Paper Citation Record · LEDGER

Evaluating Large Language Models in Scientific Discovery

As of 5 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 19 inbound Pith citation observations for arXiv:2512.15567.

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

pith.paper-citation-record.v1
2512.15567 v2

Coverage vector

measured 87 of 87 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-16T21:47:09.588941Z

measured 106 of 106 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T14:21:28.776336Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T23:49:02.992717Z

Reference resolution

87 of 87 outbound references displayed

verified exact53
verified fuzzy14
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9f0640b1-528c-4d3d-83d2-ace7d6334936 · outbound

This paper cites Attention Is All You Need.

Evaluating Large Language Models in Scientific Discovery Attention Is All You Need

Reference 1

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Source-reported events for the cited work

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Observation dbc7f627-0a59-479e-9d19-ea2b35641718 · outbound

This paper cites Language Models are Few-Shot Learners.

Evaluating Large Language Models in Scientific Discovery Language Models are Few-Shot Learners

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.423251Z

Source-reported events for the cited work

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Observation afff3a85-eff5-47df-80c2-c0a6c721b2b3 · outbound

This paper cites Scaling Laws for Neural Language Models.

Evaluating Large Language Models in Scientific Discovery Scaling Laws for Neural Language Models

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.430755Z

Source-reported events for the cited work

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Observation 30cff53c-4f69-4892-8b5e-842d005e7555 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Evaluating Large Language Models in Scientific Discovery ReAct: Synergizing Reasoning and Acting in Language Models

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.433996Z

Source-reported events for the cited work

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Observation 0a7d8187-c1a2-4a04-81ef-2e55e789b84e · outbound

This paper cites an unresolved cited work.

Evaluating Large Language Models in Scientific Discovery Unresolved cited work

Reference 5

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.179219Z

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Observation f70da868-bce8-4aea-b8da-340e2689ea60 · outbound

This paper cites Szczypiński, Jean-François Ayme, Ehsan Simaei, Thomas Fellowes, Rob Clowes, Lyubomir Kotopanov, Caitlin E.

Evaluating Large Language Models in Scientific Discovery Szczypiński, Jean-François Ayme, Ehsan Simaei, Thomas Fellowes, Rob Clowes, Lyubomir Kotopanov, Caitlin E

Reference 6

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.176942Z

Source-reported events for the cited work

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Observation e603c3ba-77f9-4cc4-a0e7-f64ac86c14d9 · outbound

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Evaluating Large Language Models in Scientific Discovery Unresolved cited work

Reference 7

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Source-reported events for the cited work

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Observation c444f2e5-1d85-4d02-9f60-f6bdf5a7ab5a · outbound

This paper cites M., Schwaller, P., Ortega-Guerrero, A.

Evaluating Large Language Models in Scientific Discovery M., Schwaller, P., Ortega-Guerrero, A

Reference 8

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.181289Z

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Observation ee537345-f752-4b81-b1d6-3cbb2cd771b6 · outbound

This paper cites Large language models for scientific discovery in molecular property prediction.

Evaluating Large Language Models in Scientific Discovery Large language models for scientific discovery in molecular property prediction

Reference 9

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.185238Z

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Observation 8157938f-d834-4280-8055-3f6d762e87b7 · outbound

This paper cites an unresolved cited work.

Evaluating Large Language Models in Scientific Discovery Unresolved cited work

Reference 10

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.183304Z

Source-reported events for the cited work

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Observation 6f83cdde-1ec6-4981-815c-6916632c7119 · outbound

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

Evaluating Large Language Models in Scientific Discovery Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.415900Z

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Observation efcefe1b-1ada-4635-ae5a-6f9bd7fd68b0 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Evaluating Large Language Models in Scientific Discovery Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.427363Z

Source-reported events for the cited work

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Observation e596813c-3562-4a4c-a084-ee7faff8cbe0 · outbound

This paper cites OpenAI o1 System Card.

Evaluating Large Language Models in Scientific Discovery OpenAI o1 System Card

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.101539Z

Source-reported events for the cited work

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Observation c0243ecb-d07d-4dc2-ba2e-84de42be422e · outbound

This paper cites doi: 10.1038/s41586-025-09422-z.

Evaluating Large Language Models in Scientific Discovery doi: 10.1038/s41586-025-09422-z

Reference 15

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.054580Z

Source-reported events for the cited work

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Observation d7ad4896-b02d-4838-9b44-96f52eee144b · outbound

This paper cites Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q.

