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Paper Citation Record · LEDGER

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono

As of 14 August 2026, this Paper Citation Record lists 100 of 100 outbound references and 2 inbound Pith citation observations for arXiv:2501.04062.

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

pith.paper-citation-record.v1
2501.04062 v1

Coverage vector

measured 100 of 100 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:51:14.924742Z

measured 102 of 102 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-11T01:46:15.489550Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T21:51:24.176631Z

Reference resolution

100 of 100 outbound references displayed

  • verified exact2
  • verified fuzzy30
  • unresolved65
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0b56b1b3-fdca-4d58-a45b-1e98945957ba · outbound

This paper cites In: High Performance Computing in Science and Engineering: Second International Conference, HPCSE 2015, Sol´ aˇ n, Czech Republic, May 25-28, 2015, Revised Selected Papers 2, pp.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: High Performance Computing in Science and Engineering: Second International Conference, HPCSE 2015, Sol´ aˇ n, Czech Republic, May 25-28, 2015, Revised Selected Papers 2, pp

Reference 1

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Observation c7dfffe4-14a0-4a2d-b2cc-f2eb393ed8b0 · outbound

This paper cites In: Advances in Nonlinear Dynamics, pp.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: Advances in Nonlinear Dynamics, pp

Reference 2

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Observation 33e1dc7d-cdcf-41cf-a769-9d722873b82f · outbound

This paper cites an unresolved cited work.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-10T21:51:10.877114Z digest=sha256:9fd3017b7e530ef52440699b3a013633708bf3dc35eeece12e6935fd9a0af18b

Observation 8165dbc7-c4b3-4795-8cfa-6047fe471725 · outbound

This paper cites an unresolved cited work.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-10T21:51:10.919447Z digest=sha256:6acfa75d765c6ae0775b74582e72ff6b4085a7782bf42c1b424659bb85db66f2

Observation e99f0205-7c55-4ef6-853e-acad0b9b21e9 · outbound

This paper cites CEAS Space Journal 7(3), 335–346 (2015).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono CEAS Space Journal 7(3), 335–346 (2015)

Reference 5

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Observation 16f7809b-e786-48ef-a335-b82ac96b23c6 · outbound

This paper cites Multibody System Dynamics39(1-2), 3–20 (2017).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Multibody System Dynamics39(1-2), 3–20 (2017)

Reference 6

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Observation b124bd42-d727-4936-90b1-edd6417bcda9 · outbound

This paper cites In: AIAA Scitech 2020 Forum, p.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: AIAA Scitech 2020 Forum, p

Reference 7

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Observation 5942c674-f7bb-47d8-8af3-8c3045b3940a · outbound

This paper cites Computers & Graphics 116, 23 ' & $ % You ’ r e an e x p e r t i n t h e PyChrono s i m u l a t o r.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Computers & Graphics 116, 23 ' & $ % You ’ r e an e x p e r t i n t h e PyChrono s i m u l a t o r

Reference 8

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Observation 10460a19-a0cc-4559-8107-d0f3a169497b · outbound

This paper cites In: 2017 International Joint Conference on Neural Networks (IJCNN), pp.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: 2017 International Joint Conference on Neural Networks (IJCNN), pp

Reference 9

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Observation 9f72832a-dab9-41dc-8bb7-878292c54395 · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Reference 10

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source=pdf_text observed=2026-08-10T21:51:11.144798Z digest=sha256:ce54c2329b7da12b204e3bde9454af4196b2cf9855e7b5f3d7a5401a88e36bd1

Observation 312c09d3-d06a-4c87-a285-679df6f14593 · outbound

This paper cites SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain Adaptation.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain Adaptation

Reference 11

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Observation bd66e7d5-efd1-4e92-9f6c-23fcea3eca00 · outbound

This paper cites In: Com- putational Science and Computational Intelligence (CSCI), 2016 International Conference On, pp.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: Com- putational Science and Computational Intelligence (CSCI), 2016 International Conference On, pp

