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Paper Citation Record · LEDGER

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test?

As of 8 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2507.10576.

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

pith.paper-citation-record.v1
2507.10576 v2

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:24:54.245706Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

58 of 58 outbound references displayed

  • verified exact5
  • verified fuzzy27
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bd480f45-3c14-47fc-ba1d-4638e2847c4f · outbound

This paper cites MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:28.036402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:28.036402Z digest=sha256:c1ad7dcb67f7a6e4c7dc7bb6c3e99905298d3f13ed3c40515a81ce8485957b45

Observation 87ce4ee5-14b4-4e21-9bdd-59fc431d5d02 · outbound

This paper cites World Patent Information 37:3--13.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? World Patent Information 37:3--13

Reference 2

Resolution
verified exact
doi, observed 2026-08-06T18:24:54.929583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.362354Z digest=sha256:9f2260ab14381e7b23600bdcedbefa2ffc293e612291e8ee109f8ae843b47ae1

Observation 97e0d213-1df1-470d-9906-244202cbfa1e · outbound

This paper cites Claude-3 Model Card.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Claude-3 Model Card

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:25:19.589827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.454546Z digest=sha256:ce8f3d77bb8adbb9684eac1547b8c5eb29f0dda7afa20b79991935c593094696

Observation b6421edb-c960-491e-b099-533a724c6e38 · outbound

This paper cites an unresolved cited work.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:25:19.573174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.499448Z digest=sha256:c4a8ae096b2833634c92e1c962c942eb4759d19c41b3428be409c3957be2ea0a

Observation 20e1ee75-cbd3-485d-9f32-a4450a553aa2 · outbound

This paper cites In: Advances in Neural Information Processing Systems, pp 1877--1901.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: Advances in Neural Information Processing Systems, pp 1877--1901

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:25:19.554120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.648001Z digest=sha256:7dfa2e9053d43279852d53c534a9a981089e3d008bec13ba5da7121a0f70960a

Observation 8e517e5c-6dcb-49b4-af18-8c604eb26f6d · outbound

This paper cites World Patent Information 80:102341.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? World Patent Information 80:102341

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:25:19.535516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.721307Z digest=sha256:61bdf7617b16918015bfe02bc7b899d703129f9dd6756c7d6a105a589c3b55be

Observation 05c13d2f-e131-4e93-bf89-1df22924b7a2 · outbound

This paper cites In: IET Conference Proceedings CP907, IET, pp 135--138.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: IET Conference Proceedings CP907, IET, pp 135--138

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:25:19.518572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.780944Z digest=sha256:b1e40f64643ef43760e526d00ea50f9a3a420926483fd07fd218a221de6de065

Observation 31485b73-dc5b-483e-86a1-ab8dbbed761b · outbound

This paper cites World Patent Information 80:102339.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? World Patent Information 80:102339

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:25:19.502313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.875149Z digest=sha256:ea4d7dd837914d3609220618c47a40fd0bf7b3a46ba6a1fe06e08ee54ef12df2

Observation f475226c-86d3-4b73-ab52-de0b2a8793e2 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Evaluating Large Language Models Trained on Code

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:50.966765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:50.966765Z digest=sha256:bca482383323df0a5402467fee0c151ec67639f3ed35cb580f21d372f4c31ec4

Observation f76f8c66-fb3e-4bf5-afb1-d20e3b3515f8 · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? PaLM: Scaling Language Modeling with Pathways

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:51.055409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:51.055409Z digest=sha256:6fc70a0c4b0aa753f12b9935afc155151808512be74b5f4c8f812a3046f7079e

Observation 40d72c2f-a7b1-48cf-8ad6-c158d49e2616 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Training Verifiers to Solve Math Word Problems

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:51.096900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:51.096900Z digest=sha256:90e118142c07060b4232b4e198edd4c414e3353211ba86dfdbd9ba7893379da6

Observation ab965656-7940-427e-863a-32d9ba8b8942 · outbound

This paper cites In: Proceedings of NAACL-HLT, pp 4171--4186.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: Proceedings of NAACL-HLT, pp 4171--4186

