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Paper Citation Record · LEDGER

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol

As of 7 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 3 inbound Pith citation observations for arXiv:2506.13068.

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

pith.paper-citation-record.v1
2506.13068 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:40:22.399701Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:26:39.618536Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T02:37:34.582129Z

Reference resolution

60 of 60 outbound references displayed

  • verified exact1
  • verified fuzzy24
  • unresolved35
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2284ccdc-720f-4bda-bc84-561b7c7977e3 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:31.571674Z

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.

source=arxiv_source observed=2026-08-07T00:40:16.942543Z digest=sha256:6f1b3b0a96ff263f94b50b1e64b906917fa67b7678f4de1f03d670918311d117

Observation 57522c89-02c8-42a4-86eb-ecf3f3cf6baf · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:31.427553Z

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.

source=arxiv_source observed=2026-08-07T00:40:17.022364Z digest=sha256:5626e6c04fed94a4f3fe9e41a04d3f6997c6bae4f7f1c8fde28c1a4a968d2879

Observation ed0dc1fe-ce68-4c6f-b5d9-4a5e43fc9558 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:31.266655Z

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.

source=arxiv_source observed=2026-08-07T00:40:17.111543Z digest=sha256:03f392733afb7955e8344525db5bd2c25dc97f3cb998059ce8a0451a7eccbedc

Observation e2cd9198-bfcc-4c78-9604-ab3ddf896dcc · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:31.018064Z

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.

source=arxiv_source observed=2026-08-07T00:40:17.218704Z digest=sha256:16e45c371a4de55e72d406f74c53313ab0e6aa4afba3a4acb4f7230ad109ce0a

Observation 5dd42a6a-52c9-41b3-8218-1cd62c3761d2 · outbound

This paper cites and Macharis, C.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol and Macharis, C

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:30.752244Z

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.

source=arxiv_source observed=2026-08-07T00:40:17.330657Z digest=sha256:16e7381b7de754ad538f8766b35c762cfe2421c20e76aa461f19a8a830a87b47

Observation 5f1f2414-6ace-4e78-b46c-830a8571e557 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:30.488003Z

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.

source=arxiv_source observed=2026-08-07T00:40:17.437683Z digest=sha256:e24767b5fbe6b84982cbcedeec0d9d754d2ee29d3b93ebb97e536cf6ef600612

Observation 626fec08-1d7f-4c9d-8c7b-0b07572592e1 · outbound

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

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol On the Opportunities and Risks of Foundation Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T00:40:17.517015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:40:17.517015Z digest=sha256:1341a5af2b6aeaffacab7d4cc87da31a6f5a8bcfd5825f82df7415c747d6f085

Observation 37501f18-fcec-4776-aac1-3fe5c1fea7ea · outbound

This paper cites C., Poschmann, P., Werner, J., and Zarnitz, S.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol C., Poschmann, P., Werner, J., and Zarnitz, S

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:30.249639Z

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.

source=arxiv_source observed=2026-08-07T00:40:17.600523Z digest=sha256:458dbcd290014b0b16a57853e70757d0d3eb3255f1b4ecc62857f82467479361

Observation e60bd66a-8c50-444f-8b34-1dd3d55a87f7 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:29.926577Z

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.

source=arxiv_source observed=2026-08-07T00:40:17.689392Z digest=sha256:855611dd4b5abdd97900537805c334f238dbf51995ecae042d9f3efb51252df3

Observation 8cbdf526-237e-49f4-a938-0a27bad9f772 · outbound

This paper cites L., Jain, R., Emami, P., Wadsack, K., Ding, F., Sun, H., Gruchalla, K., Hong, J., Zhang, H., Zhu, X., and Kroposki, B.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol L., Jain, R., Emami, P., Wadsack, K., Ding, F., Sun, H., Gruchalla, K., Hong, J., Zhang, H., Zhu, X., and Kroposki, B

