Paper Citation Record · LEDGER

Shared Global and Local Geometry of Language Model Embeddings

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 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 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:53:47.405225Z

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 e6857bfc-e133-47eb-bc4b-af5aa067c455 · inbound

The Geometry of Self-Verification in a Task-Specific Reasoning Model cites this paper.

The Geometry of Self-Verification in a Task-Specific Reasoning Model Shared Global and Local Geometry of Language Model Embeddings

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T11:53:47.405225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

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 2026-08-18T06:34:40.430872+00:00.

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 2026-08-18T06:34:40.430872+00:00.

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 2026-08-18T06:34:40.430872+00:00.

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 2026-08-18T06:34:40.430872+00:00.