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Paper Citation Record · LEDGER

L1 Augmented Attention as an Improved Vector Similarity Metric

As of 20 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 0 inbound Pith citation observations for arXiv:2607.18027.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.18027 v1

Coverage vector

measured 7 of 7 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T16:21:54.227358Z

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

7 of 7 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 67e6a282-833f-4083-80f7-04779b32ab58 · outbound

This paper cites Lipschitz constraints can endow models with provable robustness against adversarial perturbations …, and guaranteed generalisation bounds.

L1 Augmented Attention as an Improved Vector Similarity Metric Lipschitz constraints can endow models with provable robustness against adversarial perturbations …, and guaranteed generalisation bounds

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-01T16:21:54.204586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:21:54.204586Z digest=sha256:1a2d7349eca5e3a2fe359c0a8b7454ddb1cc3f57abc28c4fe591297f006c23cc

Observation 5ecc28cd-e85f-46dc-93e6-8c12b7f6754f · outbound

This paper cites As we will see, this explicitly unmasks the otherwise hidden norm information and provides access to a richer geometry when calculating vector similarity.

L1 Augmented Attention as an Improved Vector Similarity Metric As we will see, this explicitly unmasks the otherwise hidden norm information and provides access to a richer geometry when calculating vector similarity

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-01T16:21:54.208701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:21:54.208701Z digest=sha256:8ddeb8aef05547e476ca6ad899e15f8a06c885c5e85c22b219ecd32e67380cd2

Observation 95ffb299-ad8b-4664-833e-7c076ed80d63 · outbound

This paper cites an unresolved cited work.

L1 Augmented Attention as an Improved Vector Similarity Metric Unresolved cited work

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-01T16:21:54.211973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:21:54.211973Z digest=sha256:1aa51d09abb248bc5c576f91df8d90bb535b30efd3df1c5ccd9bd8d4097b1567

Observation f10e91ff-e367-4a32-9456-4fc479522bbd · outbound

This paper cites A striking finding in empirical work on word embeddings is that there is a sweet spot for the dimensionality of word vectors: neither too small, nor too large.

L1 Augmented Attention as an Improved Vector Similarity Metric A striking finding in empirical work on word embeddings is that there is a sweet spot for the dimensionality of word vectors: neither too small, nor too large

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-01T16:21:54.215144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:21:54.215144Z digest=sha256:038ff7666a6ca69cc899bfec3e7a21b5f2c45c7ceca4220b6159a20ff7dcb162

Observation 045a2706-a6fa-43a7-ac21-98fa46783c18 · outbound

This paper cites While section 1 presents some limitations of dot product, L1 Augmented Attention is also imperfect.

L1 Augmented Attention as an Improved Vector Similarity Metric While section 1 presents some limitations of dot product, L1 Augmented Attention is also imperfect

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-01T16:21:54.220428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:21:54.220428Z digest=sha256:9df69e9025db3f1e23a08b06e6436f9ddaf8e164986b94e4d94a25531703d934

Observation d290e1b2-db5b-4361-8c03-197be9302fe0 · outbound

This paper cites Another line of research to be explored is related to the choice of feature map for linear attention.

L1 Augmented Attention as an Improved Vector Similarity Metric Another line of research to be explored is related to the choice of feature map for linear attention

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-01T16:21:54.223890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:21:54.223890Z digest=sha256:5573d31011b859baa5c758a6351ba136d1e16e3f79b7f5b3fe232c2ff58f4eb6

Observation 79cb83c2-4fdc-4ddf-bac4-6b786b64b428 · outbound

This paper cites Music Transformer.

L1 Augmented Attention as an Improved Vector Similarity Metric Music Transformer

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-01T16:21:54.227358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:21:54.227358Z digest=sha256:7eaab692d6e629fcd6c90d8cdfbb7c5709c2a2f532057289dc574ecb04101def

Pith citing papers

No inbound Pith citation observations are available.