Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
As of 1 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2409.04701.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-31T06:34:12.847434+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-08T20:30:59.126121Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-10T06:15:00.866473Z
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 fe1596e0-b8bd-4a31-a0e9-c65923edc99a · inbound
Should We Still Pretrain Encoders with Masked Language Modeling? cites this paper.
Should We Still Pretrain Encoders with Masked Language Modeling? Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T06:32:07.634367Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 0cddb2e4-67d0-4a19-89ce-ccb4bfc0402b · inbound
Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval cites this paper.
Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T10:31:04.315061Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation b3607b9b-7a4b-420a-a8eb-1da11d011dbf · inbound
SPIRE: Structure-Preserving Interpretable Retrieval of Evidence cites this paper.
SPIRE: Structure-Preserving Interpretable Retrieval of Evidence Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 10
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-16T03:37:13.960980Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation c1551767-024f-4310-8e36-69cffa56c4ba · inbound
Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering cites this paper.
Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:51:33.688605Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 64258219-92d4-4c11-a459-027c5190ed3b · inbound
Qwen Goes Brrr: Off-the-Shelf RAG for Ukrainian Multi-Domain Document Understanding cites this paper.
Qwen Goes Brrr: Off-the-Shelf RAG for Ukrainian Multi-Domain Document Understanding Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T05:31:25.286534Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 2a45ffcf-6c5d-4c29-85ab-de67b2c81b70 · inbound
IdioLink: Retrieving Meaning Beyond Words Across Idiomatic and Literal Expressions cites this paper.
IdioLink: Retrieving Meaning Beyond Words Across Idiomatic and Literal Expressions Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T06:04:39.604235Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation fe14c051-8d41-4458-a752-a1ff973090b5 · inbound
Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations cites this paper.
Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T18:42:29.520964Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation a607de2b-55bd-4289-9141-0c519b0fa5b0 · inbound
Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking cites this paper.
Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 21
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T20:56:13.832403Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 510faa45-9d0a-4d72-8ecf-4d02e234c849 · inbound
EASE-TTT: Evidence-Aligned Selective Test-Time Training for Long-Context Question Answering cites this paper.
EASE-TTT: Evidence-Aligned Selective Test-Time Training for Long-Context Question Answering Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 42
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T17:17:15.110769Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 3d01698a-fd5f-4230-b2b6-44debdfe38cc · inbound
Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation cites this paper.
Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T00:19:13.217097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation c8776c16-f326-4f9c-81d3-ecf47ca1f263 · inbound
Improving Long-Context Retrieval with Multi-Prefix Embedding cites this paper.
Improving Long-Context Retrieval with Multi-Prefix Embedding Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T12:39:49.183603Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation ab67ba42-7c21-4413-a5c7-d3c8cad17abc · inbound
Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts cites this paper.
Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T06:57:42.262653Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 05fd7c64-495a-4834-9002-282c0731209a · inbound
Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts cites this paper.
Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 69
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T06:57:42.541870Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 71a8316c-4b90-469c-9eca-333ebc1253a6 · inbound
CMDR: Contextual Multimodal Document Retrieval cites this paper.
CMDR: Contextual Multimodal Document Retrieval Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Reference 18
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-08T20:35:34.380019Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.