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:25:19.480280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.179835Z digest=sha256:fa57b03c3fc4e00efcc6115e047124b90466e6857a497681e1c5f9af7541f5f7

Observation 61768884-7ccd-416c-a0ed-dc85b49c2ae7 · outbound

This paper cites In: Kotz S, Johnson NL (eds) Breakthroughs in Statistics: Methodology and Distribution.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: Kotz S, Johnson NL (eds) Breakthroughs in Statistics: Methodology and Distribution

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:51.252433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:51.252433Z digest=sha256:60d147e72e7a1791b19576bd9ad4217f3f06ef5fa135fcfb0581d4cb34eb92be

Observation 4e9e3b21-05b9-4125-a14b-20bf67498b3d · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:51.327970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:51.327970Z digest=sha256:8d00a7fd49491c261d0328808e434075b0b59fc0a2c7c1c0231d21fc94db8025

Observation 1b6792db-cc16-4b75-9f04-d77fc292970f · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? The Curious Case of Neural Text Degeneration

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:51.397706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:51.397706Z digest=sha256:f3807c25e9282358ece788602b3c8cf04fadcdd49ccd5dac84c88cc96a32e797

Observation ae3bdaf0-2706-49d5-9f1f-d66b4bd45b74 · outbound

This paper cites Locret: Enhancing Eviction in Long-Context LLM Inference with Trained Retaining Heads on Consumer-Grade Devices.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Locret: Enhancing Eviction in Long-Context LLM Inference with Trained Retaining Heads on Consumer-Grade Devices

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:51.445421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:51.445421Z digest=sha256:1e142f0bcff82cfd5f6bcb5e61424359eee8eab6b6df0aec96e924a91137ab59

Observation 89db867d-0db6-4f4c-a980-318cea097311 · outbound

This paper cites Mistral 7B.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Mistral 7B

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:51.489855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:51.489855Z digest=sha256:77220f6600e6bcd15164433b10e8d24dc60a2874fbdd182f6d947441a4986b1b

Observation b8e20a1a-cc07-48a6-b5e9-7b9bdfc05351 · outbound

This paper cites Artificial Intelligence Review 58(7).

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Artificial Intelligence Review 58(7)

Reference 18

Resolution
verified exact
doi, observed 2026-08-06T18:24:54.768912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.541954Z digest=sha256:7e893d140b4bff83ad15a077da51cfbe7a203f0709445ea04c538e799bd765b7

Observation f76610d1-984b-4488-a31d-dd7f6cb38b96 · outbound

This paper cites arXiv:241202549.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? arXiv:241202549

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:25:19.458752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.592523Z digest=sha256:7d128425358c663305add7a95b05beb10a2a8b2a8fbf3e939ac77b9c8c904e51

Observation 64feae18-4356-436b-add4-2d8f9b59878f · outbound

This paper cites arXiv:250512568.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? arXiv:250512568

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-06T18:25:19.424068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.640447Z digest=sha256:2b9af14de524b4900650126f39df02fbfb97cf2a7a895a0c8956615965b786bc

Observation 5c2a928e-0ca5-4db4-a5ff-bea54809d6a5 · outbound

This paper cites arXiv:250511095.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? arXiv:250511095

Reference 21

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verified fuzzy
raw_fallback, observed 2026-08-06T18:25:19.390392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.711751Z digest=sha256:998af28be33b71ea512de4b4548d5c34c3cd815250e4e311b4d0961546eb9237

Observation c004a3f1-d28a-4c42-b4b9-0ac97487740f · outbound

This paper cites Can Large Language Models Generate High-quality Patent Claims?.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Can Large Language Models Generate High-quality Patent Claims?

