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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 18 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 2026-08-18T06:34:40.430872+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=sha256:83e7226d1476fbb09972db7a06eaaefbb1fd0f80a7dbf32fdd190216dbd74bfb

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.362354Z digest=sha256:db2965d02100ac1539cb05e14374bd7bd72396f70ec0b964f466715a67eabf5f

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.454546Z digest=sha256:61f7838c18d45242a20ed8de11b86e7ebcbe7dc5bd40d3055aac319628b1c708

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.499448Z digest=sha256:a4e3ddd486a4c19e3e8e11561507474582ea769271694141e6aedea7309b40ef

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.648001Z digest=sha256:813a7e5bbbbb34cb5dce5100249f0093ccb594f08537ca5601d86a26e3c1340b

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.721307Z digest=sha256:a9d3b732c2a3d9179637a1453e6c72b8a091c3321169a3c9c63ab04c41e8996e

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.780944Z digest=sha256:aff2b8a01858c0e115b6657dccc5be63e29a77445832662c421522458995aa8e

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:50.875149Z digest=sha256:17ea645b20f7e586ed7d00dbc34e8d6d188d9525aa340a23ce852152a64c21f0

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:c7897af221c57e14e484645b3be303849c2e775808328e188eefcdef0e026a80

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=sha256:1cacda957424700318bc6e6786245f3f440267e363c0ce29075c3e504a4ca4cd

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:f6212e62ba00cdf8794076e39434368a4f6d5cbcb0a144befb906f8633e92234

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.179835Z digest=sha256:a0a4922770142446e7bb499de20eb96ea27b6bcb955c0bd404bc941b0c5246b3

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:f5c71c253fbfa5e424547e3281e2bce871fd800f8e5c7c5abd3c52900aae9cd1

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:a08ace252122dacc7ce204805eb27eda09da21be699525b4ed5c2a8edcc7d803

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=sha256:53e9cd201a7d79981aa413c7a324778347537206399af702e03490b86404b3df

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:b7bed7cb11773b5ac60303d4c66eb61a25910225708d518542605465088c91cc

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:c27c7915271184957343b44225d95975aebb2c2ef0753bda23330ca13164550b

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.541954Z digest=sha256:f96db74616a70de952fcd7694a49c31d3882449b82d1d633cbae436a2c844706

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.592523Z digest=sha256:c5843de26ee2283d62c1139943c29d9408c043193a99197383ee96806d99ea72

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.640447Z digest=sha256:928ce0ab0ca01ca7b0d9ee77be845ef5f574bba5302503d3df57ab87716518c8

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.711751Z digest=sha256:365ec96c1a0fd9bb1c8e9d4d671f2ef19ee6171eaeb14d2969cb1bc2c397e3af

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.765694Z digest=sha256:fd0e97bf1c890f8dc436e200c965713e927e8cbae4808e7ebecc0a113edebd2b

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.825636Z digest=sha256:cfb1d2a0422ff8c5cd12b6c6593e97be86210ff50d3dec9355904726b193e865

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.875034Z digest=sha256:f80ae7e81f22e0241b015c31290f3582ef204b0daa1d1823ff73befcd5213ab3

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:bafc903553e33d33f19019dcf561f94e816a43b6ebd9c9427888a11869da3979

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:51.958270Z digest=sha256:41fc3da2733a079b19e4f0a17a2c6ac0e4d16f14cd70bf495f2e42d847bbaf90

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

Resolution
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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.065868Z digest=sha256:0046a43356079a598568a56e239ea2ab28e5cd3667c0d8841a02d766af3f54e5

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.156221Z digest=sha256:d360bcfa4f6a5002b3b1a1c76078f1591dcf638eb2ebc845911fa4df39f0f538

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.337655Z digest=sha256:7ccabdda174797a13e3443de222e1e022b8b91b0ed667733ec940ff78eedb154

