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 7 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.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:49:26.830335Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-11T01:30:19.859374Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T01:45:51.728965Z

Reference resolution

62 of 62 outbound references displayed

verified exact2
verified fuzzy27
unresolved32
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:33.697319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:33.498134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:33.311643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:33.113430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:32.886791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:32.700860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 18d6d9e4-12a1-4b72-a819-0a8490f797c4 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:32.501768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:32.300522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ad560513-4a6c-4b92-9af5-8b064249327a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:32.127432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:19.355888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5882c869-3dc4-4a8b-9842-26a9114c2950 · 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 12

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:49:31.961168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3b26080f-d737-4a4a-9cac-075ecdd8f64a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:31.792315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7e1d82df-8346-4791-a14d-4a01c60d2ea9 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:19.811133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e4732fb-78b7-4544-841f-a71cf67dd296 · outbound

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

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:19.970278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85806492-1d5f-4d4c-ae9b-e9df3d4732ed · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:20.092816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efc71f90-c60d-45c3-99a8-81215aa71ac3 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:20.239820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5134e77b-ed5b-4c5b-aec0-d17dc0a9b9d8 · outbound

This paper cites Language Models are Few-Shot Learners.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:20.405347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5dda0e2-fddd-419a-acc2-078ec3b24cbe · outbound

This paper cites Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:31.595544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4ab89589-d506-47c4-94cc-ef01adda4acb · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:20.706177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 981a7435-8627-40a6-bb7f-e1059fb4b624 · outbound

This paper cites Large Language Models for Robotics: Opportunities, Challenges, and Perspectives.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:20.851411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b811ba1b-f483-41ce-bec5-5eaca50d2461 · outbound

This paper cites The Rise and Potential of Large Language Model Based Agents: A Survey.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:20.966294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 532e4619-8257-4dc0-b5aa-368fdea44713 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:21.044088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3e13fc9-8ceb-4f7b-8cd4-49c0d2b33f0d · outbound

This paper cites Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:21.215658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a556ea8-4c84-4037-aafe-0edc359db1ca · outbound

This paper cites KoLA: Carefully Benchmarking World Knowledge of Large Language Models.

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

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:21.334055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66bca87e-c276-4f6c-901c-046408192b4e · outbound

This paper cites AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:21.448385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e1dab46-f9c5-4b27-beb3-4cf5d18e6bb8 · outbound

This paper cites Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:21.567604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 005f607b-93a4-4f93-aec0-35fa75c678f4 · outbound

This paper cites A survey on LLM-based multi-agent systems: workflow, infrastructure, and challenges,.

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,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:21.685345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ded1fee8-0bd2-4beb-871f-b51bcbded6ce · outbound

This paper cites Large Language Models are Zero-Shot Reasoners.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:21.819342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88a229f2-5bf7-46ed-a553-113521ac4b2e · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:22.000026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a88201e-00ca-43f3-b94b-e72ac483714b · outbound

This paper cites LLM4Rec: A Comprehensive Survey on the Integration of Large Language Models in Recommender Systems—Approaches, Applications and Challenges,.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:22.108732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 453dd425-01cc-44a5-a74b-5358aeec3eb5 · outbound

This paper cites Improving Language Understanding by Generative Pre-Training,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:31.441191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 61a478f4-d858-410f-9483-3946ddd31af5 · outbound

This paper cites The Claude 3 Model Family: Opus, Sonnet, Haiku,.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:31.260968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 991a1327-badc-4dc2-a9cd-98de7cde2ed4 · outbound

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

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:49:27.009383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2db429d8-60ec-4112-b3d0-79bb75b2aa1a · outbound

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

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T22:49:27.799616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4b0450f4-d54d-4da0-8dd3-1eedbf84b942 · outbound

This paper cites and Huang, K., 2025.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:31.076530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a0289f81-b72a-4618-a819-70076bf1aa78 · outbound

This paper cites A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:22.911853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c79e9a24-ed02-4647-94d9-4d798691f4d3 · outbound

This paper cites PubMed Central: The GenBank of the published literature.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:30.890612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9b2adc80-5eec-4e96-8fd1-d1abf99e140d · outbound

This paper cites and Li, K., 2019.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:30.708765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fe2f6dc4-266d-4add-a0ef-0ddf67128234 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:30.500460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ee38a785-6be0-48a5-8820-74167ffe2ee1 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:23.389504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f7557c1-9c72-41b1-9710-27353326a2ed · outbound

This paper cites Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:23.531540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da049385-9835-4fe2-912c-c123f14a058e · outbound

This paper cites A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models.

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

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:23.694775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17a93ab3-4729-4973-9cb1-b98fb9bc703a · outbound

This paper cites GPT-4o System Card.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:23.830393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcab14af-c35d-4aaf-af4b-574fa0d2b6d4 · outbound

This paper cites Available at: https://openai.com/index/gpt-4-1/ (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://openai.com/index/gpt-4-1/ (Accessed: 25 June 2025)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:30.326141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 22fe0186-561a-4967-99d6-90251305da7b · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:24.186978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eef60a3e-3585-4387-8498-3dc196228a9e · outbound

This paper cites and Jurgens, D., 2024, November.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:30.101351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

This paper cites and Sakr, M., 2024, March.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:29.809761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+00:00.