Paper Citation Record · LEDGER

LongEmbed: Extending Embedding Models for Long Context Retrieval

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2404.12096.

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

pith.paper-citation-record.v1
2404.12096 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 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:55:40.315946Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T12:39:49.176532Z

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 103344b9-9bf8-450c-822c-82850473ccbd · inbound

Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text Approaches cites this paper.

Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text Approaches LongEmbed: Extending Embedding Models for Long Context Retrieval

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T14:55:40.315946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d576f3c-b7f1-45f7-9736-6a5458b8a13a · inbound

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings cites this paper.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings LongEmbed: Extending Embedding Models for Long Context Retrieval

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:41.561351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e9e472f-cde0-4800-a99d-94003388c8e3 · inbound

EmbeddingGemma: Powerful and Lightweight Text Representations cites this paper.

EmbeddingGemma: Powerful and Lightweight Text Representations LongEmbed: Extending Embedding Models for Long Context Retrieval

Reference 27

Resolution: malformed identifier; arxiv_id, observed 2026-05-15T12:07:21.027986Z

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.

Observation 69cd4032-b75c-4397-b829-1cccfb512e41 · inbound

HAKARI-Bench: A Lightweight Benchmark for Comparing Retrieval Architectures and Efficiency Settings under Unified Conditions cites this paper.

HAKARI-Bench: A Lightweight Benchmark for Comparing Retrieval Architectures and Efficiency Settings under Unified Conditions LongEmbed: Extending Embedding Models for Long Context Retrieval

Reference 152

Resolution: verified exact; arxiv_id, observed 2026-07-04T11:59:51.148061Z

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.

Observation 6316441a-ef31-430b-bf10-616ef0dc427c · inbound

Improving Long-Context Retrieval with Multi-Prefix Embedding cites this paper.

Improving Long-Context Retrieval with Multi-Prefix Embedding LongEmbed: Extending Embedding Models for Long Context Retrieval

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T12:39:49.178468Z

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.

Observation 2ae6d31a-3728-4967-bdcf-cfc421db5ff3 · inbound

Bekko Embedding: Parameter-Efficient Multilingual Retrieval with Ultra-Compact Encoders cites this paper.

Bekko Embedding: Parameter-Efficient Multilingual Retrieval with Ultra-Compact Encoders LongEmbed: Extending Embedding Models for Long Context Retrieval

Reference 112

Resolution: unresolved; no resolver link, observed 2026-08-01T03:16:00.737130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.