Paper Citation Record · LEDGER

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond

As of 7 August 2026, this Paper Citation Record lists 84 of 84 outbound references and 2 inbound Pith citation observations for arXiv:2502.09897.

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

pith.paper-citation-record.v1
2502.09897 v1

Coverage vector

measured 84 of 84 reference resolution

Typed states for the displayed outbound observations.

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measured 86 of 86 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T07:04:51.970049Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T14:28:31.384103Z

Reference resolution

84 of 84 outbound references displayed

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External citation measurements

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Outbound references

Observation f1f24bcb-54cf-4fa9-8986-74144a3f9092 · outbound

This paper cites an unresolved cited work.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unresolved cited work

Reference 1

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unresolved cited work

Reference 2

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This paper cites Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry

Reference 3

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This paper cites G., Muhoberac, M., Randolph, C.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond G., Muhoberac, M., Randolph, C

Reference 4

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unresolved cited work

Reference 5

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This paper cites A., Kozlov, K.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond A., Kozlov, K

Reference 6

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Observation d8f2ee62-3f92-45ed-bb64-4629593b850a · outbound

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

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond On the Opportunities and Risks of Foundation Models

Reference 7

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This paper cites MassSpecGym: A benchmark for the discovery and identification of molecules.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond MassSpecGym: A benchmark for the discovery and identification of molecules

Reference 8

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This paper cites Interleaved Scene Graphs for Interleaved Text-and-Image Generation Assessment.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Interleaved Scene Graphs for Interleaved Text-and-Image Generation Assessment

Reference 9

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This paper cites GUI-World: A Video Benchmark and Dataset for Multimodal GUI-oriented Understanding.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond GUI-World: A Video Benchmark and Dataset for Multimodal GUI-oriented Understanding

Reference 10

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Observation 6a34acb4-4812-46db-a1b7-3a18a447f5ba · outbound

This paper cites Uncertainty-aware yield prediction with multimodal molec- ular features.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Uncertainty-aware yield prediction with multimodal molec- ular features

Reference 11

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Observation dc41afd8-0846-49b3-a2fa-2514e714f7ce · outbound

This paper cites Unveiling the power of language models in chemical research question answering.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unveiling the power of language models in chemical research question answering

Reference 12

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond V ., Gelin, M

Reference 13

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond C., Sievers, C

Reference 14

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This paper cites W ., Jin, W ., Rogers, L., Jamison, T.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond W ., Jin, W ., Rogers, L., Jamison, T

Reference 15

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Observation fd1bf59f-a8f3-4dd7-8cde-83f96116e1d8 · outbound

This paper cites Pure isotropic proton nmr spectra in solids using deep learning.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Pure isotropic proton nmr spectra in solids using deep learning

Reference 16

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This paper cites Spectroscopy-guided deep learning predicts solid–liquid surface adsorbate properties in unseen solvents.Journal of the American Chem- ical Society, 146(1):811–823, 2023.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Spectroscopy-guided deep learning predicts solid–liquid surface adsorbate properties in unseen solvents.Journal of the American Chem- ical Society, 146(1):811–823, 2023

Reference 17

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This paper cites A., Petras, Daniel ans Ger- wick, W.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond A., Petras, Daniel ans Ger- wick, W

Reference 18

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond E., Gibbons, F

Reference 19

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond A., North, N

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond K.-Y., Beck, A., Alzarieni, K

Reference 21

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond A., Rajasekar, A

Reference 22

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond A., Bößl, F ., and Wilhelm, M

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Honestllm: Toward an honest and helpful large language model

Reference 24

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This paper cites Machine learning molecular dynamics for the simulation of infrared spectra.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Machine learning molecular dynamics for the simulation of infrared spectra

Reference 25

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond T ., and Müller, K.-R

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond A., Packer, M

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Prefix-tree decoding for predicting mass spectra from molecules

Reference 28

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond S., Gallegos, L

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond V ., Wiest, O., and Zhang, X

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond What can large language models do in chemistry? a comprehensive benchmark on eight tasks

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Graph-based Molecular Representation Learning

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond S., Rotskoff, G

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond TrustGPT: A Benchmark for Trustworthy and Responsible Large Language Models

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond TrustLLM: Trustworthiness in Large Language Models

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Jailbreaking Large Language Models Through Alignment Vulnerabilities in Out-of-Distribution Settings

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Social Science Meets LLMs: How Reliable Are Large Language Models in Social Simulations?