Evaluating Large Language Models in Scientific Discovery Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q

Reference 16

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.165587Z

Source-reported events for the cited work

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Observation 21f1490d-d25f-4580-9681-a2d834e8ef9e · outbound

This paper cites Optimizing generative ai by backpropagating language model feedback.

Evaluating Large Language Models in Scientific Discovery Optimizing generative ai by backpropagating language model feedback

Reference 17

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.157366Z

Source-reported events for the cited work

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Observation 1aea955e-5fcc-421d-91df-1f631e346c4e · outbound

This paper cites Augmenting large language models with chemistry tools.

Evaluating Large Language Models in Scientific Discovery Augmenting large language models with chemistry tools

Reference 18

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.064570Z

Source-reported events for the cited work

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Observation 5755c56e-43b1-4364-a810-bcbbb10d99e9 · outbound

This paper cites Autonomous chemical research with large language models.

Evaluating Large Language Models in Scientific Discovery Autonomous chemical research with large language models

Reference 19

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.087275Z

Source-reported events for the cited work

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Observation b9b766ca-b087-4907-b0eb-21f0987dcd2f · outbound

This paper cites Towards an AI co-scientist.

Evaluating Large Language Models in Scientific Discovery Towards an AI co-scientist

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.393084Z

Source-reported events for the cited work

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Observation a6853eaa-e856-4e54-9fae-36d304c724da · outbound

This paper cites The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search.

Evaluating Large Language Models in Scientific Discovery The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.389858Z

Source-reported events for the cited work

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Observation 1e2fe954-0c13-4eb5-9516-6d7def2bb618 · outbound

This paper cites The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies.Nature.

Evaluating Large Language Models in Scientific Discovery The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies.Nature

Reference 22

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.139295Z

Source-reported events for the cited work

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Observation a5d05575-9a18-4e68-8b60-70b0c1812ca4 · outbound

This paper cites an unresolved cited work.

Evaluating Large Language Models in Scientific Discovery Unresolved cited work

Reference 23

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.149082Z

Source-reported events for the cited work

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Observation 83c69c0a-5b55-4df2-8c2b-9d07af1bab00 · outbound

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Evaluating Large Language Models in Scientific Discovery Unresolved cited work

Reference 24

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.077398Z

Source-reported events for the cited work

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Observation d043dccf-8417-4b83-8917-3e4aa7469eaa · outbound

This paper cites Self-Driving Laboratories for Chemistry and Materials Science.Chemical Reviews.

Evaluating Large Language Models in Scientific Discovery Self-Driving Laboratories for Chemistry and Materials Science.Chemical Reviews

Reference 25

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.069283Z

Source-reported events for the cited work

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Observation 4dbfdc77-6178-4673-a072-62ec0212d1b2 · outbound

This paper cites author Kitchin, J.

Evaluating Large Language Models in Scientific Discovery author Kitchin, J

Reference 26

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.167862Z

Source-reported events for the cited work

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Observation 8183efdb-d861-421b-af41-32ab346a75d4 · outbound

This paper cites A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence.

Evaluating Large Language Models in Scientific Discovery A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.094503Z

Source-reported events for the cited work

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Observation ae2ce515-2ae1-4f8e-bccd-db46876d6b13 · outbound

This paper cites and Johnson, William A.

Evaluating Large Language Models in Scientific Discovery and Johnson, William A

Reference 28

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.081059Z

Source-reported events for the cited work

correction dated 2025-12-08. Source: crossref record 10.1038/s41551-025-01589-0->10.1038/s41551-025-01463-z:correction, observed 2026-07-11T03:07:47.241959+00:00. This notice travels one citation hop only.

Observation 4f033503-7f58-4dd1-b20d-b547dfdd5da7 · outbound

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Evaluating Large Language Models in Scientific Discovery Unresolved cited work

Reference 29

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.084003Z

Source-reported events for the cited work

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Observation 07f03eb2-7fce-4928-8c9f-85b38e985c9c · outbound

This paper cites Democratizing ai scientists using tooluniverse.

Evaluating Large Language Models in Scientific Discovery Democratizing ai scientists using tooluniverse

Reference 30

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Observation 6622025f-b967-4f82-9024-63fa96084adf · outbound

This paper cites (10) Jiang, G.; Luo, Q.