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:29.688161Z

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.

source=arxiv_source observed=2026-08-07T00:40:17.800982Z digest=sha256:7e9e6645d197a8824a8b656a712d0ddde16279dc33c0d1f4216c4de87ab00521

Observation 66364a53-920a-470c-868a-e9631f1238db · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 11

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:29.489787Z

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.

source=arxiv_source observed=2026-08-07T00:40:17.909996Z digest=sha256:cb8b5a7041f2386209b26546cf949afc67d657a371a59581a4ccb23afc9660e0

Observation fe4527af-1b9c-4586-a460-96449a832fd9 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:29.166609Z

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.

source=arxiv_source observed=2026-08-07T00:40:18.022005Z digest=sha256:0bb3101a5980f966ab2e9786b99f38cfb9f1472e7cb1a90a813c1c99002282e8

Observation e1391804-c642-4641-83aa-5c5a653afbb5 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:28.949920Z

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.

source=arxiv_source observed=2026-08-07T00:40:18.125064Z digest=sha256:ab9ba72fa4920a6a58c6f4a820b3598f23c5a27a24b1c57e857e0eed7263f00b

Observation 7d410999-9b39-423f-90ba-2083442e5af1 · outbound

This paper cites and Sinha, D.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol and Sinha, D

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:28.733246Z

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.

source=arxiv_source observed=2026-08-07T00:40:18.186770Z digest=sha256:b339445ac990cb5005255f8e1fc322ee8b772166723a61eefec406e73fd9e1b8

Observation 02e1dfaa-7225-4e05-a3b4-1a59361fb742 · outbound

This paper cites J., Foropon, C., Tiwari, M., and Gunasekaran, A.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol J., Foropon, C., Tiwari, M., and Gunasekaran, A

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:28.477147Z

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.

source=arxiv_source observed=2026-08-07T00:40:18.272753Z digest=sha256:4dc14ab8255bfdb2976c5828ed2f96aaa59657fc676290eaf545f84c0d0b4b5b

Observation aff5b3b6-1196-4c35-8764-32f0b3207095 · outbound

This paper cites and Weiss, G.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol and Weiss, G

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:28.228862Z

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.

source=arxiv_source observed=2026-08-07T00:40:18.348432Z digest=sha256:7cf7f054d2b447d0190e383761016cb8b1b4f86df8ebfb5c0d6e6dec9116b495

Observation 19081cda-92c0-4b07-ad95-58e06d9ad713 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:27.859228Z

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.

source=arxiv_source observed=2026-08-07T00:40:18.425591Z digest=sha256:1c53cb659879748b21f453ad3211192f8529eb9d216b1cbe20d68db125b68a08

Observation 63f82a3c-2890-49ea-8211-d5cd468b6fbc · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:27.530317Z

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.

source=arxiv_source observed=2026-08-07T00:40:18.548662Z digest=sha256:1f8e8424c45581b178731d6bc769f915c4f1d127d8ef476016a2d17a80e91bd7

Observation 91ef5012-67ee-464e-a4f4-e548e9641cac · outbound

This paper cites S., Jernegan, L.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol S., Jernegan, L

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:27.308908Z

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.

source=arxiv_source observed=2026-08-07T00:40:18.636237Z digest=sha256:0fb91a85c32c58e071b8fcffb15612155538f11e20f924682c2a298ab73593ec

Observation ec1b5408-af2b-4905-9e49-391608b2d998 · outbound

This paper cites F., Connor, D., Fotheringham, A.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol F., Connor, D., Fotheringham, A

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:27.089131Z

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.

source=arxiv_source observed=2026-08-07T00:40:18.710504Z digest=sha256:474a5871655e8ce6d0ad3c5907784d0e3d1b3e5d8106bc752f2551f917f7c6b2

Observation 820cf74f-7836-4cf3-9d76-150417264cde · outbound

This paper cites From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models

Reference 21

Resolution
verified exact
local_arxiv, observed 2026-08-07T00:40:22.627390Z

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.