Reference 12

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Observation 6ce647c3-c461-4298-ba97-34467c3d759f · outbound

This paper cites In: Military Technologies (ICMT), 2017 International Conference On, pp.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: Military Technologies (ICMT), 2017 International Conference On, pp

Reference 13

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Observation a11c5f8f-dbf0-4608-a6dd-0ac6c7079858 · outbound

This paper cites In: Ground Vehicle Systems Engineering and Technology Symposium (2017).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: Ground Vehicle Systems Engineering and Technology Symposium (2017)

Reference 14

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Observation c6137c5f-ecb9-4d30-bfe5-4e609b80a314 · outbound

This paper cites IEEE Transactions on Robotics (2023) 24.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono IEEE Transactions on Robotics (2023) 24

Reference 15

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Observation 1d2c3ad5-3258-446e-9aa8-ebe1b88b1d2d · outbound

This paper cites Agriculture 14(3), 499 (2024).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Agriculture 14(3), 499 (2024)

Reference 16

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Observation 3f58e298-367b-4858-9f8e-6a5503751958 · outbound

This paper cites Journal of Marine Science and Engineering 12(5), 701 (2024).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Journal of Marine Science and Engineering 12(5), 701 (2024)

Reference 17

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Observation a46f41d9-c74a-4ff9-8ad1-788b806e078a · outbound

This paper cites Vibroengineering PROCEDIA 18, 123–127 (2018).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Vibroengineering PROCEDIA 18, 123–127 (2018)

Reference 18

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Observation 0b0f2cb5-3d62-4e0b-94de-be46717da9bd · outbound

This paper cites In: 11th International SPHERIC Workshop – Munich Germany, June 14-16, 2016 (2016).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: 11th International SPHERIC Workshop – Munich Germany, June 14-16, 2016 (2016)

Reference 19

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Observation c327e00b-b597-4e39-adba-084ce2c4f9f6 · outbound

This paper cites KSCE Journal of Civil Engineering, 1–10 (2019).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono KSCE Journal of Civil Engineering, 1–10 (2019)

Reference 20

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Observation 2e2a1c52-e4c3-4efd-9b33-06213e92aef3 · outbound

This paper cites In: Fluid Power Systems Technology, vol.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: Fluid Power Systems Technology, vol

Reference 21

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Observation abd94c6e-cc50-419b-a296-542c96119a6f · outbound

This paper cites In: ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition, pp.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition, pp

Reference 22

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 96feadfd-9c5d-45c8-b4fe-5de2dca44af5 · outbound

This paper cites Journal of Engineering for Gas Turbines and Power 141(4), 041014 (2019).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Journal of Engineering for Gas Turbines and Power 141(4), 041014 (2019)

Reference 23

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verified fuzzy
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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation b1c87f68-71a6-4a1a-9366-5124011b1245 · outbound

This paper cites In: Proceedings of the European Wave and Tidal Energy Conference, vol.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: Proceedings of the European Wave and Tidal Energy Conference, vol

Reference 24

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 231eb5eb-59a0-47bc-85a9-ddda5f37ee85 · outbound

This paper cites In: SPE Latin American and Caribbean Petroleum Engineering Conference (2020).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: SPE Latin American and Caribbean Petroleum Engineering Conference (2020)

Reference 25

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verified fuzzy
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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 70980770-b15e-4d49-8f31-b781245f004f · outbound

This paper cites https://groups.google.com/forum/#! forum/projectchrono.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono https://groups.google.com/forum/#! forum/projectchrono

Reference 26

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raw_fallback, observed 2026-08-10T21:51:28.477286Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 54a81346-b687-4d3d-b92b-64d3873d8d34 · outbound

This paper cites https://anaconda.org/projectchrono/pychrono.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono https://anaconda.org/projectchrono/pychrono

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:28.313863Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 578ee016-fc47-4683-9ab8-53db1add5cb9 · outbound

This paper cites https://hub.docker.com/ r/uwsbel/projectchrono.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono https://hub.docker.com/ r/uwsbel/projectchrono

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:28.114750Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 49b61c08-fd92-4013-a3f7-761a70703851 · outbound

This paper cites https://github.com/projectchrono/ chrono.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono https://github.com/projectchrono/ chrono

Reference 29

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 9e28797a-50b8-481a-8cac-c18306d47493 · outbound

This paper cites Scaling Laws for Neural Language Models.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Scaling Laws for Neural Language Models

Reference 30

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Unavailable: canonical work link unavailable.

