Paper Citation Record · LEDGER

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges

As of 12 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 1 inbound Pith citation observation for arXiv:2412.11427.

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

pith.paper-citation-record.v1
2412.11427 v2

Coverage vector

measured 78 of 78 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:00:20.226180Z

measured 79 of 79 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-23T04:30:38.804702Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T04:32:33.210774Z

Reference resolution

78 of 78 outbound references displayed

verified exact1
verified fuzzy12
unresolved65
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 80223f97-2f6d-438e-a46a-538dab519268 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges , " * write output.state after.block = add.period write newline

Reference 1

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Source-reported events for the cited work

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Observation 475af7c9-fe99-4d2c-8375-e3d138d7adac · outbound

This paper cites write newline.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges write newline

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9403b52-daf7-407e-89cd-14d131e5c9ad · outbound

This paper cites N.; Urban, N.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges N.; Urban, N

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:00:21.477610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9e79a849-dfdf-4ccc-83eb-3ddcdd8416b8 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:00:21.458641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7c63387e-0dff-40c7-bd06-807659b2aaf0 · outbound

This paper cites M.; Wu, Y.; and Krenn, M.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges M.; Wu, Y.; and Krenn, M

Reference 5

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Source-reported events for the cited work

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Observation 06cd4391-7b3c-449c-bbb8-844a55b701cb · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:00:21.442915Z

Source-reported events for the cited work

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Observation e6ad23fc-be91-4e2d-ae8f-ba765c09f4cf · outbound

This paper cites SciBERT: A Pretrained Language Model for Scientific Text.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges SciBERT: A Pretrained Language Model for Scientific Text

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T15:00:19.891811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55a91cba-c220-47d4-92dd-6a5343a3ceef · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:00:21.427502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e58a4b56-e06b-4879-97ef-a60176db5d74 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:00:21.411897Z

Source-reported events for the cited work

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Observation acbab42e-54e4-45fe-a2c5-53acdc396d04 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 10

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Source-reported events for the cited work

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Observation 2936fca9-36a3-4352-82e5-01742010aa24 · outbound

This paper cites A.; MacKnight, R.; Kline, B.; and Gomes, G.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges A.; MacKnight, R.; Kline, B.; and Gomes, G

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T15:00:19.911994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96d3fa19-bc3c-468d-afa3-4a3a702c3ca7 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges On the Opportunities and Risks of Foundation Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T15:00:19.916805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26f06797-6766-43ed-a875-5dccb5203577 · outbound

This paper cites Language Models are Few-Shot Learners.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Language Models are Few-Shot Learners

Reference 13

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Source-reported events for the cited work

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Observation f6535761-b996-476e-a158-e42e5634655b · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T15:00:19.926500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b54cfff6-5fa5-4489-a1d5-85a1255b6f33 · outbound

This paper cites W.; Charton, F.; Nolte, N.; Wilhelm, M.; Cranmer, K.; and Dixon, L.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges W.; Charton, F.; Nolte, N.; Wilhelm, M.; Cranmer, K.; and Dixon, L

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:00:21.369648Z

Source-reported events for the cited work

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Observation 045be212-6ceb-444a-80a2-e1ac7a85c0ec · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:00:21.353512Z

Source-reported events for the cited work

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Observation 27a7353d-49aa-404c-9895-34c9dec1ffa7 · outbound

This paper cites Quantifying Memorization Across Neural Language Models.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Quantifying Memorization Across Neural Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T15:00:19.940406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c2f2881-febb-4545-946c-749ac797daf8 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08b18b27-d986-46e3-a7ce-972f0cbc74f1 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:00:21.327401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 22f63e89-dea8-48cd-bf22-77eb723c0e21 · outbound

This paper cites Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:00:20.543947Z

Source-reported events for the cited work

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Observation ebaa6f5a-d24b-45cb-88d3-d9c4bf0feb11 · outbound

This paper cites R.; Goncalves, J.; Clarkson, K.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges R.; Goncalves, J.; Clarkson, K

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:00:21.312467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5fc96bb7-40cb-426a-9d82-fe05eca25821 · outbound

This paper cites Lagrangian Neural Networks.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Lagrangian Neural Networks

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b865a590-b7a5-4de7-bef1-20c28e5629a1 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 362bf5a2-6581-4ff8-b979-55fcfbae6401 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 24

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Source-reported events for the cited work

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Observation 012a9eaa-bd15-401c-ad66-d60ca3296919 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:00:21.265731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 670787f7-f471-4ec0-93c0-4323363fbbbb · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:00:21.251071Z

Source-reported events for the cited work

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Observation 24420c58-eed2-4069-a2d1-87ad593bceb8 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:00:21.235594Z

Source-reported events for the cited work

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Observation 5bf46d16-0201-48ee-9e0d-99b2284285a0 · outbound

This paper cites d.; and Lamb, L.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges d.; and Lamb, L

Reference 28

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Source-reported events for the cited work

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Observation 535ea426-63a2-4a65-8f89-a55c4a2883df · outbound

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

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence

Reference 29

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Observation 9a1885fe-f0e3-4699-991f-61a0a0e9d496 · outbound

This paper cites SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning

Reference 30

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Source-reported events for the cited work

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Observation d23c14df-55b4-442b-8f49-09513fd551ba · outbound

