Paper Citation Record · LEDGER

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials

As of 8 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2508.06591.

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

pith.paper-citation-record.v1
2508.06591 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:56:20.064068Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

verified exact16
verified fuzzy5
unresolved24
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9558919d-5ef1-4409-865e-eadbfddd2734 · outbound

This paper cites , author Luu, R.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials , author Luu, R

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:56:25.440227Z

Source-reported events for the cited work

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Observation c8fbd863-3275-45cd-a98b-fa2ddea684b4 · outbound

This paper cites Can LLMs' Tuning Methods Work in Medical Multimodal Domain?.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Can LLMs' Tuning Methods Work in Medical Multimodal Domain?

Reference 2

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Source-reported events for the cited work

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Observation 18987828-7bd9-44b7-afd3-796f97f2b825 · outbound

This paper cites an unresolved cited work.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-05T22:56:25.255838Z

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Observation 72e249ce-89fc-4186-a627-554ee847855d · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Code Llama: Open Foundation Models for Code

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T22:56:17.457160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8fe9f23-13c5-41c8-88fc-5ffb940b5506 · outbound

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

Reference 5

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Observation 8afde955-c54d-4107-8568-3124c93d04a9 · outbound

This paper cites & author Burgert, I.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials & author Burgert, I

Reference 6

Resolution: verified exact; doi, observed 2026-08-05T22:56:21.189221Z

Source-reported events for the cited work

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Observation 9a668055-7d70-4610-83eb-728c71ba36ad · outbound

This paper cites an unresolved cited work.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

Reference 7

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Observation 19b8093b-22ab-409e-99e0-a5090ae93948 · outbound

This paper cites , author Mail, M.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials , author Mail, M

Reference 8

Resolution: verified exact; doi, observed 2026-08-05T22:56:21.066546Z

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Observation ea93fbe2-5f66-4466-86ca-6c6a1264dbec · outbound

This paper cites an unresolved cited work.

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Reference 9

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Observation 27b7719f-5696-42b0-a95d-b0b22242bba4 · outbound

This paper cites an unresolved cited work.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

Reference 10

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Observation 81ebc0e1-2db5-4922-84fc-638bc4f39a9f · outbound

This paper cites & author Fratzl, P.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials & author Fratzl, P

Reference 11

Resolution: verified exact; raw_fallback, observed 2026-08-05T22:56:22.230121Z

Source-reported events for the cited work

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Observation 6b3c494a-d2ed-4eb2-99b5-643207bd9495 · outbound

This paper cites an unresolved cited work.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

Reference 12

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Observation 6b89ff1e-2267-443d-a617-2fda325ef698 · outbound

This paper cites title In touch: plant responses to mechanical stimuli.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials title In touch: plant responses to mechanical stimuli

Reference 13

Resolution: verified exact; raw_fallback, observed 2026-08-05T22:56:22.063808Z

Source-reported events for the cited work

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Observation de95501d-f73c-45df-9554-db1e2ff835bb · outbound

This paper cites title The light eaters : the new science of plants ( year 2024 ).

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials title The light eaters : the new science of plants ( year 2024 )

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:56:24.274927Z

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Observation a2e78725-662f-4fae-80c4-f14e797b6429 · outbound

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Reference 15

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Observation f5bbba19-b145-4af7-9c18-fbc4474b4526 · outbound

This paper cites an unresolved cited work.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

Reference 16

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Observation b7bf892a-8ff1-4e31-aec2-76c460b56f93 · outbound

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

Reference 17

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Observation b4dc06ce-b107-4022-ae26-1308ba409779 · outbound

This paper cites , author Liu, M.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials , author Liu, M

Reference 18

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Source-reported events for the cited work

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Observation 8ad74a3e-983b-41ac-b0d8-400f0986daee · outbound

This paper cites an unresolved cited work.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

Reference 19

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Source-reported events for the cited work

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Observation c1f337b3-785a-4b44-a8f0-59d9c5b9f427 · outbound

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

Reference 20

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Observation 4a74ff21-b7b5-45c0-ab58-6feb68d9f185 · outbound

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

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials title A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 21

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials , author Drobnjak, A

Reference 22

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Source-reported events for the cited work

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Observation 7cbd4cc0-af34-4ee9-b024-575e77c61dc4 · outbound

This paper cites PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T22:56:18.807734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0ede9a6-72a8-4668-8da7-b377c26259ee · outbound

This paper cites In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4de8ebf8-3358-4e6f-a0f4-a62022083455 · outbound

This paper cites Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers

Reference 25

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Source-reported events for the cited work

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Observation 3ae93b24-9f0e-400d-aec1-1369e25ba778 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 26

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Observation 7eb1e7a6-c284-4f76-aa54-21a08ab17e01 · outbound

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Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48b270ea-f5c4-4882-910a-ee2326a826f3 · outbound

This paper cites MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

Reference 28

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Observation f5ccb9ed-f0b7-4806-a346-5dc0d53da2f9 · outbound

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:56:21.492690Z

Source-reported events for the cited work

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Observation e282586e-965e-4719-82d8-33480e6a540a · outbound

This paper cites & author Buehler, M.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials & author Buehler, M

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:56:23.818052Z

Source-reported events for the cited work

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Observation 6426c692-32a4-45cf-a004-1492b7fd59c3 · outbound

This paper cites , author Buehler, M.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials , author Buehler, M

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb6558e5-e74a-4bb8-a154-48417ee2073a · outbound

This paper cites AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T22:56:19.235826Z

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Unavailable: canonical work link unavailable.

Observation d1b036e3-1d68-493d-9a91-826ff4d734d8 · outbound

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T22:56:19.303105Z

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Observation dc64f3fb-322c-4ce4-ad8b-9a821d7efead · outbound

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Assessing and Understanding Creativity in Large Language Models

Reference 34

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Observation 345e5314-40ff-45b1-b482-c53add57eb0f · outbound

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Reference 35

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Observation 89dcfd86-e413-4bcd-9484-3bd5e19f76ce · outbound

This paper cites an unresolved cited work.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

Reference 37

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Observation bf532f20-f4cd-4620-8a58-e1f636ded1f7 · outbound

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

Reference 38

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This paper cites , author McKittrick, J.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials , author McKittrick, J

Reference 39

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Observation 5addf366-67b1-4225-869b-e12a38b3205d · outbound

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Unresolved cited work

Reference 40

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Observation f1d0bbb1-bbc4-4b47-96db-6791dd730d72 · outbound

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials GPT-4 Technical Report

Reference 41

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Holistic Evaluation of Language Models

Reference 42

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Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Can Large Language Models Be an Alternative to Human Evaluations?

Reference 43

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Observation e6ee4100-cff8-4116-ad67-63afce0e9c73 · outbound

This paper cites Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate

Reference 45

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Unavailable: canonical work link unavailable.

Pith citing papers

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