Paper Citation Record · LEDGER

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT

As of 12 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2507.02734.

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

pith.paper-citation-record.v1
2507.02734 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:29:51.349498Z

measured 27 of 27 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 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

27 of 27 outbound references displayed

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External citation measurements

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Outbound references

Observation 52f093be-79f3-495e-aa9e-18313748e9df · outbound

This paper cites J.; Reich, S.; Skeel, R.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT J.; Reich, S.; Skeel, R

Reference 1

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Observation b6c56826-55ac-4c94-be5e-481b341e626d · outbound

This paper cites Symplectic splitting methods for rigid body molecular dynamics.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Symplectic splitting methods for rigid body molecular dynamics

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:55.200785Z

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Observation b03888a2-735f-47a8-918e-6045c2f3bad7 · outbound

This paper cites D.; Singhal, N.; Pande, V.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT D.; Singhal, N.; Pande, V

Reference 3

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This paper cites C.; Roux, B.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT C.; Roux, B

Reference 4

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Observation 4513c3dc-da0a-4ae8-bbb6-98a80c3ada7d · outbound

This paper cites A variational approach to modeling slow processes in stochastic dynamical systems.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT A variational approach to modeling slow processes in stochastic dynamical systems

Reference 5

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Observation 0a774f46-3436-49a9-bf00-39ace62462c0 · outbound

This paper cites E.; Pande, V.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT E.; Pande, V

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:54.673593Z

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Observation 09892cab-da8d-4155-aaa6-852726eacc29 · outbound

This paper cites A.; Schmidhuber, J.; Cummins, F.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT A.; Schmidhuber, J.; Cummins, F

Reference 7

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Observation f28fdc2b-4f72-40de-aa28-b3ea0d8686b1 · outbound

This paper cites R.; Beauchamp, K.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT R.; Beauchamp, K

Reference 8

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Observation 7fe5cc4a-08e1-4fa9-97cf-704542aab889 · outbound

This paper cites Learning molecular dynamics with simple language model built upon long short-term memory neural network.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Learning molecular dynamics with simple language model built upon long short-term memory neural network

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:54.311988Z

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Observation db0eb2ac-1f61-4126-88d1-366810057076 · outbound

This paper cites Path sampling of recurrent neural networks by incorporating known physics.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Path sampling of recurrent neural networks by incorporating known physics

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:54.198360Z

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Observation 1a819e35-da8c-4622-a572-4f82187e82a3 · outbound

This paper cites Do RNN and LSTM have long memory? International Conference on Machine Learning.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Do RNN and LSTM have long memory? International Conference on Machine Learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:54.062251Z

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Observation 9ed283f0-7b8d-428b-9e13-daa975367fbb · outbound

This paper cites B.; Gao, J.; Wang, C.; Paisley, J.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT B.; Gao, J.; Wang, C.; Paisley, J

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:53.962070Z

Source-reported events for the cited work

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Observation afc073f4-53e1-4191-958c-acb5c3a65868 · outbound

This paper cites Can Recurrent Neural Networks Warp Time? The 6th International Conference on Learning Representations (ICLR).

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Can Recurrent Neural Networks Warp Time? The 6th International Conference on Learning Representations (ICLR)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:53.834863Z

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Observation 55820f53-9251-46d3-a46c-0540bc85185a · outbound

This paper cites Improving the gating mechanism of recurrent neural networks.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Improving the gating mechanism of recurrent neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:53.652024Z

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Observation d4076c0d-94c8-4a54-865e-1e18f16e833a · outbound

This paper cites D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; others.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; others

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:53.507124Z

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Observation b053fe11-9bac-4b20-81c6-30b8c19acaef · outbound

This paper cites Long Range Arena: A Benchmark for Efficient Transformers.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Long Range Arena: A Benchmark for Efficient Transformers

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:53.290719Z

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Observation daaa06ef-eeaf-437f-a194-76cb06a69dbf · outbound

This paper cites Accurate prediction of the kinetic sequence of physicochemical states using generative artificial intelligence.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Accurate prediction of the kinetic sequence of physicochemical states using generative artificial intelligence

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:53.103424Z

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Observation 8e5d3e09-2749-43a8-935a-7ec9303e35d1 · outbound

This paper cites Scheduled Sampling Based on Decoding Steps for Neural Machine Translation.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Scheduled Sampling Based on Decoding Steps for Neural Machine Translation

Reference 18

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Observation 17c263ca-f963-4b70-a6bd-67d99f4e558b · outbound

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Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Unresolved cited work

Reference 19

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Observation 70c2316c-290a-4c38-ad00-56f1a917810a · outbound

This paper cites Y.; He, H.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Y.; He, H

Reference 20

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Observation 6a8cb52b-202b-412d-bc75-343477d14186 · outbound

This paper cites K.; Sun, J.; Sander, P.; Huang, X.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT K.; Sun, J.; Sander, P.; Huang, X

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:52.400719Z

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Observation ff27d890-dc74-4592-afb6-d9688eadb568 · outbound

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Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Unresolved cited work

Reference 22

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This paper cites J.; Carlsson, G.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT J.; Carlsson, G

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:52.094015Z

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Observation 5b17048a-b68e-476a-9899-62a94a283a8f · outbound

This paper cites Z.; Bowman, G.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Z.; Bowman, G

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:51.905276Z

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Observation 462b85a6-1ab7-4205-9299-6c7753938944 · outbound

This paper cites Locating and editing factual associations in GPT.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Locating and editing factual associations in GPT

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:51.757469Z

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Observation e11d6949-8400-45b6-9c40-8f9e764b6f64 · outbound

This paper cites E.; Maragakis, P.; Lindorff-Larsen, K.; Piana, S.; Dror, R.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT E.; Maragakis, P.; Lindorff-Larsen, K.; Piana, S.; Dror, R

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:51.599202Z

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Observation 09d01c45-f547-4029-a5e8-3cd67c52bb0b · outbound

This paper cites Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics.

Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics

Reference 27

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Source-reported events for the cited work

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Pith citing papers

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