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Paper Citation Record · LEDGER

L1 Augmented Attention as an Improved Vector Similarity Metric

As of 15 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 2026-08-14T06:32:32.682623+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=sha256:4747bd6637062023c854fe2b4e24f9bfe8b8191a313b34af2673c6e103170b12

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=sha256:9598dd59540bfd45d875bc4da47221935a40e1873ef774d54245a2f04e5dc9df

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=sha256:0a6c1d11e1508c05edc4467c8ffb931ace57f4ba2e19052d4b951a7d1e1cc452

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:e7df8382d0ea97360723999ab41d1948d2617c7b7efe1ad071c43807d8f966cc

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=sha256:8fd4d3f007759f9a84c5519abcf33503c93e1a3c8d5b3d1ddc184862c8ca53d5

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:cfedfd3c10a59a0956342cc7b690650414a0ee32b36b10c5473301c851c74aaf

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:fb89359175c0c33f38ac06640bd40c7f520df8bf315bd3655a81132e8b5bc3d8

Pith citing papers

No inbound Pith citation observations are available.