Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T23:48:07.554421Z
Paper Citation Record · LEDGER
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3
As of 23 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2506.16037.
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 2026-08-23T06:30:58.430688+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
13 of 13 outbound references displayed
- verified exact1
- verified fuzzy8
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation e0430962-32e6-42ba-a03f-2c3c3b3c95ee · outbound
This paper cites Cab-kws: Con- trastive augmentation: An unsupervised learning approach for keyword spotting in speech technology,.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Cab-kws: Con- trastive augmentation: An unsupervised learning approach for keyword spotting in speech technology,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:48:07.825987Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1b8bc425-8253-44b1-b0cb-5874935efc74 · outbound
This paper cites Attention-driven interaction network for e-commerce recom- mendations,.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Attention-driven interaction network for e-commerce recom- mendations,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:48:07.806307Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation e451ad7e-2013-4184-80b5-b883dbdf1cc0 · outbound
This paper cites FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:48:06.816069Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation afd3ab8c-3f1d-4f72-b0b5-de66c0a56720 · outbound
This paper cites Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systems.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systems
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:48:06.852776Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aef49496-a5f5-4f3e-85ce-eae748ab21f8 · outbound
This paper cites Coarse-to-fine multi-view 3d reconstruction with slam opti- mization and transformer-based matching,.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Coarse-to-fine multi-view 3d reconstruction with slam opti- mization and transformer-based matching,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:48:07.785505Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation a78ee2d3-eb80-4451-9d20-0dd7e4184a58 · outbound
This paper cites Breast cancer risk prediction: A machine learning study using network analysis,.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Breast cancer risk prediction: A machine learning study using network analysis,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:48:07.767090Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation cac47411-0d3a-4b2f-8773-5e75068b39fd · outbound
This paper cites Gemini-graphqa: Integrating language models and graph encoders for executable graph reasoning,.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Gemini-graphqa: Integrating language models and graph encoders for executable graph reasoning,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:48:07.746988Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation bd5a954e-3032-40e3-8bfc-41d24c39d6fc · outbound
This paper cites FinTextQA: A Dataset for Long-form Financial Question Answering.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 FinTextQA: A Dataset for Long-form Financial Question Answering
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:48:07.211413Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 02d0a714-3ca7-4caf-b3f6-535a1363c6d0 · outbound
This paper cites Hy- bridrag: Integrating knowledge graphs and vector retrieval augmented generation for efficient information extraction,.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Hy- bridrag: Integrating knowledge graphs and vector retrieval augmented generation for efficient information extraction,
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:48:07.341095Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8dade4c7-e5bf-4a11-ab57-610f3c5d39fc · outbound
This paper cites Evaluating retrieval- augmented generation models for financial report question and answer- ing.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Evaluating retrieval- augmented generation models for financial report question and answer- ing
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:48:07.712250Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 649383fa-4459-43b1-8981-fa1f146d6e01 · outbound
This paper cites Towards intelligent cloud scheduling: Dynasched-net with rein- forcement learning and predictive modeling,.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Towards intelligent cloud scheduling: Dynasched-net with rein- forcement learning and predictive modeling,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:48:07.694668Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 42e957a9-4994-431b-aa37-88fa6f175f59 · outbound
This paper cites Tax share analysis and prediction of kernel extreme learning machine optimized by vector weighted average algorithm,.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Tax share analysis and prediction of kernel extreme learning machine optimized by vector weighted average algorithm,
Reference 12
- Resolution
- verified exact
- doi, observed 2026-08-06T23:48:07.599359Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 7f093e72-017d-4d34-bf25-13612f5cda97 · outbound
This paper cites Privacypreservenet: A multilevel privacy-preserving framework for multimodal llms via gradient clipping and attention noise,.
Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3 Privacypreservenet: A multilevel privacy-preserving framework for multimodal llms via gradient clipping and attention noise,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T23:48:07.675770Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Pith citing papers
No inbound Pith citation observations are available.