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Observation fe5f9caa-1325-4a3b-a17d-5ed396aa82b9 · outbound

This paper cites Training Compute-Optimal Large Language Models.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Training Compute-Optimal Large Language Models

Reference 31

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Observation 35815adc-6ef1-4a80-b6a1-26915d786ed9 · outbound

This paper cites Emergent Abilities of Large Language Models.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Emergent Abilities of Large Language Models

Reference 32

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ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 33

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation ccf2eac0-737e-404f-b114-978f5ed5ed75 · outbound

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ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 34

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation d9958f12-4e3f-471c-8278-d0935a81280d · outbound

This paper cites Language Models are Few-Shot Learners.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Language Models are Few-Shot Learners

Reference 35

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source=pdf_text observed=2026-08-10T21:51:11.940608Z digest=sha256:aeebe4120a97e12dd5a50bc1edb6285ed5e89beeaab1cfd22ef230f9ab6197d1

Observation e1ef2698-35b8-440f-8aa0-f4bd4fc83cf2 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Gemini: A Family of Highly Capable Multimodal Models

Reference 36

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source=pdf_text observed=2026-08-10T21:51:11.964196Z digest=sha256:9b720b2c7a0f09da953d93b0adac1106f4dd0b20517acc62fe11797ac8262b9c

Observation a30d8e1a-5b1b-455b-b41e-58e56a3cae29 · outbound

This paper cites https: //www-cdn.anthropic.com/bd2a28d2535bfb0494cc8e2a3bf135d2e7523226/ Model-Card-Claude-2.pdf.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono https: //www-cdn.anthropic.com/bd2a28d2535bfb0494cc8e2a3bf135d2e7523226/ Model-Card-Claude-2.pdf

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:27.499673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:11.995241Z digest=sha256:688cf28a53b889b501d225d73efbf51d093210e57db61f3c52641848efe94636

Observation e6ea2325-ae03-4383-83cf-c045cf0aee1b · outbound

This paper cites ACM computing surveys (csur) 53(3), 1–34 (2020).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono ACM computing surveys (csur) 53(3), 1–34 (2020)

Reference 38

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source=pdf_text observed=2026-08-10T21:51:12.034683Z digest=sha256:3486aa49d61b7a1d77fee6a51416d9e681c353fa454062502e6a37b7e1cd4837

Observation 7e9139a2-8ccd-480f-8ef4-8fcf8bc2d78c · outbound

This paper cites Advances in neural information processing systems 35, 24824–24837 (2022).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Advances in neural information processing systems 35, 24824–24837 (2022)

Reference 39

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raw_fallback, observed 2026-08-10T21:51:27.355072Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:12.058220Z digest=sha256:14e8af214835b8134bcbd6d4d7e1285a5d118ebc3c1cdb0dbc4eac03d64654fd

Observation d634c8e0-35eb-4b2f-87d6-14e154003494 · outbound

This paper cites Show Your Work: Scratchpads for Intermediate Computation with Language Models.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Show Your Work: Scratchpads for Intermediate Computation with Language Models

Reference 40

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source=pdf_text observed=2026-08-10T21:51:12.082003Z digest=sha256:6b704b0188ac0a6638cd63fa27c1fe3d2bb46628e6a37164889cbc2a51dc3144

Observation 34e33a1f-fbde-47be-8efa-2324bd0c6434 · outbound

This paper cites PPT: Pre-trained Prompt Tuning for Few-shot Learning.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono PPT: Pre-trained Prompt Tuning for Few-shot Learning

