Paper Citation Record · LEDGER

Memorization Capacity of Multi-Head Attention in Transformers

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 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 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:49:39.624488Z

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.

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.

Observation 289511ff-3ec4-438f-be27-4f0afc7f3bad · inbound

Attention Mechanism, Max-Affine Partition, and Universal Approximation cites this paper.

Attention Mechanism, Max-Affine Partition, and Universal Approximation Memorization Capacity of Multi-Head Attention in Transformers

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-16T05:49:39.624488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-16T06:30:59.297886+00:00.

Observation 459d6037-a32a-4761-9567-d68c15222fcb · 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: unresolved; no resolver link, observed 2026-08-15T20:39:09.951062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

Observation 387f4de9-42a7-4fd9-bcde-37b5866ee7aa · inbound

Capacity Matters: a Proof-of-Concept for Transformer Memorization on Real-World Data cites this paper.

Capacity Matters: a Proof-of-Concept for Transformer Memorization on Real-World Data Memorization Capacity of Multi-Head Attention in Transformers

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T19:51:54.915584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-16T06:30:59.297886+00:00.

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 2026-08-16T06:30:59.297886+00:00.