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Paper Citation Record · LEDGER

Improving Convergence and Generalization Using Parameter Symmetries

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2305.13404.

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

pith.paper-citation-record.v1
2305.13404 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:58:27.096742Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T18:50:30.158833Z

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 0e132255-ffe7-4f2a-ae40-fefbecd25cd5 · inbound

Parameter Symmetry Potentially Unifies Deep Learning Theory cites this paper.

Parameter Symmetry Potentially Unifies Deep Learning Theory Improving Convergence and Generalization Using Parameter Symmetries

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-08T19:58:27.096742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:58:27.096742Z digest=sha256:0bf70352efb0be58a9ffd37d6a28662c30bd1333a7ef41e09a9dbf7f89c996e2

Observation decbdb45-d7ab-4cc6-87df-5803ac34e32f · inbound

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations cites this paper.

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations Improving Convergence and Generalization Using Parameter Symmetries

Reference 69

Resolution
verified exact
arxiv_id, observed 2026-05-21T18:50:30.161371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-21T18:46:04.926179Z digest=sha256:3c3c713e9b3a624a55a916e15c3830ec2e7f47d573c20be0a1d2c1dc879c6076

Observation b968b451-a949-4687-b63f-12a440c3c59d · inbound

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations cites this paper.

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations Improving Convergence and Generalization Using Parameter Symmetries

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-03T23:21:42.380287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T23:21:42.380287Z digest=sha256:b44e8ad4aaca98643b959cfd25e30cba64236046d5f1c6c6957274efa495632f