Reference 41

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source=pdf_text observed=2026-08-10T21:51:12.124752Z digest=sha256:75653ef9f77105e0b57a828533f897a03eb1d8093eebea65e3f659eaebeb9007

Observation 069e3b93-c481-46ef-9e89-64ee2033c155 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 42

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source=pdf_text observed=2026-08-10T21:51:12.157702Z digest=sha256:6f87a7a45bd8bb5315d73cc2a62ccbaa31c03482e3f97fb9fec08483a2043a53

Observation 2ab9b42e-8f20-4333-a63a-0e58ecfdfdf8 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 43

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source=pdf_text observed=2026-08-10T21:51:12.194887Z digest=sha256:1686c9307c1544e1018caed4bb6fa13623e74ab4aea77d3c58c16495bc8001b2

Observation 16cc91be-4bd0-4857-ae56-9c4564bb95c0 · outbound

This paper cites In: The 2023 Conference on Empirical Methods in Natural Language Processing (2023).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: The 2023 Conference on Empirical Methods in Natural Language Processing (2023)

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:27.144755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:12.235302Z digest=sha256:66a912713e79aca186dfe2bc5d1c00610503c007c307da9dd11da5ffeb174b35

Observation 08c410ff-e619-461a-97dd-bd0b3c0a7964 · outbound

This paper cites In: International Conference on Learning Representations (2022).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: International Conference on Learning Representations (2022)

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:51:26.984758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:12.274742Z digest=sha256:ee70c4724bc2ae71c44aa29235e2a8da0f48cb193856506a88b63bdf01a61585

Observation 99149124-b2b1-419c-9ac8-6548610c0964 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 46

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no resolver link, observed 2026-08-10T21:51:12.324824Z

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source=pdf_text observed=2026-08-10T21:51:12.324824Z digest=sha256:d845bbdb2b7dd02f82bff1b7ec6046f65cc0a87b9d4570e06021124296f07df5

Observation 18f82041-0a4e-4a14-b812-a6b2f057587f · outbound

This paper cites OMPGPT: A Generative Pre-trained Transformer Model for OpenMP.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono OMPGPT: A Generative Pre-trained Transformer Model for OpenMP

Reference 47

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source=pdf_text observed=2026-08-10T21:51:12.374744Z digest=sha256:4b4960434f00ab8daa19b224e1e8db2f9e63e808928307a80f83e6373eb53b09

Observation d40b33da-7959-4cf2-b712-dc5665b41e3a · outbound

This paper cites MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks

Reference 48

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source=pdf_text observed=2026-08-10T21:51:12.423447Z digest=sha256:da311f6f739009c3ae9ffcc120f17932f3a4c5a0b663408a5bb91a15aa2276b8

Observation 4a246db9-2c5b-481e-8e43-128b0bda3414 · outbound

This paper cites https://github.com/meta-llama/ llama3/blob/main/MODEL CARD.md.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono https://github.com/meta-llama/ llama3/blob/main/MODEL CARD.md

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:51:26.824760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:12.464747Z digest=sha256:8f80cc78068eff1316e5acc66142d1c1a895bce50a00f518e4594bec4317dba1

Observation b55ddc40-1689-4799-88ab-f292fe0cabcb · outbound

This paper cites Multibody System Dynamics, 1–23 (2024) 28.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Multibody System Dynamics, 1–23 (2024) 28

Reference 50

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:26.684860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:12.514129Z digest=sha256:aa4dd9b1d45bdc6ea2b4aab7a6318351ed47fa7599f87af7e1b3abd76a55bf81

Observation 5f926c8e-bebb-4382-bce4-c7b3ecf28394 · outbound

This paper cites Multibody System Dynamics, 1–29 (2023).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Multibody System Dynamics, 1–29 (2023)

Reference 51

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:26.524931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:12.554743Z digest=sha256:ba03e838d6abad1dd33f8f40a7a220e28b03a6d0124afd08c72ab7fd3ec9707f