source=arxiv_source observed=2026-08-07T00:40:18.798889Z digest=sha256:eefc5d6855f34b397e20d7db929f1f66b7a70aad2c9caa80068385ebbc86000b

Observation 280abfe8-4ab9-452c-ba1d-17fd0ec054ef · outbound

This paper cites Parameter-efficient fine-tuning (peft) — hugging face documentation.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Parameter-efficient fine-tuning (peft) — hugging face documentation

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:26.941169Z

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.

source=arxiv_source observed=2026-08-07T00:40:18.911341Z digest=sha256:f478430f7c03d796522d42adcf4e4c518fdb407141061a6c5db88b6a9f78ab02

Observation 0233b7cc-7477-421a-af16-367cf2ce3299 · outbound

This paper cites Supervised fine-tuning trainer — hugging face trl documentation.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Supervised fine-tuning trainer — hugging face trl documentation

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:26.790725Z

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.

source=arxiv_source observed=2026-08-07T00:40:18.993981Z digest=sha256:66626db4dfd64ca1de4eaaf23d65754ac23e0abcee33ccd0f625c9f0b8967a70

Observation b8f0b810-ed04-478e-a9b7-44e8b6d6966d · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 24

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:26.661133Z

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.

source=arxiv_source observed=2026-08-07T00:40:19.052543Z digest=sha256:cced34e72ee79dc0868ece5fb9918c8796559bd7b41c6ec8c3788172971a4c4b

Observation 5f05f6cd-a565-4508-8016-f83b00ca330b · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 25

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:26.518755Z

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.

source=arxiv_source observed=2026-08-07T00:40:19.128073Z digest=sha256:4cf9aa02d511a2a7ec7ffecc1117312666727d226187967035503b4066e76007

Observation 73cd4a34-ba39-4e2c-9868-f873f4f20f5a · outbound

This paper cites Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T00:40:19.247609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:40:19.247609Z digest=sha256:5c5459b6f40bedeb01ffa58bd46e5f9196722326fdaf225d9e9e9f1d138e86e1

Observation 774759bf-6ec7-42d5-8333-12ec04df5214 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:26.357042Z

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.

source=arxiv_source observed=2026-08-07T00:40:19.364244Z digest=sha256:b63fc9daa7096cf669762eb1bf6536d7ca575298d6e03e25d2b9cab24d3c7ace

Observation f473c0de-8ae2-484c-95be-47aa06feafdb · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 28

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:26.195410Z

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.

source=arxiv_source observed=2026-08-07T00:40:19.516224Z digest=sha256:eebd557a6bcb267ecb5e8695d264e004b1d68d0b80142fc38727a7f3e34f6371

Observation 88f356d8-c798-46d9-b3fb-a14f1367c319 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 29

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:26.069617Z

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.

source=arxiv_source observed=2026-08-07T00:40:19.606333Z digest=sha256:0f3fe2f37845632d8bced76f43a1e2cd768ded87d6363be4cee41c5f4892b5b8

Observation 96e7a52c-6851-4a3d-af1f-dc10fe32fae5 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 30

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:25.929551Z

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.

source=arxiv_source observed=2026-08-07T00:40:19.720139Z digest=sha256:b4063aad0890b5af8dc2152f1f05e9a490a9cc5e3760014fe5a67ad5b2052de3

Observation e6796ae1-569f-4a45-8b1a-65717f8fc8e9 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 31

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:25.815489Z

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.

source=arxiv_source observed=2026-08-07T00:40:19.922706Z digest=sha256:e2130eec1d0d9a36d2ddbafc25403a4b7581e9260fa67bba9c9a15141d0783a7

Observation 94108e58-b19e-4ce1-960d-60a571f3633d · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 32

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:25.697766Z

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.

