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Paper Citation Record · LEDGER

Shared Global and Local Geometry of Language Model Embeddings

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2503.21073.

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

pith.paper-citation-record.v1
2503.21073 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:58:14.907095Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T05:15:22.273018Z

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 bbe0ba58-9f38-4d09-804c-726e2b9f7f3b · inbound

Training-Free Tokenizer Transplantation via Orthogonal Matching Pursuit cites this paper.

Training-Free Tokenizer Transplantation via Orthogonal Matching Pursuit Shared Global and Local Geometry of Language Model Embeddings

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T05:58:14.907095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:58:14.907095Z digest=sha256:11aabcba4afdbd9179da5528269529cf854582727fe9937440c725ba4400710e

Observation 079acf37-067e-437a-b9ab-1f0adc7c24d4 · inbound

The Indra Representation Hypothesis for Multimodal Alignment cites this paper.

The Indra Representation Hypothesis for Multimodal Alignment Shared Global and Local Geometry of Language Model Embeddings

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-10T22:15:48.729376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T20:06:03.531145Z digest=sha256:48afc6547692a3f4d437297ea15c6400c6609055c4aa1de93e1d403c4f3b76f1

Observation eeb8314e-0a9c-4af1-a38f-e86ae2e1a79f · inbound

The Cost of Language: Centroid Erasure Exposes and Exploits Modal Competition in Multimodal Language Models cites this paper.

The Cost of Language: Centroid Erasure Exposes and Exploits Modal Competition in Multimodal Language Models Shared Global and Local Geometry of Language Model Embeddings

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-10T13:35:26.797349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T13:25:27.762910Z digest=sha256:9678f1d4f253c8602c47d93be6af856dc8bb1a1739802991349de7e80b6b6205

Observation 64a63bd8-a23e-4345-b354-3bb5177584d1 · inbound

RDP LoRA: Geometry-Driven Identification for Parameter-Efficient Adaptation in Large Language Models cites this paper.

RDP LoRA: Geometry-Driven Identification for Parameter-Efficient Adaptation in Large Language Models Shared Global and Local Geometry of Language Model Embeddings

Reference 6

Resolution
malformed identifier
arxiv_id, observed 2026-05-11T13:01:25.053275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T02:26:52.361669Z digest=sha256:63e2136593d13e276d561d05ffb8bf014729ea89011579154a4cddb26fcbd8bc

Observation 84ce9db8-0be9-4c6f-8f45-bc36408a9eec · inbound

Learning Through Noise: Why Subliminal Learning Works and When It Fails cites this paper.

Learning Through Noise: Why Subliminal Learning Works and When It Fails Shared Global and Local Geometry of Language Model Embeddings

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:15:22.276166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-25T05:14:46.278482Z digest=sha256:78af19f12dcae4a8014ddd7b33079359f61322eed5059e0605697e3744d549c2