Observation 3e174d04-4939-40af-b181-8c4731f6ed9e · outbound

This paper cites Journal of Machine Learning for Modeling and Computing 4(4) (2023).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Journal of Machine Learning for Modeling and Computing 4(4) (2023)

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:51:26.404969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:12.604812Z digest=sha256:8f2a722e23a8cebf630a80cfbf80267ad037914e6e2372a5cb7d751b6b0990ed

Observation e332b476-09d6-4e83-bf41-fd9fc1673924 · outbound

This paper cites Journal of Computational Physics 378, 686–707 (2019) https://doi.org/10.1016/j.jcp.2018.10.045.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Journal of Computational Physics 378, 686–707 (2019) https://doi.org/10.1016/j.jcp.2018.10.045

Reference 53

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source=pdf_text observed=2026-08-10T21:51:12.665262Z digest=sha256:0a42557d34f1128f1b03a1dbf742af7df33cc6697b55d78a4c08ffbb8aa1a1d1

Observation 44452e0f-78de-4ea8-af0a-c82a039cfa07 · outbound

This paper cites Computers and Geotechnics 169, 106237 (2024) https://doi.org/10.1016/j.compgeo.2024.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Computers and Geotechnics 169, 106237 (2024) https://doi.org/10.1016/j.compgeo.2024

Reference 54

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:12.715257Z digest=sha256:33efa0983e3f77a5d0d37c2eb12504503921c3b7e302f7db092fcbf434cf00aa

Observation cca651f7-cdc5-49f2-8aa6-2743e1377cc4 · outbound

This paper cites IEEE Transactions on Neural Networks and Learning Systems (2023).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono IEEE Transactions on Neural Networks and Learning Systems (2023)

Reference 55

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:26.235100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:12.752825Z digest=sha256:d425aac4db0db2f193b88903a88adfd93e6ad2b45421502d73741b6b62c368dc

Observation 5b64310c-0a2c-4e72-98c4-3eb3ecba978f · outbound

This paper cites an unresolved cited work.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 56

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raw_fallback, observed 2026-08-10T21:51:26.085562Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:12.784758Z digest=sha256:9517d605f994f7aab102a726acf366b90e38e18e97abcc70498377e91c5e4d05

Observation c2a597f3-b136-447a-8d9a-0addc8aa6554 · outbound

This paper cites an unresolved cited work.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 57

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raw_fallback, observed 2026-08-10T21:51:25.914749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:12.854733Z digest=sha256:fa4629642e70bdeaa364307f9412c599c740931518d73c0addbf8c5966eff850

Observation acd0ab90-b393-4c59-ac74-b9b6bee42100 · outbound

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

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Code Llama: Open Foundation Models for Code

Reference 58

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no resolver link, observed 2026-08-10T21:51:12.903168Z

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source=pdf_text observed=2026-08-10T21:51:12.903168Z digest=sha256:16f48f31195ba4380fe89bd8e3dd4713ef85cca57f73e6586eab48966b1a2d0d

Observation 29c4fe9c-0013-498b-9f8a-bfb33279c008 · outbound

This paper cites an unresolved cited work.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 59

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raw_fallback, observed 2026-08-10T21:51:25.734747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:12.964746Z digest=sha256:d42db20bf1c1f1e575811914b32d1811104e9044854a057ee8b6c8e2cc8fbbc8

Observation 819eed8d-b7e3-448a-bc40-41cc71832feb · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Gemma: Open Models Based on Gemini Research and Technology

Reference 60

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source=pdf_text observed=2026-08-10T21:51:13.012279Z digest=sha256:bce5296e59bf36cc854fc55d251d5a4fca5a4c9e8144b60b09d8329a3d5d948b

Observation 4a725dd1-0c4a-4e36-a0af-527667c48825 · outbound

This paper cites https://goo.gle/codegemma.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono https://goo.gle/codegemma

Reference 61

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:25.554758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:13.054748Z digest=sha256:54c85ecbb5ff8ba0f2423dc99bdcf5967cfbb130d70e8135dd702fd6a4d15a7a