This paper cites xVal: A Continuous Numerical Tokenization for Scientific Language Models.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges xVal: A Continuous Numerical Tokenization for Scientific Language Models

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ec6b40b-6927-441e-a284-5cff453d2201 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 32

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Source-reported events for the cited work

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Observation 2e264bf0-8c97-4c16-9798-39bc71059358 · outbound

This paper cites Proof Artifact Co-training for Theorem Proving with Language Models.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Proof Artifact Co-training for Theorem Proving with Language Models

Reference 33

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Observation 651727af-b25f-4148-97ca-f2ec449e4bf6 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Measuring Mathematical Problem Solving With the MATH Dataset

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e88312c-281b-43cb-972d-f1a728a3a5bc · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 35

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Source-reported events for the cited work

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Observation 5575fa86-5386-4459-832f-c4d3b945cdb8 · outbound

This paper cites CRISPR-GPT for Agentic Automation of Gene-editing Experiments.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges CRISPR-GPT for Agentic Automation of Gene-editing Experiments

Reference 36

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Observation a66356b2-c261-4118-82f7-9ebe7c4cb4e5 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 37

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Source-reported events for the cited work

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Observation df8ae83e-7fbb-40ee-b092-9d9b1eac6cc4 · outbound

This paper cites Q.; Welleck, S.; Zhou, J.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Q.; Welleck, S.; Zhou, J

Reference 38

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Source-reported events for the cited work

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Observation 46fd1aa9-1181-4222-9418-149875a70642 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 39

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Observation 50d550a8-9f21-4251-bdca-c1ee550b257b · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 40

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Source-reported events for the cited work

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Observation 3e0d0d21-1d5c-4979-8a36-b3f4519b2f30 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

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This paper cites S.; Yang, J.; Glatt, R.; Santiago, C.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges S.; Yang, J.; Glatt, R.; Santiago, C

Reference 42

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Source-reported events for the cited work

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Observation cf04a520-abdb-48fd-8c75-fdb101b5e519 · outbound

This paper cites H.; and Kang, J.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges H.; and Kang, J

Reference 43

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Source-reported events for the cited work

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Observation 8613b4f4-bd91-4607-beaf-4411a157566b · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 44

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Source-reported events for the cited work

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Observation 2953ea7c-4621-4337-8c0a-7cd8f72c83e8 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges KAN: Kolmogorov-Arnold Networks

Reference 45

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Source-reported events for the cited work

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Observation b8acb730-11f3-4b8d-b317-64c97b485270 · outbound

This paper cites The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Reference 46

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Source-reported events for the cited work

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Observation ed5898da-4e92-47be-b71c-86abfab24cff · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 47

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f084b23-3933-4887-ba6e-5b4f89438864 · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 48

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Source-reported events for the cited work

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Observation 8984f7cb-2916-4073-9cab-f41c3b242cbf · outbound

This paper cites Bran, A.; Cox, S.; Schilter, O.; Baldassari, C.; White, A.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Bran, A.; Cox, S.; Schilter, O.; Baldassari, C.; White, A

Reference 49

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Source-reported events for the cited work

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Observation 9ec18ad1-b8d1-4530-b9c2-53f3f6dc89dc · outbound

This paper cites B.; Rus, D.; Gan, C.; and Matusik, W.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges B.; Rus, D.; Gan, C.; and Matusik, W

Reference 50

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Source-reported events for the cited work

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Observation 40f43047-cc12-412d-95d2-6c267dbbca4f · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:00:21.016061Z

Source-reported events for the cited work

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Observation 12749f1e-5d57-472f-87a9-412b37c4d41e · outbound

This paper cites an unresolved cited work.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 52

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Source-reported events for the cited work

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Observation 33fad84e-0073-4523-b847-52cda94b33ab · outbound

This paper cites K.; and Farimani, A.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges K.; and Farimani, A

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:00:20.984938Z

Source-reported events for the cited work

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Observation cae96fd8-a160-4cdf-866c-c3170afc01ea · outbound

This paper cites S.; Aykol, M.; Cheon, G.; and Cubuk, E.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges S.; Aykol, M.; Cheon, G.; and Cubuk, E

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:00:20.969539Z

Source-reported events for the cited work

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Observation a261bb43-95c4-4873-b325-dd84a52ca500 · outbound

This paper cites Are LLMs Ready for Real-World Materials Discovery?.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Are LLMs Ready for Real-World Materials Discovery?

Reference 55

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design

Reference 56

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Generative Language Modeling for Automated Theorem Proving

Reference 57

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges O.; Pitera, J

Reference 58

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges V.; and Katritch, V

Reference 59

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Reference 60

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges H.; Preuss, M.; and Waller, M

Reference 61

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Reference 62

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 63

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This paper cites LLM-SR: Scientific Equation Discovery via Programming with Large Language Models.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

Reference 64

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers

Reference 65

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges S.; Wei, J.; Chung, H

Reference 66

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Reference 67

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Reference 68

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Reference 69

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Reference 70

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 71

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 72

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This paper cites SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models

Reference 73

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This paper cites Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks

Reference 74

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 75

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This paper cites J.; and Anandkumar, A.

Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges J.; and Anandkumar, A

Reference 76

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models

Reference 77

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Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:00:20.741396Z

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Pith citing papers

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Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges

Reference 160

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