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.445861Z digest=sha256:96a2b943bc385ea5f9e700cd6e30fec93924accaaba8ba3b09c7bfa65efc63a9

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

Resolution
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:b3a72072d35cb1f3a94e6264ef6deeed396290081dab5db1e070c370cbca9270

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=sha256:8b34d77f98137cff6017e0cfbf1034f08ec113aa695fddd586c8e6e70346714d

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

Resolution
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=sha256:55b149fc5dfdfc6d44e5bd5b33b4340cb92262dc8dd1cc5b92d1acdf8a9f94d5

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=sha256:134c0c12e7ebbc6e48a3ebe8aed797801b1965706f91a1c10300dffc364a8c7c

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.770075Z digest=sha256:6772ef255336ee015ff6773dace78a95b8c55ec592866b6e476f294a37b87142

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=sha256:952fd2cea62ddc29fbb5148782b98b9fc524be22b1722068b8b461d19768f58c

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:ac62bb5cf2c17718fda49defe305011a45ab9b9f268689aaefd14cee027aafd2

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.913956Z digest=sha256:44d95668d31b0bf8bac5b65b01f37d566a654589d06016e01df07d21abfe7f7c

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:52.962797Z digest=sha256:47a0b5fba4f69b08287c3b553d54e02c117569d032687f5fead3aaf0050e2f59

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.007139Z digest=sha256:81bbfe53f3b7120983c36d0ae744873e55d264b0ba2208db180c30c4afa5ddc0

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.042233Z digest=sha256:558f831950dc38b7ed228706f97c8ec6ef128af443f3ceb676a2f172b7fd8901

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.082141Z digest=sha256:83f884d64b91934c08b82f472da269ec196e5a4be5541159e29151104dfb9442

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=sha256:595e45c6dd7e33a3f2915e45cb938994020dd1ae51bbcd96d9af408fc97dc5e0

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:a2792c38fe47bfeb27ab335e081fb6321376d1856cc359bac0b2f825e85caeaf

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.363507Z digest=sha256:d4d4cadb847780812b9998ba53f9f34cbc645e774422be1911e2f77be401c25d

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=sha256:5a31b1793fcb819c62f4b5af5f0117e1426575f7afbe112d02bcd1e365443eba

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=sha256:2cf29b64c2a85cf831e7099c67bb93734a3da6b40611554a528041ce6b433a02

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:ceb5f4eb3d29f0dc32935e78da3fc3bc82feca5e34b912d2ed3b7da277d5e326

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.558608Z digest=sha256:d24f9405d05978cbfce634478b3b681a3a6d5250449e5a0df46ae7598103a2bc

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.599286Z digest=sha256:3c661628618f832b57e0111bc14391fe518f79d2dafcd5ca5e1915ef8abc1ea4

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.658248Z digest=sha256:cb264dde2497ec97210e2b95ebff4e06ece2a6f827df22205fc7e8e6ec4413a6

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.731381Z digest=sha256:f671b90ff98840dd10070de0cd7dfd05accde95ed388dc36e0f7194ff3587dc3

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.821846Z digest=sha256:96b3987169535775a8c1683dbaa27b8eb21f626556f53a618f729ccdabc9e689

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:53.924869Z digest=sha256:72c302fd5064c79246c964caac886098b89ab290a7b8cfd7d0a2f2f706a7980a

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=sha256:8752c03cc63a2f7f60f7f0a319cad48811c0c34f872f497d3bf2a95c092312f3

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:54.091847Z digest=sha256:4524835c25a8743a168564844cafcec34f36d45467207dc68802d3e1284eb0e6

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 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T18:24:54.168666Z digest=sha256:1746857e423667126d024350bc8a7778794751ded00d691c221833e0f033748f

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=sha256:5dd140990d939bb036ac36ac4793f37ddb6e880061c53e86026250f93e90d3bc

Pith citing papers

No inbound Pith citation observations are available.