Paper Citation Record · LEDGER

RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2401.08406.

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

pith.paper-citation-record.v1
2401.08406 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:21:55.687566Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

51
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e6edb8b8-67f3-45fb-bc23-fac50ae1f363 · inbound

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions cites this paper.

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 73

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T02:46:27.353648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5d02c397-788b-46af-b46d-81afcf438c52 · inbound

A Survey on Large Language Models for Code Generation cites this paper.

A Survey on Large Language Models for Code Generation RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 90

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:18:06.450861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7c02bdb3-ccd8-4502-b440-651f604d29d8 · inbound

Knowledge Management for Automobile Failure Analysis Using Graph RAG cites this paper.

Knowledge Management for Automobile Failure Analysis Using Graph RAG RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T10:09:09.750367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82032150-6fcd-4a65-96f3-ce1859ce36a1 · inbound

A Large Language Model Approach to Identify Flakiness in C++ Projects cites this paper.

A Large Language Model Approach to Identify Flakiness in C++ Projects RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T14:13:50.022795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e62fc161-d0af-4e04-8f88-d9187f272f26 · inbound

Adaptations of AI models for querying the LandMatrix database in natural language cites this paper.

Adaptations of AI models for querying the LandMatrix database in natural language RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T13:36:21.191997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da5b8eae-ee06-4a65-9293-dd0a6dcb88e4 · inbound

ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine cites this paper.

ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T11:58:36.363251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a09967e-9457-49e0-a6fd-06aa0687e991 · inbound

Efficient Knowledge Injection in LLMs via Self-Distillation cites this paper.

Efficient Knowledge Injection in LLMs via Self-Distillation RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T11:52:37.121350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 956e66df-3455-40d0-91eb-49d0519ae64f · inbound

Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform cites this paper.

Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:05.603655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90134a62-5c2b-4768-acb7-547d2d876932 · inbound

MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation cites this paper.

MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T23:20:01.012259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15e398b9-1767-4aad-a10d-23fb85fb751e · inbound

APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel Encoding cites this paper.

APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel Encoding RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T19:28:43.436028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61b11cb7-541c-481d-900f-02f73ab0e72f · inbound

Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments cites this paper.

Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-22T17:11:49.729883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c5cbd918-4de7-4957-92b7-24643e87d4d3 · inbound

Optimizing Retrieval Augmented Generation for Object Constraint Language cites this paper.

Optimizing Retrieval Augmented Generation for Object Constraint Language RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:55.687566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca39461f-c058-42d9-89d4-e561298ec2c3 · inbound

Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks cites this paper.

Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:37.200193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bec8112-9cd3-4831-8ab0-ec7afcd86abe · inbound

A Practical Guide for Evaluating LLMs and LLM-Reliant Systems cites this paper.

A Practical Guide for Evaluating LLMs and LLM-Reliant Systems RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:58.679114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a16c343-36ce-466b-8ca1-db223d99cef0 · inbound

LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs cites this paper.

LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T13:59:47.109158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da0d8e46-fa56-44b5-acd8-d7927eeee0ab · inbound

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems cites this paper.

Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T21:59:59.980690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9f2eae5-6a77-4d1c-bf85-7dacc2db88df · inbound

GridCodex: A RAG-Driven AI Framework for Power Grid Code Reasoning and Compliance cites this paper.

GridCodex: A RAG-Driven AI Framework for Power Grid Code Reasoning and Compliance RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T17:22:37.047043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9dc44bc-a79a-4578-b5c2-3e942b1c687e · inbound

DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence cites this paper.

DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T12:11:34.167576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac1d85a5-d11e-475b-860e-0fa14fc4d9a5 · inbound

CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation cites this paper.

CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T15:28:02.419717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b5cea1c5-1ae8-4b08-85bf-9711c63a4da1 · 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 RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:41:23.688867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c7c23bf6-edc1-4af5-9324-b8b7d2bb32fe · inbound

Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm cites this paper.

Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:56:25.543343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9cf50c5c-0bdd-4eef-ab95-8118e74d6c63 · inbound

CultivAgents: Cultivating Relationship-Centered Multi-Agent Systems for Personalized Gardening cites this paper.

CultivAgents: Cultivating Relationship-Centered Multi-Agent Systems for Personalized Gardening RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:00:19.116122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b52e266d-96bf-415f-b162-81a7688a117c · inbound

NOMADD: Numerical Optimization of Models Adapting to Data Drift cites this paper.

NOMADD: Numerical Optimization of Models Adapting to Data Drift RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T15:05:18.123652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.