Observation 32e5c4e0-1f31-4137-aa00-489c1efeb25f · outbound

This paper cites an unresolved cited work.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 62

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unresolved
raw_fallback, observed 2026-08-10T21:51:25.404752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:13.087663Z digest=sha256:1a0af20712eea1d98d20113f9bfa40c7d63b81d416398532f105d165dfc7e1ec

Observation b20608e6-1d6e-4f16-8090-718d60c16447 · outbound

This paper cites OceanGPT: A Large Language Model for Ocean Science Tasks.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono OceanGPT: A Large Language Model for Ocean Science Tasks

Reference 63

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source=pdf_text observed=2026-08-10T21:51:13.124747Z digest=sha256:88782acf4ce692cb08488d33f493b65bc752b1ce6f5bcc11e198191d9d64a48e

Observation 37ecc86b-5485-4519-9d70-bd42c29cc706 · outbound

This paper cites an unresolved cited work.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 64

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raw_fallback, observed 2026-08-10T21:51:25.248078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:13.174748Z digest=sha256:2b3099e66a9b10aef012790d239da14ed3b03d6bccb706e9cc85072fe2d2d5e5

Observation f010d8d6-5da3-4a1c-94fc-ea430e3ef2ca · outbound

This paper cites AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

Reference 65

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no resolver link, observed 2026-08-10T21:51:13.224748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:51:13.224748Z digest=sha256:463b0954f6cfd6d624e765e5fe7c1e7a5972880b10589dc56e4d1fc5d73ea905

Observation bb8ebf95-0e71-4157-a9b1-9f3e7810d8f8 · outbound

This paper cites C4Q: A Chatbot for Quantum.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono C4Q: A Chatbot for Quantum

Reference 66

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metadata mismatch
local_arxiv, observed 2026-08-10T21:51:18.784769Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:13.271865Z digest=sha256:6d6adc3d20a9936f59f6b42af46e573ca06c8bdbd8ff0fd21e7c4cbbd7641d27

Observation 1b422ac3-3e5c-48fe-b08b-c4cf180aeb6d · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 67

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unresolved
no resolver link, observed 2026-08-10T21:51:13.314829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:51:13.314829Z digest=sha256:1178315c691d74bbea7d373c6a25850e0d6bdce8d78b28643ad6b29f02cc5824

Observation 46f42efe-07f9-4641-bea7-a1360c2c4b5e · outbound

This paper cites an unresolved cited work.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 68

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unresolved
raw_fallback, observed 2026-08-10T21:51:25.074755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:13.373416Z digest=sha256:16ea6ba7b94bd17581542693b1a360f9356a42c33766ef643741bcea67b583ed

Observation b2646517-0a9b-4ab2-881a-3bcbe04e875d · outbound

This paper cites an unresolved cited work.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 69

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unresolved
raw_fallback, observed 2026-08-10T21:51:24.931462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:13.424749Z digest=sha256:a5c3274625b0f63879a467797c4890e61417c20da094577c8039f2f5751d3bc4

Observation b1542245-9061-4789-806e-6e75f2285e04 · outbound

This paper cites Advances in Neural Information Processing Systems 35, 1950–1965 (2022).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Advances in Neural Information Processing Systems 35, 1950–1965 (2022)

Reference 70

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation df5b034d-1dd3-4f3e-9036-5be9015d4aaa · outbound

This paper cites Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

Reference 71

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7a3d1ccc-302c-455c-add2-42c06fff9140 · outbound

This paper cites In: Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp

Reference 72

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raw_fallback, observed 2026-08-10T21:51:24.584752Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:13.564749Z digest=sha256:dcfc80f9007c0ef0c9d52d2e226eeb8eb10733a56b911e1a40f1413e6eb45e47

Observation 221c938d-b862-4012-975b-30ea7b345737 · outbound

This paper cites http://api.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono http://api