source=arxiv_source observed=2026-08-07T00:40:20.061102Z digest=sha256:f2bdc2a6e7d16825f880a16f6ffc1c34637504cc17c0b715df35088e2ca6edf7

Observation 2530c08e-3c09-4c7a-88aa-a92988165f3e · outbound

This paper cites I., Sathvik, A.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol I., Sathvik, A

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:25.602111Z

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.

source=arxiv_source observed=2026-08-07T00:40:20.196513Z digest=sha256:369e01e5f449dda18e6c11bfefa6fafe3d8f42dc5b6f0b9f2560c9b6d3c7d421

Observation d6e2a2bd-af5e-4642-b880-868890f8f630 · outbound

This paper cites ClimaX: A foundation model for weather and climate.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol ClimaX: A foundation model for weather and climate

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T00:40:20.308443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:40:20.308443Z digest=sha256:72c09127d7330050f260d54757d4043fdf331cf440de9dd8fca75f6130118496

Observation 52eff9a0-fe42-449c-a5e9-e74d19d18e67 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 35

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:25.507407Z

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.

source=arxiv_source observed=2026-08-07T00:40:20.476535Z digest=sha256:caf94849e47d9e0bec94919487da25d19600165231f2d6c37d4553563d2e9464

Observation c8819070-b297-40e2-8786-d5cb06f47444 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:25.397199Z

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.

source=arxiv_source observed=2026-08-07T00:40:20.613765Z digest=sha256:2f63a94ef35f3647ddf4ec1fe0366b067fea5fec3e35b0151b522d65607822e1

Observation 1bab224f-ec04-4627-89d0-7dc4ea805e95 · outbound

This paper cites and Mac \'a rio, R.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol and Mac \'a rio, R

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:25.266211Z

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.

source=arxiv_source observed=2026-08-07T00:40:20.679222Z digest=sha256:ef86e113fbc7c911f990309c59e0255177d5a3504c86bcf244f7cb28760892d7

Observation 6363a46c-09c3-4a02-bec3-9eca97f229ba · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:25.161623Z

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.

source=arxiv_source observed=2026-08-07T00:40:20.749446Z digest=sha256:8b47591b8072ca71c1386148e636148e4e78cb0e05716a866a75585ea0414f53

Observation 673537e4-9a9a-4a02-90ce-b2d0e1962e27 · outbound

This paper cites and Gecan, R.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol and Gecan, R

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:25.024516Z

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.

source=arxiv_source observed=2026-08-07T00:40:20.782562Z digest=sha256:2b9a8cec060bfcd2118f074b05fe763be543c80ee624a96143f12b054a9b6267

Observation abba4fa2-db06-4db6-8bf9-e6dca4efe8c1 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:24.872839Z

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.

source=arxiv_source observed=2026-08-07T00:40:20.831740Z digest=sha256:38d7ed19674d43188008f95e770b27e489b4011a4dc8e49bdbe2cde081c942cd

Observation 190a3df7-c4e6-49f4-8739-4f7ad9488ac7 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:24.717597Z

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.

source=arxiv_source observed=2026-08-07T00:40:20.887357Z digest=sha256:fc47d8db397b5c78b5eab38f3c3d7545b860932554a37bb16a75eafb517c5e9d

Observation eb28d2fe-a284-418f-a3d0-36b66630b495 · outbound

This paper cites C., et al.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol C., et al

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:24.512553Z

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.

source=arxiv_source observed=2026-08-07T00:40:20.951704Z digest=sha256:b1396cd7cf24adcb31e1c725da02e5e2cc7b2c1574314d3297838826975b1db7

Observation 426f5c5e-6c59-4e1e-ac51-4be73929158e · outbound

This paper cites A., Camur, M.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol A., Camur, M

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:24.396004Z

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.

source=arxiv_source observed=2026-08-07T00:40:21.048210Z digest=sha256:a3ad1806475b2ca49543e73531be6e0e7b78023c2d9c244249a581dc14ba80d3