Evaluating Large Language Models in Scientific Discovery (10) Jiang, G.; Luo, Q

Reference 31

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.136456Z

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Observation ac5939bc-ad50-4f65-a51f-963cfec2128f · outbound

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Evaluating Large Language Models in Scientific Discovery Unresolved cited work

Reference 32

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Observation 71b16236-543e-4da2-ac7e-8952cb2bdbe9 · outbound

This paper cites Kosmos: An AI Scientist for Autonomous Discovery.

Evaluating Large Language Models in Scientific Discovery Kosmos: An AI Scientist for Autonomous Discovery

Reference 33

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Observation 2db10bce-473a-4909-bfd8-7e9e01c06712 · outbound

This paper cites Carter, Xin Zhou, Matthew Wheeler, Jonathan A.

Evaluating Large Language Models in Scientific Discovery Carter, Xin Zhou, Matthew Wheeler, Jonathan A

Reference 34

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.160536Z

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Observation 4665ccbb-6984-43f9-a616-04b528763551 · outbound

This paper cites Physics Supernova: AI Agent Matches Elite Gold Medalists at IPhO 2025.

Evaluating Large Language Models in Scientific Discovery Physics Supernova: AI Agent Matches Elite Gold Medalists at IPhO 2025

Reference 35

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Observation 95a7e49d-c68f-48b6-b3ff-b27d0ccb3e79 · outbound

This paper cites Sciarena: An open evaluation platform for foundation models in scientific literature tasks.

Evaluating Large Language Models in Scientific Discovery Sciarena: An open evaluation platform for foundation models in scientific literature tasks

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:48:34.374071Z

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Observation 8a894e2a-8a35-4514-b7e3-10727f84c871 · outbound

This paper cites title Swe-bench verified.

Evaluating Large Language Models in Scientific Discovery title Swe-bench verified

Reference 37

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Observation 5a14fbfa-cee3-4293-8761-74db70cfd251 · outbound

This paper cites MathArena: Evaluating LLMs on Uncontaminated Math Competitions.

Evaluating Large Language Models in Scientific Discovery MathArena: Evaluating LLMs on Uncontaminated Math Competitions

Reference 38

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Observation dfe06954-e8e9-47ef-bd0a-e0287188fce7 · outbound

This paper cites From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline.

Evaluating Large Language Models in Scientific Discovery From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:48:34.399906Z

Source-reported events for the cited work

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Observation de5bfb54-62c9-4c9f-aa94-2c5bb8c53223 · outbound

This paper cites $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains.

Evaluating Large Language Models in Scientific Discovery $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.128012Z

Source-reported events for the cited work

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Observation d6f5e6cc-05b8-4fe4-958a-dd25b98dc564 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

Evaluating Large Language Models in Scientific Discovery GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.112821Z

Source-reported events for the cited work

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Observation cea9f1d9-0d6e-4e02-a93c-bde834d0e606 · outbound

This paper cites Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering.

Evaluating Large Language Models in Scientific Discovery Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:48:34.106503Z

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Observation 57ed24f4-c6c1-4ae4-899f-03564cc70e2f · outbound

This paper cites MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI.

Evaluating Large Language Models in Scientific Discovery MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.143394Z

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Observation caa43568-44e2-46c8-a1c4-f85b36185768 · outbound

This paper cites Humanity's Last Exam.

Evaluating Large Language Models in Scientific Discovery Humanity's Last Exam

Reference 44

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T21:48:34.124182Z

Source-reported events for the cited work

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Observation 6d2225c7-b4fc-4369-a909-f7fe10b3249c · outbound

This paper cites Intell.1, 14, DOI: 10.1038/s44387-025-00019-5 (2025).

Evaluating Large Language Models in Scientific Discovery Intell.1, 14, DOI: 10.1038/s44387-025-00019-5 (2025)

Reference 45

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.146674Z

Source-reported events for the cited work

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Observation eb9bae1d-f52c-4578-90fd-7daf419b1f52 · outbound

This paper cites Elbeheiry, María Victoria Gil, Christina Glaubitz, Maximilian Greiner, Caroline T.

Evaluating Large Language Models in Scientific Discovery Elbeheiry, María Victoria Gil, Christina Glaubitz, Maximilian Greiner, Caroline T

Reference 46

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.172602Z

Source-reported events for the cited work

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Observation 836537d0-3232-4b45-9326-727cb0c321c3 · outbound

This paper cites Toward Scientific Reasoning in LLMs: Training from Expert Discussions via Reinforcement Learning.