Reference 73

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raw_fallback, observed 2026-08-10T21:51:24.442572Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:13.614747Z digest=sha256:5b2bdb311e3a715241895ca4c5f0c440ff83366bb147c2c883e2ccf343e1da59

Observation 05bcd1ba-8b30-4e1e-a066-8b24e08c5444 · outbound

This paper cites Mixtral of Experts.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Mixtral of Experts

Reference 74

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ec6ef824-9fcd-4b99-9dba-55a028db04d1 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Evaluating Large Language Models Trained on Code

Reference 75

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-10T21:51:13.694893Z digest=sha256:6b974b18f06241afcc650029f74e99cba9e1428f642010d2d74e782afacf9bc2

Observation a6c569bb-55d3-477c-9673-e8318ecaaac2 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Measuring Massive Multitask Language Understanding

Reference 76

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Observation 0fcb1583-8a49-4ccd-81ed-42557c00110b · outbound

This paper cites StarCoder: may the source be with you!.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono StarCoder: may the source be with you!

Reference 77

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source=pdf_text observed=2026-08-10T21:51:13.794747Z digest=sha256:783cdf72727a0928c3a17d3f35142c8a5fa35d9296a3ec4950d047830e266b4f

Observation 1bdbbe04-ce0a-488c-892a-f66034cc6aa0 · outbound

This paper cites Understanding Emergent Abilities of Language Models from the Loss Perspective.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Understanding Emergent Abilities of Language Models from the Loss Perspective

Reference 78

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unresolved
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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:51:13.834744Z digest=sha256:34ab8160468badaf6b4c1eddff494a49370b46ef6562086a7e4c1aa18691b056

Observation 80e4cdfc-4ead-4b0c-8c6c-87eea928ec23 · outbound

This paper cites Trends in cognitive sciences 3(4), 128–135 (1999) 31.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Trends in cognitive sciences 3(4), 128–135 (1999) 31

Reference 79

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:24.274909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:13.864752Z digest=sha256:2cd483c8a6caf35b3b6e7b6ea3b1128af2af3ecdaff73bb3d19056705a5dd7bd

Observation 55a04383-6957-44b5-9249-568c922f2bec · outbound

This paper cites In: International Conference on Learning Representations 2022 (2022).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: International Conference on Learning Representations 2022 (2022)

Reference 80

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:24.094753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:13.918275Z digest=sha256:1ebad9cd349029868d180b58d76d9a6a99334c863872385ccdee82ff642589f0

Observation 16865331-98b1-4acd-ad22-e5fad726a01a · outbound

This paper cites Continual Pre-Training of Large Language Models: How to (re)warm your model?.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Continual Pre-Training of Large Language Models: How to (re)warm your model?

Reference 81

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5f11d318-a8f9-4c3c-81d9-369c4dd331f6 · outbound

This paper cites Proceedings of the national academy of sciences 114(13), 3521–3526 (2017).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Proceedings of the national academy of sciences 114(13), 3521–3526 (2017)

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:51:23.924749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 5fd3dd38-e579-4fe1-915d-761d47dfcc58 · outbound

This paper cites Advances in neural information processing systems 30 (2017).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Advances in neural information processing systems 30 (2017)

Reference 83

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:23.714752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation dad60fa9-2924-4eb6-9d24-0f7c780309e6 · outbound

This paper cites Structured Chain-of-Thought Prompting for Code Generation.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Structured Chain-of-Thought Prompting for Code Generation

Reference 84

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Observation 4f9371d7-986d-442c-9a03-914930ee7b9c · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 85

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Observation 0330f9dc-5b6b-4ef0-aef6-9cece5c8e97d · outbound

This paper cites In: The Twelfth International Conference on Learning Representa- tions (2023).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: The Twelfth International Conference on Learning Representa- tions (2023)

Reference 86

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:23.514858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation cbaf42d4-afe6-4161-9eea-af50fd9b6f10 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 87

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Observation c0f45c1c-d86e-4d3e-a1fe-8e964d22000f · outbound

