Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-13T10:23:38.252227Z
Paper Citation Record · LEDGER
Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges
As of 7 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2604.04997.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
10 of 10 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved9
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation a640463d-41db-4d00-84f4-ad2f3c89df2f · outbound
This paper cites Qwen2.5-VL Technical Report.
Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges Qwen2.5-VL Technical Report
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T10:23:38.252227Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 327e26c3-81f1-4bc0-8ef2-9132f67cfad0 · outbound
This paper cites Semantic Instance Segmentation with a Discriminative Loss Function.
Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges Semantic Instance Segmentation with a Discriminative Loss Function
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T10:23:38.252227Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d12608a9-c756-45f1-a6da-387da50789a7 · outbound
This paper cites mmE5: Improving multimodal multilingual embeddings via high-quality synthetic data.
Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges mmE5: Improving multimodal multilingual embeddings via high-quality synthetic data
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T10:23:38.252227Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6348b99e-eac4-421e-854c-0053c8c59cbe · outbound
This paper cites Gemma 3 Technical Report.
Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges Gemma 3 Technical Report
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T10:23:38.252227Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6389a32a-f430-46ad-9512-5f49b8004d8c · outbound
This paper cites Model card for vdr-2b-multi-v1.
Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges Model card for vdr-2b-multi-v1
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T10:23:38.252227Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3c2b68ee-8bd0-443c-9d57-15e0861e819e · outbound
This paper cites Model card for mistral-small-3.2-24b-instruct-2506.
Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges Model card for mistral-small-3.2-24b-instruct-2506
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T10:23:38.252227Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4c4c7c1d-db6e-403f-9f95-13aa5dcf545c · outbound
This paper cites Learning Transferable Visual Models From Natural Language Supervision.
Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges Learning Transferable Visual Models From Natural Language Supervision
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T10:23:38.252227Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 799cc8b0-9f54-4284-a546-079d56eaf580 · outbound
This paper cites Chi, Quoc V.
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T10:23:38.252227Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 09101564-083c-4c12-a45b-25aa8df6c83c · outbound
This paper cites Improve Multi-Modal Embedding Learning via Explicit Hard Negative Gradient Amplifying.
Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges Improve Multi-Modal Embedding Learning via Explicit Hard Negative Gradient Amplifying
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T10:23:38.252227Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0db46689-fc17-4661-b21a-50998fe0b545 · outbound
This paper cites GME: Improving Universal Multimodal Retrieval by Multimodal LLMs.
Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges GME: Improving Universal Multimodal Retrieval by Multimodal LLMs
Reference 10
- Resolution
- malformed identifier
- no resolver link, observed 2026-07-13T10:23:38.252227Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.