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Paper Citation Record · LEDGER

Scaling Laws for Associative Memories

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.02984.

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

pith.paper-citation-record.v1
2310.02984 v2

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 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:31:19.171471Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T23:10:44.860801Z

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 b81b91af-df0c-4091-a82e-b4c499256fed · inbound

The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations cites this paper.

The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations Scaling Laws for Associative Memories

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:19.171471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:19.171471Z digest=sha256:35a31a5d049076409c08361e29bcd11a9731622c331061fe9d5928cc1c6fb207

Observation e3e4f27d-6cd1-488b-ac03-dc1803f1a87a · inbound

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

Provable Knowledge Acquisition and Extraction in One-Layer Transformers Scaling Laws for Associative Memories

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-21T23:10:44.862743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-21T23:05:56.687644Z digest=sha256:f513b2b967c16a05d221b09eded9403b1a00bd42a945ccdce5d7eecad9e71729

Observation a31b80b2-5637-44a4-b8dc-16a9dfbc3ce1 · inbound

From Zipf's Law to Neural Scaling through Heaps' Law and Hilberg's Hypothesis cites this paper.

From Zipf's Law to Neural Scaling through Heaps' Law and Hilberg's Hypothesis Scaling Laws for Associative Memories

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-03T16:34:33.084703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T16:34:33.084703Z digest=sha256:7cba2a1bb3309c518dd345266aeb24a51984b7ad98ad3acee286cc678a8000b2

Observation 4f65800a-3e51-44f0-8c5c-5c7099804397 · inbound

Muon in Associative Memory Learning: Training Dynamics and Scaling Laws cites this paper.

Muon in Associative Memory Learning: Training Dynamics and Scaling Laws Scaling Laws for Associative Memories

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-03T04:14:13.432391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T04:14:13.432391Z digest=sha256:c51143c10cd5e430143007177983ec7d2c4c151f42046c87d882413f40c1f806

Observation 6b9f30fd-ad69-4e47-b955-19eea330bd0c · inbound

Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval cites this paper.

Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Scaling Laws for Associative Memories

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T18:16:09.982062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-08T16:23:37.393165Z digest=sha256:995148ad8dd32a1a30fbc6c72a8b21b3ef3301ee0933dd2adfb2198d88ed311c

Observation 2471482c-4825-4f4d-a37f-772602908ce0 · inbound

Extending LLM Context via Associative Recurrent Memory cites this paper.

Extending LLM Context via Associative Recurrent Memory Scaling Laws for Associative Memories

Reference 27

Resolution
unresolved
no resolver link, observed 2026-07-14T04:23:21.000846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T04:23:21.000846Z digest=sha256:7673407873921e318d32081de3ff972ef2566d7bed643775238db86ff55822d7