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Paper Citation Record · LEDGER

Scaling Sentence Embeddings with Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2307.16645.

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

pith.paper-citation-record.v1
2307.16645 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T01:02:19.290688Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T05:49:36.568826Z

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 68cb3468-7d33-48b1-aac8-099dc5bc6c4e · inbound

E5-V: Universal Embeddings with Multimodal Large Language Models cites this paper.

E5-V: Universal Embeddings with Multimodal Large Language Models Scaling Sentence Embeddings with Large Language Models

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-16T22:52:20.966154Z

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=2026-05-16T22:52:20.935555Z digest=sha256:f457675214f16d182768b6e20eea851a584056e63ad6784ae9b304ad447af199

Observation d4489262-0e48-49a9-b1e0-08901c5a0712 · inbound

Division-of-Thoughts: Harnessing Hybrid Language Model Synergy for Efficient On-Device Agents cites this paper.

Division-of-Thoughts: Harnessing Hybrid Language Model Synergy for Efficient On-Device Agents Scaling Sentence Embeddings with Large Language Models

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-09T01:02:19.290688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T01:02:19.290688Z digest=sha256:30a955aef5148a3dc170a21fb18dd53f33e9dea6d7813a9142762ed72d4bee1d

Observation 18c0fd64-0ef1-41af-acbd-b61da648843a · inbound

Beyond Literal Token Overlap: Token Alignability for Multilinguality cites this paper.

Beyond Literal Token Overlap: Token Alignability for Multilinguality Scaling Sentence Embeddings with Large Language Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-08T15:23:18.559978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T15:23:18.559978Z digest=sha256:a1b7d1c6c2cc72e0af0738dfb4f9e913d1cb94101634d42ad56033d29b42a841

Observation d8214f8d-4915-4100-a9e4-2a19682c3f71 · inbound

Adapting Pretrained Language Models for Citation Classification via Self-Supervised Contrastive Learning cites this paper.

Adapting Pretrained Language Models for Citation Classification via Self-Supervised Contrastive Learning Scaling Sentence Embeddings with Large Language Models

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T15:38:29.606398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:38:29.606398Z digest=sha256:9e9e81bad4fec5869267335c49bd0e0d6ea3b563003ee75faf174b6ed3def514

Observation c19a8ece-cab0-41d7-8aa0-c4b3b9a326e6 · inbound

DeepRTL2: A Versatile Model for RTL-Related Tasks cites this paper.

DeepRTL2: A Versatile Model for RTL-Related Tasks Scaling Sentence Embeddings with Large Language Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T13:18:31.388942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:18:31.388942Z digest=sha256:8b401af701c9541bbd0ab99c8462a27d7ef44c13c68303238b5e52caa7290365

Observation b6f13da3-be39-4e45-b6e3-517c3e57ee37 · inbound

Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval cites this paper.

Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval Scaling Sentence Embeddings with Large Language Models

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-05T13:50:10.236047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:50:10.236047Z digest=sha256:e18be46f5b68836f7637ec25f34e817ff857cd15ebbbd37652c876fc70b5fe13

Observation 4d91cf6f-2ac9-4a20-92de-4f2266e204d9 · inbound

FreeRet: MLLMs as Training-Free Retrievers cites this paper.

FreeRet: MLLMs as Training-Free Retrievers Scaling Sentence Embeddings with Large Language Models

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:01:23.559306Z

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=2026-05-18T13:00:31.952588Z digest=sha256:2f65bc65cfab6e6a7e97ac079b9fe5252e455595acba2cc102d55fbcecb61b77

Observation 0c58442a-7344-4c20-834a-a54508027b37 · inbound

FreeRet: MLLMs as Training-Free Retrievers cites this paper.

FreeRet: MLLMs as Training-Free Retrievers Scaling Sentence Embeddings with Large Language Models

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-04T13:51:44.769800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T13:51:44.769800Z digest=sha256:7d6c3d879b1e6b10d9f6d974ccd1379e685f70799a1aba6da0766b69436fedc6

Observation b7be7ca8-cb4c-48d5-9d65-28ddc9800ac5 · inbound

Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations cites this paper.

Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations Scaling Sentence Embeddings with Large Language Models

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:06:04.805699Z

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=2026-05-10T04:09:10.125285Z digest=sha256:7216039421021960d407de70680f5885ede10674ffe77668a04321a5db5ace96

Observation eec67bf5-6a65-465b-b4b6-4bb3ccc8e06f · inbound

Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval cites this paper.

Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval Scaling Sentence Embeddings with Large Language Models

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:31:13.574254Z

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=2026-05-07T16:56:52.714346Z digest=sha256:3ef5ccf7a7a88195040cceef4c63aff2f3fcfd929c4cea0bbfc2fcdf9af4827b

Observation 85a7c15e-96eb-4b35-bf0d-e9a8fac82255 · inbound

GenAI Powered Dynamic Causal Inference with Unstructured Data cites this paper.

GenAI Powered Dynamic Causal Inference with Unstructured Data Scaling Sentence Embeddings with Large Language Models

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-11T02:25:53.617701Z

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=2026-05-11T02:24:49.296828Z digest=sha256:9a18965d161baba5246147aea774d77cdb77f22df5de0ffa74d6914ff5e28e46

Observation 36705e7d-6075-459d-a38f-d6fc34172d89 · inbound

ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval cites this paper.

ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval Scaling Sentence Embeddings with Large Language Models

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-07-04T05:49:36.579515Z

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=2026-06-26T15:34:55.062016Z digest=sha256:43fdc45847171677eb31253b5cbbf5515af79e0d382d237ab726ca8b0fc900af