Observation 6a6a1919-44ff-45b2-8d43-3210c9da57d4 · outbound

This paper cites S., Iqbal Nur, H., and Pertiwi, A.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol S., Iqbal Nur, H., and Pertiwi, A

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:24.283259Z

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.

source=arxiv_source observed=2026-08-07T00:40:21.129195Z digest=sha256:1a1e9236fe4711fc60e3656a69517fd1cf52f842ab63deb49dca39eec7227c58

Observation 0f927117-563b-4add-a434-fdb2811a01c6 · outbound

This paper cites Department of Transportation , F.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Department of Transportation , F

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:24.145516Z

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.

source=arxiv_source observed=2026-08-07T00:40:21.198401Z digest=sha256:caeba3649c5a7a0391c5972dde7146e0da3ff7727b8c34fa90775fc12fa5bde5

Observation 9498c0aa-a496-43ac-a169-095a617045a5 · outbound

This paper cites A., Gehlhoff, F., Dogan, A., and Fay, A.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol A., Gehlhoff, F., Dogan, A., and Fay, A

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:24.035833Z

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.

source=arxiv_source observed=2026-08-07T00:40:21.272736Z digest=sha256:88fa3dfddc6161ff44de9f7b81318e47258538d274aeed2f0c182c040a885837

Observation 4a830062-f6fb-43fd-9310-1d7e4194adae · outbound

This paper cites M., Glassy, E.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol M., Glassy, E

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:23.861376Z

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.

source=arxiv_source observed=2026-08-07T00:40:21.346121Z digest=sha256:1b96d81d1d6e583a7bf5ccd6b42c3045396d5e88e764fe100f5b5d8674f68934

Observation 93ddd1c5-c56b-42cc-93df-9c251c299b4f · outbound

This paper cites V., Zhou, D., et al.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol V., Zhou, D., et al

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T00:40:21.448178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:40:21.448178Z digest=sha256:948de5446547b7e6f582bc76181289c9b8f378db3917db3ea4c43ceac26900d2

Observation f8463484-1be3-4c70-b0b2-8cc907f684e1 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:23.729744Z

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.

source=arxiv_source observed=2026-08-07T00:40:21.580845Z digest=sha256:46845fe34bfbd5fe3ca9852ae9bd96797f69fd30e802745bcc95712040864901

Observation e94af15b-1f73-4d30-9e12-64b78bb516a0 · outbound

This paper cites A., Ravulaparthy, S.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol A., Ravulaparthy, S

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:23.600015Z

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.

source=arxiv_source observed=2026-08-07T00:40:21.681019Z digest=sha256:10371952cac380f49c3eecdbac631b822f4efce97c9c5b0bcd521aff27ceb7bc

Observation c99d7f94-67c8-4ae7-a494-9ab5bb9aa5db · outbound

This paper cites B., Sorensen, H., Nugent, P.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol B., Sorensen, H., Nugent, P

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:23.447086Z

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.

source=arxiv_source observed=2026-08-07T00:40:21.847369Z digest=sha256:7eeab470b29763f11f22b7d21c9e2930ee17fbc9f07fa3a0023ef5152f1c81b2

Observation 01cfae1d-ed82-4548-9700-5d75cb497971 · outbound

This paper cites J., and Omitaomu, O.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol J., and Omitaomu, O

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:23.311110Z

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.

source=arxiv_source observed=2026-08-07T00:40:22.015176Z digest=sha256:6516158766be30121f8ae9ed0edb75813736e574f844e4e1b8e6a71166bd620e

Observation 67f694e8-36cf-4f30-845b-338033a1029e · outbound

This paper cites Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-07T00:40:22.025168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:40:22.025168Z digest=sha256:c043b43a087ca3a455d02d42c98ab523a1f62556d879fd15daa17cf30f1e9103