Reference 22

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:24:55.703097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.765694Z digest=sha256:fcaed39dd357df60d373260dbbee40fc0e1660b118a21308a7da0e4a9c59c59d

Observation 99ec6d25-7796-4593-b437-69e621b502b8 · outbound

This paper cites Technovation 129:102883.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Technovation 129:102883

Reference 23

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T18:24:55.589699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.825636Z digest=sha256:29d55e8b2c38a094480d2a7c61574bd68502ede3cd20caac6f5c669102f44163

Observation 0a3a472d-f0de-4986-bbae-80481e89d608 · outbound

This paper cites arXiv:241007009.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? arXiv:241007009

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:25:19.090139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.875034Z digest=sha256:5db64442e78a9f061ce8005d86053263e1d0cfacb93476983f9c6c3c8378af6d

Observation 434e553e-4b05-439f-9b66-7ab552e715b2 · outbound

This paper cites World Patent Information 65:102035.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? World Patent Information 65:102035

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:51.915332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:51.915332Z digest=sha256:537cc4bee671a99496c92c4781b19d8cfc36b6daf103b08865d41e9f0301d797

Observation 2b3c1fdc-59aa-4fd6-86ec-b0f9ea99847d · outbound

This paper cites In: Proceedings of the 7th JURIX Doctoral Consortium (DC JURIX 2019), CEUR Workshop Proceedings, vol 2598.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: Proceedings of the 7th JURIX Doctoral Consortium (DC JURIX 2019), CEUR Workshop Proceedings, vol 2598

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:57.504722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.958270Z digest=sha256:0a859b3aa5d2044cb3edbfbd103f4fecfe05226e1401b33b6ff38dbf82a66c21

Observation 8571118b-11bb-4de8-8871-217584c0a4aa · outbound

This paper cites Artificial Intelligence and Law pp 1--44.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Artificial Intelligence and Law pp 1--44

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-06T18:24:57.281544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.065868Z digest=sha256:d5c3d56e423c6c33da4cdfca544df51900e17fb65bea41c833f9e982a337b90d

Observation 1220e631-3afe-4b16-8cd6-e362afad1459 · outbound

This paper cites World Patent Information 62:101983.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? World Patent Information 62:101983

Reference 28

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T18:24:55.346805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.156221Z digest=sha256:877c4ae59182a430d6d9661a84e1a18ca71cf6d715a24b0aaa2bc6f43e10fbf9

Observation 801457e6-322c-4359-a49c-9b4ff2f1d2dc · outbound

This paper cites In: Proceedings of ACL , pp 7871--7880.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: Proceedings of ACL , pp 7871--7880

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:57.169237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.337655Z digest=sha256:231b41b216c5d3969a1131f655c745edde7c3bc6a30caaf67351e10c0e3217f4

Observation b53af296-6345-4cb9-9db4-144e6ee2a252 · outbound

This paper cites In: Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, pp 74--81.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, pp 74--81

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:57.036117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.445861Z digest=sha256:02f0c9fa47e8ef6e1406a3bf342b1b91cc1bbbeb926e44df9720587182fb2258

Observation 98b11a33-30b5-4eef-8cfa-07fbe4ff4e8e · outbound

This paper cites DeepSeek-V3 Technical Report.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? DeepSeek-V3 Technical Report

Reference 31

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unresolved
no resolver link, observed 2026-08-06T18:24:52.521448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:52.521448Z digest=sha256:1ff99b8707654b598c0cf6b187313259f600694ae911a7fbe355d96ef7df8dc5

Observation 550a77f9-f406-4fb0-8e66-11359f5dd37c · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:52.580847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:52.580847Z digest=sha256:b91c78605c2f67637428ad655a19a05b8f5a856fef20cd8bce0ff3eb5bf23f6d

Observation 95c66574-def2-44e3-957e-660766251c26 · outbound

This paper cites Journal of the American Statistical Association 44(247):335--341.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Journal of the American Statistical Association 44(247):335--341

Reference 33

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unresolved
no resolver link, observed 2026-08-06T18:24:52.651374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:52.651374Z digest=sha256:eafe970289f91450ca75e88f4c39547f0aa3a0ee4eb1df53c960d8b8e912372e

Observation 6bf45c99-75e5-400b-990d-75e2f67ebd71 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Efficient Estimation of Word Representations in Vector Space

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:52.714665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:52.714665Z digest=sha256:68c49697bece0b27bb7f3c74bfd8194c2b8e697d4b1efd6d6e71174e10376ec5

