Paper Citation Record · LEDGER

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges

As of 19 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 2 inbound Pith citation observations for arXiv:2506.20598.

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

pith.paper-citation-record.v1
2506.20598 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

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measured 64 of 64 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation 3817cfa7-7256-442d-9f33-e1aeff9cabe8 · outbound

This paper cites and Guo, M., 2024.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Guo, M., 2024

Reference 2

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Observation 225e5086-bf0b-4743-8675-11cc06da49c4 · outbound

This paper cites and Narasagoudr, S.S., 2024.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Narasagoudr, S.S., 2024

Reference 3

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Observation fe4362f2-672d-4de6-955e-f23980b18894 · outbound

This paper cites and O'leary, J.A., 1976.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and O'leary, J.A., 1976

Reference 4

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Observation 49669105-3ee3-464d-87e8-57ab396d3c87 · outbound

This paper cites and Sambrook, I.E., 1977.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Sambrook, I.E., 1977

Reference 5

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Observation 0ac05388-264f-4260-9f3d-21603b55cce9 · outbound

This paper cites and Dyer, P.S., 2020.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Dyer, P.S., 2020

Reference 6

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Observation 7967941e-bf84-4ffe-bffa-47713ece8410 · outbound

This paper cites and Wall, B.T., 2021.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Wall, B.T., 2021

Reference 7

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This paper cites and Freedman, M.R., 2019.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Freedman, M.R., 2019

Reference 8

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Observation 8e0d6eec-a287-44a0-9c6c-611eb988961b · outbound

This paper cites and Ugbogu, O.C., 2016.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Ugbogu, O.C., 2016

Reference 9

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This paper cites and Weiss, G., 1999.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Weiss, G., 1999

Reference 10

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Observation f557eb6f-30df-41ca-8eec-c04cc9c209a6 · outbound

This paper cites Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

Reference 11

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Unresolved cited work

Reference 12

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This paper cites and Price, N.D., 2014.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Price, N.D., 2014

Reference 13

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This paper cites GPT-4 Technical Report.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges GPT-4 Technical Report

Reference 14

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges LLaMA: Open and Efficient Foundation Language Models

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This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Gemini: A Family of Highly Capable Multimodal Models

Reference 16

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This paper cites Scaling Laws for Neural Language Models.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Scaling Laws for Neural Language Models

Reference 17

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Language Models are Few-Shot Learners

Reference 18

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models,

Reference 19

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

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges A Survey on Large Language Models for Code Generation

Reference 20

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Large Language Models for Robotics: Opportunities, Challenges, and Perspectives

Reference 21

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges The Rise and Potential of Large Language Model Based Agents: A Survey

Reference 22

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This paper cites What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks

Reference 23

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation

Reference 24

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges KoLA: Carefully Benchmarking World Knowledge of Large Language Models

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors

Reference 26

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems

Reference 27

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges A survey on LLM-based multi-agent systems: workflow, infrastructure, and challenges,

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Large Language Models are Zero-Shot Reasoners

Reference 29

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 30

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges LLM4Rec: A Comprehensive Survey on the Integration of Large Language Models in Recommender Systems—Approaches, Applications and Challenges,

Reference 31

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Improving Language Understanding by Generative Pre-Training,

Reference 32

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges The Claude 3 Model Family: Opus, Sonnet, Haiku,

Reference 33

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This paper cites Closing the gap between open-source and commercial large language models for medical evidence summarization.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Closing the gap between open-source and commercial large language models for medical evidence summarization

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This paper cites Evaluation of open and closed-source LLMs for low-resource language with zero-shot, few-shot, and chain-of-thought prompting,.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Evaluation of open and closed-source LLMs for low-resource language with zero-shot, few-shot, and chain-of-thought prompting,

Reference 35

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Huang, K., 2025

Reference 36

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 37

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges PubMed Central: The GenBank of the published literature

Reference 38

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Li, K., 2019

Reference 39

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This paper cites Available at: https://github.com/pdfminer/pdfminer.six (Accessed: 25 June 2025).

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Available at: https://github.com/pdfminer/pdfminer.six (Accessed: 25 June 2025)

Reference 40

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This paper cites Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases

Reference 41

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models

Reference 42

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges GPT-4o System Card

Reference 44

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Available at: https://openai.com/index/gpt-4-1/ (Accessed: 25 June 2025)

Reference 45

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This paper cites When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models

Reference 46

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Jurgens, D., 2024, November

Reference 47

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Observation 4259c6b0-eb14-442c-843d-8033b337328a · outbound

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Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Sakr, M., 2024, March

Reference 48

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Observation 44a4a1c5-73c0-4cc6-b2ba-422952751864 · outbound

This paper cites and Hoque, E., 2020, May.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Hoque, E., 2020, May

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:29.574406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 54a0fd68-1d0f-486d-9716-547d00248534 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:24.827964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d456d0a4-8489-41ac-9362-e85478deea84 · outbound

This paper cites A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:25.036937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44b3b850-e829-41aa-ac35-4b35b9607c51 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:29.314446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3bb67c41-89e6-4c55-8d38-a63cbe0d0a38 · outbound

This paper cites Summarization for Generative Relation Extraction in the Microbiome Domain.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Summarization for Generative Relation Extraction in the Microbiome Domain

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:49:27.394535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 28c349f8-c2b2-4b56-a039-db9a75a57202 · outbound

This paper cites A Study of Biomedical Relation Extraction Using GPT Models.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges A Study of Biomedical Relation Extraction Using GPT Models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:29.064335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 64c72bbf-1122-40d1-b4f2-17824e395362 · outbound

This paper cites Learning to Route LLMs with Confidence Tokens.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Learning to Route LLMs with Confidence Tokens

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:25.596883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c8a8b54-f03c-450f-ade5-3ddc91787d45 · outbound

This paper cites and Fernández, J.H., 2022.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Fernández, J.H., 2022

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:28.795732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e99a9c16-78a9-4597-81ea-0263dcd16f8d · outbound

This paper cites an unresolved cited work.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:49:28.610298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bba32164-5a8e-41d4-bd5f-ce4d9d858b19 · outbound

This paper cites and Ong, W.K., 2019.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Ong, W.K., 2019

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:28.384559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5db1a622-08c8-4536-92f3-519d73674b89 · outbound

This paper cites and Petryszak, R., 2024.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Petryszak, R., 2024

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:28.361402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aae18092-65ec-41a0-bc0c-aef62abfba09 · outbound

This paper cites and Muller, K.R., 2009.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges and Muller, K.R., 2009

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:28.112692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b9d8a428-c5fb-408c-9165-739eadbbecc7 · outbound

This paper cites A Study on the Calibration of In-context Learning.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges A Study on the Calibration of In-context Learning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:26.569651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2ca588f-56f0-427b-8268-949e3b56886d · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:26.745696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f02e4824-305b-4dea-99fd-dd0ce667b963 · outbound

This paper cites DeepSeek-V3 Technical Report.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges DeepSeek-V3 Technical Report

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:26.830335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 56fa3777-dbde-4e39-ad52-6e6dc5d1a38d · inbound

Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges cites this paper.

Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges

Reference 69

Resolution: malformed identifier; arxiv_id, observed 2026-05-09T06:50:41.894814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 31fa660b-5945-4c2b-89dc-8dbf858f4d75 · inbound

Tools as Continuous Flow for Evolving Agentic Reasoning cites this paper.

Tools as Continuous Flow for Evolving Agentic Reasoning Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.731002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.