Observation dcfd61fd-e594-4b12-bea1-2863cfd6ea32 · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:23.174688Z

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.

source=arxiv_source observed=2026-08-07T00:40:22.074566Z digest=sha256:fb2f01232f5c5c547c678a937c0116ba38268c26e67993dc24020d7ab1d231fc

Observation 86bd9022-670d-4ad0-aecc-e59e2f22fd59 · outbound

This paper cites GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systems.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systems

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-07T00:40:22.191430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:40:22.191430Z digest=sha256:a8b0271abfb6bb63e8c54f3fbe8f2ad558c7b75023fd07992cb97cb073117e52

Observation ac6af2dd-7905-4793-b791-1cf873b1cb59 · outbound

This paper cites T., and Huang, G.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol T., and Huang, G

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:23.049198Z

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.

source=arxiv_source observed=2026-08-07T00:40:22.248540Z digest=sha256:e664236ab652d173524f6c264f12388f8d5804d7c652f8b089f7d34e20cedabd

Observation d3d5afcf-68c0-4197-8519-1c0a8ef874ba · outbound

This paper cites C., Supriya, Y., Srivastava, G., Maddikunta, P.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol C., Supriya, Y., Srivastava, G., Maddikunta, P

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:40:22.918850Z

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.

source=arxiv_source observed=2026-08-07T00:40:22.280043Z digest=sha256:9e91a4c6a9e797ed5a9817efe42558f6eec9a8ff004aadc45c3ee9fece9e2d9d

Observation ff71a4f4-6420-43c0-820d-5a2fb1a02fe9 · outbound

This paper cites Scientific Large Language Models: A Survey on Biological & Chemical Domains.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Scientific Large Language Models: A Survey on Biological & Chemical Domains

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-07T00:40:22.334354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:40:22.334354Z digest=sha256:0a741a13ef39d26a07efbf82cea6c8b3d9a92ebe6bd6f6c7f9dbde698966541c

Observation 5b4c6ae0-d287-439a-9d8e-6ce1e693383a · outbound

This paper cites an unresolved cited work.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Unresolved cited work

Reference 59

Resolution
unresolved
raw_fallback, observed 2026-08-07T00:40:22.782857Z

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.

source=arxiv_source observed=2026-08-07T00:40:22.369967Z digest=sha256:21a11829d8d199869060245d5fe3c9da438fc68229b1def564795f4956c34f60

Observation 9529956f-d3cf-4215-939a-1a54c248b0e7 · outbound

This paper cites Data-Centric Foundation Models in Computational Healthcare: A Survey.

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol Data-Centric Foundation Models in Computational Healthcare: A Survey

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T00:40:22.399701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:40:22.399701Z digest=sha256:98eabede739c2835931153f1e37bd199eb1e3b0263f16a5a5dd71bafc4110ce4

Pith citing papers

Observation 38959b42-05e9-4497-a149-5b58194f371f · inbound

Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning cites this paper.

Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T11:26:39.618536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:26:39.618536Z digest=sha256:531a79973a78133f54a3669c66f513f9a1851d6195b59cdad6599215cc89b0d8

Observation 1814dfb4-2ef3-4ae0-9d73-bbd9acba5854 · inbound

Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support cites this paper.

Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol

Reference 82

Resolution
verified exact
arxiv_id, observed 2026-07-01T20:56:14.232189Z

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.

source=pdf_text observed=2026-06-28T17:35:21.127740Z digest=sha256:ac0905d86a3e1f228323ecc9fb88583b536d9d5b9b57ad1eceabfce75c49b6f5

Observation f189624c-aca9-4b9e-8d52-e28b7a7aad62 · inbound

Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineering cites this paper.

Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineering Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-07-03T02:37:34.583750Z

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.

source=pdf_text observed=2026-06-27T15:48:39.837630Z digest=sha256:fdbce76d84feabd1b710ad42ea1e27427f413dcf8c5fa654b4f58d63a66a15e3