 'np.ndarray'; from the registry, opsllmcore.get("attention_apply"))

Usage

Mixes the values with the attention weights → tokens. Since row sums are 1, the output is a convex combination of the inputs.

Args:

weights: (T, S) float. Row sums must be 1 (output of attention_weights).

value: (S, dv) float. The side being mixed.

Returns:

tokens (T, dv) float: each row is a convex combination of the rows of value. The value range never leaves that of value

(at the same time the spread shrinks -- averaging crushes contrast).

Detailed usage guide

• llmcore family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_attention_identities — py -3.11 examples/poc_attention_identities.py

Ops the type connects to (they accept tokens as input)

rms_norm · rope_rotate · attention_scores · attention_softmax · attention_tiled · attention_linear · attention_grouped · kv_cache_decode

Same category (score)

attention_scores · attention_weights

*Provenance: llmcore.py — LLMCORE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.