This paper cites In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp

Reference 88

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:23.284817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 0ed08dc1-01aa-47f6-ae47-cf5bd3537b69 · outbound

This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 89

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Observation bf0d2196-1f21-4a80-a50f-0eb9b3e27a0c · outbound

This paper cites Advances in Neural Information Processing Systems 36 (2024).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Advances in Neural Information Processing Systems 36 (2024)

Reference 90

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:23.101154Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:14.386908Z digest=sha256:b3ca16640a27ad7ed00ebbe2547593a7d84c5599f3eb68ada4a952898bf7f6ac

Observation 55bb9d88-0d1e-461c-82fc-87105d0d2a73 · outbound

This paper cites an unresolved cited work.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 91

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unresolved
raw_fallback, observed 2026-08-10T21:51:22.934821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 11a4a335-bb95-45c4-a7ae-4731341daea4 · outbound

This paper cites ACM Transactions on Intelligent Systems and Technology 15(3), 1–45 (2024).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono ACM Transactions on Intelligent Systems and Technology 15(3), 1–45 (2024)

Reference 92

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:22.780188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:14.474891Z digest=sha256:245f085f889bca904375497160c4465939308bae6c2061513694b02be28570fd

Observation 7be7d693-e4dd-40d3-8d33-9b1665ca067d · outbound

This paper cites In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp

Reference 94

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e32a0667-ac2f-4e92-898c-d1b52e7c1d7b · outbound

This paper cites an unresolved cited work.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Unresolved cited work

Reference 95

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Unavailable: canonical work link unavailable.

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Observation 4cb4cfec-ec7e-4ce8-a0c5-6ff43516a2f2 · outbound

This paper cites ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Tasks.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Tasks

Reference 96

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no resolver link, observed 2026-08-10T21:51:14.662905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6824bfcb-6890-4a3f-959a-ba284af373da · outbound

This paper cites In: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming, pp.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono In: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming, pp

Reference 97

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:51:22.354832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:14.724823Z digest=sha256:2226b8b8e1fa5ff78d6cfcb745a726f833da10404029bc9f2ec541bd3c62f0b9

Observation 0bb1cba8-2754-41ad-a3ab-49d297798383 · outbound

This paper cites Advances in Neural Information Processing Systems 36 (2024).

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Advances in Neural Information Processing Systems 36 (2024)

Reference 98

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verified fuzzy
raw_fallback, observed 2026-08-10T21:51:22.634809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:14.774753Z digest=sha256:747968036a98f6a310a7cbc5b5c71a32e0e8a2ffc9dd7338b6a8e5837be9cddf

Observation ed04ff3a-7dac-4b48-94dc-698026e6557b · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 99

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unresolved
no resolver link, observed 2026-08-10T21:51:14.824751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 251beda5-c09b-4631-b574-99a77bb2fa06 · outbound

This paper cites https://arxiv.org/abs/2408.11987.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono https://arxiv.org/abs/2408.11987

Reference 100

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verified exact
raw_fallback, observed 2026-08-10T21:51:15.894818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T21:51:14.884747Z digest=sha256:a9619e8d3250e9046e3287ac426d877a81d13f5b5d427a1db386b7877026d518

Observation d6153789-b196-4583-a38c-f392e9b87b98 · outbound

This paper cites Large Language Model Unlearning.

ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Large Language Model Unlearning

Reference 101

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:51:14.924742Z digest=sha256:1b8c0e96b67d919bfe1640b0dd6dc4db28f5c9489fd27dd3f324d57d1927b6c8

Pith citing papers

Observation fd048540-e214-4a14-84fd-ca850fdc4d0b · inbound

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems cites this paper.

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono

Reference 3

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arxiv_id, observed 2026-05-11T21:51:24.275911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-08T04:05:24.618750Z digest=sha256:294571f9a333b2644eff20323f38dae5f875c3e306e88cba6b70f475c8548727

Observation 779b9797-1ace-42be-b1d2-bd92f5c46e22 · inbound

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems cites this paper.

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-11T04:20:58.696989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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