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Breaking focus: Contex- tual distraction curse in large language models

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond S., Woroch, C

Reference 43

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond J., Madotto, A., and Fung, P

Reference 44

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This paper cites Deep imitation learning for molecular inverse problems.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Deep imitation learning for molecular inverse problems

Reference 45

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond and Kuhn, S

Reference 46

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This paper cites Predictive modeling of nmr chemical shifts without using atomic- level annotations.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Predictive modeling of nmr chemical shifts without using atomic- level annotations

Reference 47

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond W ., Zhang, C., Reher, R., Wang, M., Alexander, K

Reference 48

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This paper cites Neural message passing for nmr chemical shift prediction.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Neural message passing for nmr chemical shift prediction

Reference 49

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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unresolved cited work

Reference 50

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This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 51

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Observation de2d85f0-8590-4282-a7c4-aaf51ad66b0a · outbound

This paper cites A machine learning protocol for revealing ion transport mechanisms from dynamic nmr shifts in paramagnetic battery materials.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond A machine learning protocol for revealing ion transport mechanisms from dynamic nmr shifts in paramagnetic battery materials

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:13.219162Z

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 24dd61b1-86fa-4196-ab82-e4b34af272bd · outbound

This paper cites Deep learning-assisted spectrum-structure correlation: State-of-the-art and perspectives.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Deep learning-assisted spectrum-structure correlation: State-of-the-art and perspectives

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:13.202705Z

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 66dbb488-ba26-4269-9cc1-e74beef07218 · outbound

This paper cites an unresolved cited work.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T20:11:13.186649Z

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 81bf86ad-7bd5-451f-b809-c1b0bb7db102 · outbound

This paper cites Coculture of two developmental stages of a marine-derived aspergillus alliaceus results in the production of the cytotoxic bianthrone allianthrone a.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Coculture of two developmental stages of a marine-derived aspergillus alliaceus results in the production of the cytotoxic bianthrone allianthrone a

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:13.170536Z

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 5edd327b-1738-43a1-957a-4f5e43f4af23 · outbound

This paper cites D., Joshi, R.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond D., Joshi, R

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:13.155125Z

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 fa470d36-f46f-4554-9fa4-e946405bbaac · outbound

This paper cites an unresolved cited work.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-07T20:11:13.139075Z

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 dedc1702-0501-4c79-a5f9-62f58fec7e12 · outbound

This paper cites Efficiently predicting high resolution mass spectra with graph neural networks.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Efficiently predicting high resolution mass spectra with graph neural networks

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-07T20:11:12.535701Z

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 61f54376-7e9f-4373-aff5-c427adead8e3 · outbound

This paper cites An Introduction to Convolutional Neural Networks.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond An Introduction to Convolutional Neural Networks

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T20:11:12.104999Z

Source-reported events for the cited work

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Observation b1e19cb2-e7d7-47f6-ad4d-03a3d5cc6b57 · outbound

This paper cites Mass spectra prediction with structural motif-based graph neural networks.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Mass spectra prediction with structural motif-based graph neural networks

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:13.124489Z

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 d3dd4566-0c69-43db-a4af-da0eac8a3b5a · outbound

This paper cites an unresolved cited work.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-07T20:11:13.109861Z

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 0fc49007-04fe-4972-943e-41c154c53917 · outbound

This paper cites and Paliwal, K.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond and Paliwal, K

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:13.095666Z

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 590cfe41-713d-48f5-a699-efb878ee5141 · outbound

This paper cites Equivariant message passing for the prediction of tensorial properties and molecular spectra.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Equivariant message passing for the prediction of tensorial properties and molecular spectra

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:13.081177Z

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 c90d0828-e8ff-45ca-99de-bfae80012e05 · outbound

This paper cites Graph neural networks for learning molecular excitation spectra.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Graph neural networks for learning molecular excitation spectra

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:13.065925Z

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 64a7d8b2-5961-4f43-b8d9-c7655be73376 · outbound

This paper cites an unresolved cited work.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-07T20:11:13.050377Z

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 880cba28-50dd-4419-ab3b-cf03d3a6f784 · outbound

This paper cites an unresolved cited work.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-07T20:11:13.034832Z

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 106fafe3-6f58-4666-beb2-57326bc81bab · outbound

This paper cites an unresolved cited work.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-07T20:11:13.019069Z

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 cf5414fc-5914-4cbc-a792-62ebc643ea7a · outbound

This paper cites Automation and machine learning augmented by large language models in a catalysis study.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Automation and machine learning augmented by large language models in a catalysis study