Observation 26d6f206-15fe-4c62-8ef5-3e49b825b72a · outbound

This paper cites In: Human Language Technology: Proceedings of a Workshop, Plainsboro (New Jersey), March 8-11, 1994.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: Human Language Technology: Proceedings of a Workshop, Plainsboro (New Jersey), March 8-11, 1994

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:56.904348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.770075Z digest=sha256:5025838803279d3870e118b9c8d825f801b069cd99d2e9a015df74e2d4bf0fa5

Observation c99e53aa-840d-4ce2-8bf3-aaff58b8bc84 · outbound

This paper cites Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:52.831232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:52.831232Z digest=sha256:d16579e7eff9583369942b772bde0c69601df561af54d1a2dd332009b492c62b

Observation e822a211-4e8e-4708-bbfb-a1b74a07666c · outbound

This paper cites GPT-4 Technical Report.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? GPT-4 Technical Report

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:52.873023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:52.873023Z digest=sha256:bc7cd7ee87250666ee63a3510a0071f69bb0ee1a0f44b233fff6a077e8dd3f5e

Observation 66fcecd8-01cf-403c-ad7a-5f96a1e70340 · outbound

This paper cites In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp 311--318.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp 311--318

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:56.811929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.913956Z digest=sha256:8bc50073b8bf35a1287e834cfcac04d926261e60ec87a05bebedde8b770ce2da

Observation 74baf85c-0c55-491d-9529-dfbc5c9a1c91 · outbound

This paper cites In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1532--1543.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1532--1543

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:56.746362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.962797Z digest=sha256:11c10d1b7c7b04e9be0010bd7e36bbdb1b8a4cc370699cd9f1ad99df311312da

Observation 1eb04404-5050-4df0-87eb-5777500e1d63 · outbound

This paper cites In: Proceedings of the 6th Workshop on Patent and Scientific Literature Translation.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: Proceedings of the 6th Workshop on Patent and Scientific Literature Translation

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:56.673631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.007139Z digest=sha256:fbaf0bb4175987eec48436f73bc00adb62d83dff0346cdc06e1441b21e94f631

Observation 534c5e69-2599-40e9-88c6-2d09f9ec2f04 · outbound

This paper cites OpenAI blog 1(8):9.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? OpenAI blog 1(8):9

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:56.571014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.042233Z digest=sha256:cde023863dfca6195b2dcc8f82a639c328838ecf59f255f2e8e6151d3a7dab71

Observation ef2f93a5-30c3-4eb8-9895-d6b0874a4532 · outbound

This paper cites Journal of Machine Learning Research 21(140):1--67.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Journal of Machine Learning Research 21(140):1--67

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:56.505369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.082141Z digest=sha256:145534794f04b97ec1dafd5057e65e28081b17d86d4795935f2d1381181844c6

Observation a830ea72-e3bc-4696-b52e-3de97a8c27c0 · outbound

This paper cites Knowledge and Information Systems 61(2):631--660.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Knowledge and Information Systems 61(2):631--660

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:53.167046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:53.167046Z digest=sha256:ec5c71931ab2e3f52dc295255e2c084e2ed2d24cf703862b8fd0c3ff9eac87ab

Observation 38998cde-70eb-43fd-9fd5-3e4f4b86fd5a · outbound

This paper cites BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:53.264731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:53.264731Z digest=sha256:a668f70685395650b1b52b0fc10977b0e973df133b02377621fd8113bd5b6bfe

Observation ae643ebb-4cb8-4ac3-b8a2-73ae409a9099 · outbound

This paper cites arXiv:250115074.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? arXiv:250115074

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:56.428207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.363507Z digest=sha256:a192f7a1d1976876a88069d4ef5947f5b9d562b7ac76415dbb256388589e8753

Observation 0f39b63e-5262-45e5-ae0e-17ea9de0846d · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-1(2):164--172.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-1(2):164--172

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:53.413019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:53.413019Z digest=sha256:ba51b51886711ebcb2049aa15b1d62182b12337493c5b728343d2150fa848435

