Paper Citation Record · LEDGER

Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2404.11792.

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

pith.paper-citation-record.v1
2404.11792 v2

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 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:29:40.762258Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T12:34:38.846835Z

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 a6d599c6-eeff-41e1-9682-e8d68ce6b705 · inbound

A Retrieval-Augmented Generation Framework for Academic Literature Navigation in Data Science cites this paper.

A Retrieval-Augmented Generation Framework for Academic Literature Navigation in Data Science Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T11:31:33.117636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f94699a3-3475-4017-b393-88067260bc79 · inbound

CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation cites this paper.

CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T15:53:07.576893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef7d8d9e-6195-4793-8d1f-48bb48655e09 · inbound

From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System cites this paper.

From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T11:29:40.762258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbeceba5-2633-4ff7-9fd1-970b1fc0d221 · inbound

Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications cites this paper.

Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:41:23.709473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fdb3e615-5310-4e82-82a0-2f353c558b7f · inbound

HPC-LLM: Practical Domain Adaptation and Retrieval-Augmented Generation for HPC Support cites this paper.

HPC-LLM: Practical Domain Adaptation and Retrieval-Augmented Generation for HPC Support Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:29:09.421337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation faff55d2-c757-4a7e-89d7-97c7ab4ffee0 · inbound

MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration cites this paper.

MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:34:38.848337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.