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:13.003105Z

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 3ba94cc5-8c75-42fe-8081-4378c49ab517 · outbound

This paper cites Cross-modal retrieval between 13c nmr spectra and structures based on focused libraries.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Cross-modal retrieval between 13c nmr spectra and structures based on focused libraries

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:12.986573Z

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 55c52d80-2f5c-4621-be2c-37e72214273f · outbound

This paper cites Large language models for data annotation and synthesis: A survey.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Large language models for data annotation and synthesis: A survey

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T20:11:12.154784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5d08212-57f8-460c-b6e2-e0d8aa7f5772 · outbound

This paper cites Enhancing chemical reac- tion monitoring with a deep learning model for nmr spectra image matching to target compounds.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Enhancing chemical reac- tion monitoring with a deep learning model for nmr spectra image matching to target compounds

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:12.971318Z

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 f952cff7-3907-4a36-9063-94bd6aa9c653 · outbound

This paper cites A., Karlsson, N.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond A., Karlsson, N

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:12.956119Z

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 61f40937-bfef-4029-8f4d-b231c6908f31 · outbound

This paper cites Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data

Reference 73

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T20:11:12.496104Z

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 30211657-6f46-4d95-b55b-bccfc9ca1529 · outbound

This paper cites N., Belanger, D., Adams, R.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond N., Belanger, D., Adams, R

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:12.940793Z

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 f9fc8d7b-8021-459d-9eeb-24c1e325fcb8 · outbound

This paper cites Unigen: A unified framework for textual dataset generation using large language models.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unigen: A unified framework for textual dataset generation using large language models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T20:11:12.181119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3be7d7ca-b7e8-4752-a7bc-46d21865eb08 · outbound

This paper cites T ., Athersuch, T ., Xiang, Y., Liu, Z., Jiménez, B., and Ebbels, T.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond T ., Athersuch, T ., Xiang, Y., Liu, Z., Jiménez, B., and Ebbels, T

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:12.924933Z

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 42ffcb3b-40d3-4c52-9ba9-10b739d41955 · outbound

This paper cites Conditional molec- ular generation net enables automated structure elucidation based on 13c nmr spectra and prior knowledge.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Conditional molec- ular generation net enables automated structure elucidation based on 13c nmr spectra and prior knowledge

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:12.907382Z

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 20474bce-e326-4a8a-a576-30997a4599a4 · outbound

This paper cites D., Zhang, G., Mukamel, S., and Jiang, J.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond D., Zhang, G., Mukamel, S., and Jiang, J

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:12.892014Z

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 4e43d1b0-7408-48a8-b131-cc3a4b0fd5d7 · outbound

This paper cites De novo mass spectrometry peptide sequenc- ing with a transformer model.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond De novo mass spectrometry peptide sequenc- ing with a transformer model

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:12.875819Z

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 b6a34516-b4a2-4315-b4c8-8f187e890a3c · outbound

This paper cites MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-08-07T20:11:12.375219Z

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 0f98c21b-f3b8-44c0-a32e-28e1bb7002c6 · outbound

This paper cites Cbmaff-net: An intelligent nmr-based nontargeted screening method for new psychoactive substances.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Cbmaff-net: An intelligent nmr-based nontargeted screening method for new psychoactive substances

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T20:11:12.860420Z

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 22615d24-eb66-460b-84b8-c5f7035b1df6 · outbound

This paper cites V ., and Zhang, X.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond V ., and Zhang, X

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T20:11:12.215586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 556c8ccb-9674-4548-9797-60d91125df42 · outbound

This paper cites Using Graph Neural Networks for Mass Spectrometry Prediction.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Using Graph Neural Networks for Mass Spectrometry Prediction

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T20:11:12.219996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6f0a8f4-09bb-44e6-b2de-41cfcc0cb8df · outbound

This paper cites an unresolved cited work.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-07T20:11:12.845118Z

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.

Pith citing papers

Observation 1d6312bd-f23b-4e1b-a18e-89e4417cdf38 · inbound

SpecX: A Large-Scale Benchmark for Multi-Modal Spectroscopy and Cross-Paradigm Evaluation cites this paper.

SpecX: A Large-Scale Benchmark for Multi-Modal Spectroscopy and Cross-Paradigm Evaluation Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:19:13.904782Z

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 33b470a7-947b-4e9a-b89b-0135ba7312e3 · inbound

Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agents cites this paper.

Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agents Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:28:31.385421Z

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.