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Paper Citation Record · LEDGER

Memorization Capacity of Multi-Head Attention in Transformers

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2306.02010.

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

pith.paper-citation-record.v1
2306.02010 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:07:04.316421Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:29:50.983470Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b2cb4b70-ffaf-417d-9031-cd926654ebbb · inbound

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency cites this paper.

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency Memorization Capacity of Multi-Head Attention in Transformers

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-12T13:07:04.316421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T13:07:04.316421Z digest=sha256:01e8f65a1cbc6ff4c61161ffc1f85465b79bb4ce5b7f71d7035819d6a264322b

Observation 6a82dba1-446d-42fc-b8bd-1e17d94c9020 · inbound

Understanding Factual Recall in Transformers via Associative Memories cites this paper.

Understanding Factual Recall in Transformers via Associative Memories Memorization Capacity of Multi-Head Attention in Transformers

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T19:41:31.829543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:41:31.829543Z digest=sha256:5305bb719d9e7ce34a1f67cc234cbeaedbda824195c8ca02697386276db9789d

Observation 4ddea9e2-cbf4-4517-807d-e5e1e98a3ef1 · inbound

Extracting memorized pieces of (copyrighted) books from open-weight language models cites this paper.

Extracting memorized pieces of (copyrighted) books from open-weight language models Memorization Capacity of Multi-Head Attention in Transformers

Reference 171

Resolution
verified exact
arxiv_id, observed 2026-05-22T14:01:38.076126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-22T13:59:05.460455Z digest=sha256:999038a19e9095e5a86c83ee43094ad52af3ba7ddba978ede8848be5cd2a795b

Observation 5711ed1a-7092-4b06-b5ac-7ff81ccd3cdb · inbound

How much do language models memorize? cites this paper.

How much do language models memorize? Memorization Capacity of Multi-Head Attention in Transformers

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:39.202808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:39.202808Z digest=sha256:f6b1db1f658d3985e682361f8ca8d48feacb4863c8d160d8da7805b7b4e0a0e5

Observation 6bdd94b1-7785-4547-9142-481f60980056 · inbound

Provable Knowledge Acquisition and Extraction in One-Layer Transformers cites this paper.

Provable Knowledge Acquisition and Extraction in One-Layer Transformers Memorization Capacity of Multi-Head Attention in Transformers

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-21T23:10:44.872367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-21T23:05:56.687644Z digest=sha256:9ec3c5ade9e82493130ba20020b651c2f7e5dfdb7d53f0901e0cc0fdb356c225

Observation 7f6e4b7e-902b-4e54-b38d-8080297dbc95 · inbound

Structure Before Collapse: Transient semantic geometry in next-token prediction cites this paper.

Structure Before Collapse: Transient semantic geometry in next-token prediction Memorization Capacity of Multi-Head Attention in Transformers

Reference 120

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T13:29:50.985019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-26T05:14:07.208255Z digest=sha256:d8b0d33a7492b74cbbef6f87aa84c4ae73caaef9313af8e9ac91433fc7814c95