Evaluating Large Language Models in Scientific Discovery Toward Scientific Reasoning in LLMs: Training from Expert Discussions via Reinforcement Learning

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:48:34.403311Z

Source-reported events for the cited work

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Observation 1c4242b2-3efd-4076-8f64-254f6546596c · outbound

This paper cites Alampara, et al.

Evaluating Large Language Models in Scientific Discovery Alampara, et al

Reference 48

Resolution: verified exact; doi, observed 2026-05-16T21:48:34.151610Z

Source-reported events for the cited work

correction dated 2025-08-21. Source: crossref record 10.1038/s43588-025-00869-8->10.1038/s43588-025-00836-3:correction, observed 2026-07-11T03:08:42.754763+00:00. This notice travels one citation hop only.

Observation 41a47057-3d46-43e0-b4b9-53bb251861a3 · outbound

This paper cites gpt-oss-120b & gpt-oss-20b Model Card.

Evaluating Large Language Models in Scientific Discovery gpt-oss-120b & gpt-oss-20b Model Card

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.409689Z

Source-reported events for the cited work

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Observation 737a01ef-60d1-4c6a-a5a2-ed90de0e7c8c · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

Evaluating Large Language Models in Scientific Discovery Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-16T21:48:34.406506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4be5f1ed-30b2-4ee9-a173-ced6d49e7de1 · outbound

This paper cites Reasoning with Sampling: Your Base Model is Smarter Than You Think.

Evaluating Large Language Models in Scientific Discovery Reasoning with Sampling: Your Base Model is Smarter Than You Think

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-17T03:16:05.617665Z

Source-reported events for the cited work

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Observation 2e6f973f-fc99-4d59-a193-93b0630dcc3c · outbound

This paper cites Stress-testing model specs reveals character differences among language models, 2025a.

Evaluating Large Language Models in Scientific Discovery Stress-testing model specs reveals character differences among language models, 2025a

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:48:34.383323Z

Source-reported events for the cited work

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Observation 82f3c756-502a-4990-92cb-1eba1dba1955 · outbound

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Evaluating Large Language Models in Scientific Discovery Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

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Evaluating Large Language Models in Scientific Discovery Evaluating Frontier Models for Dangerous Capabilities,

Reference 55

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Reference 56

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Evaluating Large Language Models in Scientific Discovery author Li, C

Reference 57

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Reference 58

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Reference 59

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Reference 60

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Reference 61

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Reference 62

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Reference 63

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Reference 64

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Reference 65

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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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Reference 71

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Reference 72

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Observation 12144205-038a-4174-a5c7-5a3d3d3b0598 · outbound

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Evaluating Large Language Models in Scientific Discovery title Pistachio (january 2024)

Reference 73

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Observation fa129da7-8894-46e4-81fa-5510291f49ed · outbound

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Reference 74

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Reference 75

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Reference 76

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Reference 77

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Reference 78

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Reference 79

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Evaluating Large Language Models in Scientific Discovery author Zhong, P

Reference 80

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Evaluating Large Language Models in Scientific Discovery author Fu, X

Reference 81

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Reference 82

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Reference 83

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Reference 84

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Reference 85

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Reference 86

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Reference 87

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Reference 89

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Reference 44

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Reference 39

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Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence? Evaluating Large Language Models in Scientific Discovery

Reference 70

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Observation f04ef476-8bc0-4a0f-a835-959f2171633b · inbound

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Observation 16b80c3a-0704-43ac-ae4c-2d3b27c49580 · inbound

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Observation b0e6370f-d74a-4949-9890-b3c19e1a0373 · inbound

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Reference 53

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Observation cfc64460-1382-4d82-85fc-a380c1ef1072 · inbound

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SFBench: The SciFy Scientific Feasibility Benchmark Evaluating Large Language Models in Scientific Discovery

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Observation 141afa4a-8756-46a1-bb99-7ba67e15d60f · inbound

Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs cites this paper.

Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs Evaluating Large Language Models in Scientific Discovery

Reference 88

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Observation 681e9fe6-0b58-4eca-869d-0cdd4b204582 · inbound

NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem cites this paper.

NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem Evaluating Large Language Models in Scientific Discovery

Reference 14

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Observation cd74bc2b-cdb4-4fa4-aea9-1793100af5de · inbound

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Reference 987

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