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Paper Citation Record · LEDGER

Language Models are Universal Embedders

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2310.08232.

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

pith.paper-citation-record.v1
2310.08232 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:48:01.889809Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d2a476cd-ade0-43e7-94d6-2761c7ac8f63 · inbound

Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning cites this paper.

Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning Language Models are Universal Embedders

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-05-24T04:33:53.645931Z

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=arxiv_source observed=2026-05-24T04:29:05.113230Z digest=sha256:df556f16f243a295134dffe8fc4b9713264a95540a4ff80b690d844648273e4c

Observation b8d4e2f2-f7fe-49f0-8aba-ce439538dab1 · inbound

Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing cites this paper.

Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing Language Models are Universal Embedders

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T13:48:01.889809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:48:01.889809Z digest=sha256:db494c4fbf9d71f2438266f1ae2b931338ff0e0deab01580d4526086b7640f96

Observation 0278ade8-7992-4456-aca6-2b4ac33e8f70 · inbound

GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training cites this paper.

GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training Language Models are Universal Embedders

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:29.530957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:29.530957Z digest=sha256:5a1d0f64e2b281473419ddbe0399d1091679d0a04b4dd74b5384043c9fdf0baf

Observation 9ee9f992-0319-452f-9318-4926fb7c705f · inbound

Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning cites this paper.

Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning Language Models are Universal Embedders

Reference 125

Resolution
unresolved
no resolver link, observed 2026-08-07T05:18:33.823315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:18:33.823315Z digest=sha256:c30cb7bb8ae5373bfe3d588f44cf0acdf6365f2a2e73fd47792f37de9c179e40

Observation 7d764506-9809-4df9-b08c-d063559ac7a6 · inbound

Causal2Vec: Improving Decoder-only LLMs as Embedding Models through a Contextual Token cites this paper.

Causal2Vec: Improving Decoder-only LLMs as Embedding Models through a Contextual Token Language Models are Universal Embedders

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-19T02:21:59.269987Z

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=2026-05-19T02:19:20.792488Z digest=sha256:7359eafa2953bde08b2aa9c70ad9cdaa5fc8d72bba97c4d649f4780c8c611002

Observation 15701eec-a68a-4602-bb80-7ceeb2824b31 · inbound

Temporal Self-Rewarding Language Models: Decoupling Chosen-Rejected via Past-Future cites this paper.

Temporal Self-Rewarding Language Models: Decoupling Chosen-Rejected via Past-Future Language Models are Universal Embedders

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T23:06:49.731775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T23:06:49.731775Z digest=sha256:877dd1b9aeb60a4df15e77481826f75d1174cf284b23723dedaddda47f7f92b7

Observation 51d59568-163a-4516-baa9-9ed13413b0fa · inbound

Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings cites this paper.

Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings Language Models are Universal Embedders

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-05T13:16:26.851241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:16:26.851241Z digest=sha256:eee0d122d8422f4394090105ae5d85db03326821d6a3425f9932a540e8d10b6c

Observation 3bdb5ebd-9b76-4ef6-880b-4620ec3a7171 · inbound

Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders cites this paper.

Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders Language Models are Universal Embedders

Reference 62

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T16:46:14.936176Z

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=arxiv_source observed=2026-05-09T15:03:33.323423Z digest=sha256:12d11946ecc4a3fa7667dea1ef74f60a3a8cf695fcb7ea649e40e21ef84a433b