Observation 28faa3cd-a08c-4a21-9f4a-a2105fc3b5b5 · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? LaMDA: Language Models for Dialog Applications

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:53.463690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:53.463690Z digest=sha256:d350345f3d5e4d5981f04ac0901f0cd05a15ddd253211f80b0e2da82ab02f84d

Observation e0ab48ae-d410-425f-a136-f9355aa04403 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? LLaMA: Open and Efficient Foundation Language Models

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:53.516456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:53.516456Z digest=sha256:f744565c63a3e972b449738db321087f06078de457306b460dcfb5554c1bb7c3

Observation 9cb32be1-5221-4092-8a7e-178cc4404d77 · outbound

This paper cites In: International TRIZ Future Conference, Springer, pp 3--19.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: International TRIZ Future Conference, Springer, pp 3--19

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:56.360845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.558608Z digest=sha256:94632458badc903dec7e3d5b0c8914e5b81359733166bda288eeb7a95e7ef34c

Observation 3873f137-2231-4d85-8a09-faefad3fd499 · outbound

This paper cites Tex Tech L Rev 54:255.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Tex Tech L Rev 54:255

Reference 50

Resolution
verified exact
doi, observed 2026-08-06T18:24:54.566598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.599286Z digest=sha256:49626383b448bd23cc3fca72618d857098701eb56f84bf941f0698f2b8a74fab

Observation c5ed1281-f429-41dd-af33-ccdd071d6ad1 · outbound

This paper cites In: Advances in Neural Information Processing Systems, pp 5998--6008.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? In: Advances in Neural Information Processing Systems, pp 5998--6008

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:56.289944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.658248Z digest=sha256:a668e0f933232c8e9bf89e9189c2c5927c8728b20f2b6262f288cf4e518594d5

Observation b450b84a-d513-4219-81b8-f4e972fd5c86 · outbound

This paper cites arXiv:241209796.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? arXiv:241209796

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:56.223763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.731381Z digest=sha256:0a0c5d78d62f2f81882bb3cf2d965a913a877aa45fa828f88d6606d7ff0f9297

Observation 253c995b-984a-474b-9d6d-627b22f30fa3 · outbound

This paper cites arXiv:241218100.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? arXiv:241218100

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:56.121592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.821846Z digest=sha256:0fe2a4054a414948bd4370395cf8649c4cdc69eb5c5a8170b87b8442958ed52f

Observation 4d330047-6f8f-4063-9c99-859a80d144e6 · outbound

This paper cites arXiv:241021312.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? arXiv:241021312

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:56.041534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.924869Z digest=sha256:81a745673fde94b44554b80a2f37625920343b2ac83d04783ca8703548a41419

Observation 2dd498b0-95df-40d1-bab8-36d04949d872 · outbound

This paper cites Ethical and social risks of harm from Language Models.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Ethical and social risks of harm from Language Models

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:54.006800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:54.006800Z digest=sha256:db8aa9996a62218ab2dbdb79a547c29391397f63156b22d61844b87183c3c9ec

Observation a21559c2-701a-4b66-8d7e-3d458b9ce38b · outbound

This paper cites Pattern Recognition 23(5):509--528.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? Pattern Recognition 23(5):509--528

Reference 56

Resolution
verified exact
doi, observed 2026-08-06T18:24:54.405546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:54.091847Z digest=sha256:35485d6e847675db0353b1f0b06904ffe4b099192c0c687b2828679f62ca9061

Observation 29ffb37a-4b30-4892-a8d4-710133db445f · outbound

This paper cites arXiv:250519345.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? arXiv:250519345

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:55.933098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:24:54.168666Z digest=sha256:d967bd5329c0d4dc42f7d2a1f7deb00e1e813fbe05fcc5d21019cff8641da59a

Observation 4a6420c7-f7a9-43b2-ade5-c3ae1d62cb6d · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test? BERTScore: Evaluating Text Generation with BERT

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:54.245706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:54.245706Z digest=sha256:ae37f3e8e3d4a3653de8cb73524a38589e8113f7b05f9a22b59563f42cf0afcc

Pith citing papers

No inbound Pith citation observations are available.