Pith. sign in

Paper Citation Record · LEDGER

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities

As of 19 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2411.18071.

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

pith.paper-citation-record.v1
2411.18071 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:35:21.994799Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:33:46.881977Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

22 of 22 outbound references displayed

  • verified exact0
  • verified fuzzy4
  • unresolved18
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

Observation 95cc6aa1-b101-4f66-9355-babaafed77be · outbound

This paper cites Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-12T11:35:21.894747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:35:21.894747Z digest=sha256:c1f09cb1188ac32e42c8927cefacf30d6fb5b7fb9229c17b0a9c1cbab267fcbc

Observation c5e97fdd-1471-4b30-9459-4c3a95821865 · outbound

This paper cites P.; Busby, E.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities P.; Busby, E

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:35:22.364207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T11:35:21.900625Z digest=sha256:3165429253d0a345c5f3962a6dd83ba08a0ee5071ccc5608f03244d7a5d16778

Observation 65229c64-a9a0-49cf-89d3-32e994cd80cf · outbound

This paper cites an unresolved cited work.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-12T11:35:22.349303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T11:35:21.905371Z digest=sha256:3769d4bbac0a0e4d51aee08195639eabad6ffe7ae18827ce7508d69f28e2ed05

Observation 6618433f-3cff-4245-9b5a-46f28998c69b · outbound

This paper cites Crawling the Internal Knowledge-Base of Language Models.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Crawling the Internal Knowledge-Base of Language Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-12T11:35:21.910015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:35:21.910015Z digest=sha256:97a249fe84ecfac5ae095e807aa8e4c63a5713ba75c3dcb1b6f66fad90d28d4a

Observation ed3c40cb-5da2-40f3-8005-ddc10a306082 · outbound

This paper cites an unresolved cited work.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Unresolved cited work

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-12T11:35:21.915411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:35:21.915411Z digest=sha256:ddbb7dd9c7148cf31c6ee8c07144e65657109c30508b3f39e65f5a797aa7be0e

Observation 23fd6168-a9fc-45ed-bc3e-933ffaa11902 · outbound

This paper cites T.; Hooi, B.; and Lipani, A.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities T.; Hooi, B.; and Lipani, A

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:35:22.334251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T11:35:21.920070Z digest=sha256:467fa4c7cd3f20ac31ed03a2b520b0d60ccdab2e912c2b24923ec3d130021f0e

Observation cea195db-fd96-48e1-b076-754e94458843 · outbound

This paper cites an unresolved cited work.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-12T11:35:22.319169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T11:35:21.925224Z digest=sha256:e7f8bbcef430bdbafa856ef8cc77e0306387ac87f337748ceb9129c6e0ada9d9

Observation bdf9338e-da0b-445e-a892-9c796052b800 · outbound

This paper cites Literature Meets Data: A Synergistic Approach to Hypothesis Generation.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Literature Meets Data: A Synergistic Approach to Hypothesis Generation

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-12T11:35:21.929647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:35:21.929647Z digest=sha256:1648deee4ca5a5e3f9c4e4df84e3721a42c2ce16809c76f3ea0cd3d4670bf7a5

Observation 9f04d3cd-456a-4fe6-bd11-1505e4225478 · outbound

This paper cites The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-12T11:35:21.934371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:35:21.934371Z digest=sha256:a14f925e88d6bdb9e4412425f4205dd0539ebbc414b42ee823f0e93c4ceecc65

Observation 3aae2981-8bdc-460c-9bd8-649c8f93d667 · outbound

This paper cites Machine Learning for Synthetic Data Generation: A Review.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Machine Learning for Synthetic Data Generation: A Review

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-12T11:35:21.939254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:35:21.939254Z digest=sha256:e785e9bc8096f8d76efdd488d802abd7674b2b066e9306f7185f8aad32a166c7

Observation 2d7466cb-ff31-41e7-82d4-9f4e19dbb847 · outbound

This paper cites Self-Refine: Iterative Refinement with Self-Feedback.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Self-Refine: Iterative Refinement with Self-Feedback

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T11:35:21.944111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:35:21.944111Z digest=sha256:19a30287051b5543e273c301472f6ed48e5c48a2695c04f639fe406ba0574b3a

Observation 2fbfbbf8-6da1-4497-8609-8f2a591fbf95 · outbound

This paper cites an unresolved cited work.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-12T11:35:22.303624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T11:35:21.948671Z digest=sha256:7a6e9651e5170c92f0b193da6e63879fa5dbe8d5978d971d84ec5d97da334a7e

Observation 629c5000-842b-4667-93ea-fd3e61ea4380 · outbound

This paper cites Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-12T11:35:21.953300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:35:21.953300Z digest=sha256:ed6edd02d37b5acbdebc6047b3a2bd9c2913ddb4a9d99811c57149c0e828b4b5

Observation dbd148d8-9d05-4fb8-80a1-763bc1683ccc · outbound

This paper cites H.; and Knafo, A.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities H.; and Knafo, A

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:35:22.288531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T11:35:21.957852Z digest=sha256:5e8846d930a6c13c407e9447c452a7b4a30cc5f42756b8cdeb3b7d75621d7594

Observation 8d0313ce-95bf-4234-862a-529f4cdba237 · outbound

This paper cites an unresolved cited work.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Unresolved cited work

Reference 15

Resolution
unresolved
raw_fallback, observed 2026-08-12T11:35:22.271951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T11:35:21.962588Z digest=sha256:b5a51ef445c72c99f63cfb663c1bccc8e5de2e6243d15377497ad4036beae92d

Observation d30b19ff-381c-4372-a335-30f25123acca · outbound

This paper cites an unresolved cited work.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Unresolved cited work

Reference 16

Resolution
unresolved
raw_fallback, observed 2026-08-12T11:35:22.254592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T11:35:21.967143Z digest=sha256:719bab8ebb76fd1a92c4671a23af28be4257bb91c939304d7fb216a4552014ed

Observation 505de62b-c220-4ea8-bb97-dbfb684f7f53 · outbound

This paper cites O.; Lehman, J.; and Soros, L.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities O.; Lehman, J.; and Soros, L

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:35:22.239129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T11:35:21.971793Z digest=sha256:c2ab11031bc967d695fbca59706ae82f4c3dd54469aec7c2c4691371f2df07e0

Observation a65c2db4-7547-42b4-8aea-0f55eb7236b7 · outbound

This paper cites an unresolved cited work.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-12T11:35:22.223221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T11:35:21.976611Z digest=sha256:323a7f3622a83863c2c834c2152dc8f13e9b94d2e2a5c8a28178fb45c70fc5b6

Observation 52867b05-6d16-4484-baa6-98fd4412e35d · outbound

This paper cites an unresolved cited work.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Unresolved cited work

Reference 19

Resolution
unresolved
raw_fallback, observed 2026-08-12T11:35:22.207176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T11:35:21.981216Z digest=sha256:751e90aed19442b5f4c5d3840bcf7e68a4af6b33d6894e12598e54607eda4acc

Observation 961b543e-b5c0-4e8b-aebb-4a7f4d21713f · outbound

This paper cites an unresolved cited work.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Unresolved cited work

Reference 20

Resolution
unresolved
raw_fallback, observed 2026-08-12T11:35:22.191326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-12T11:35:21.985566Z digest=sha256:6f2a5c2c5fcc58ab5bdfa71a8bc0a2203115d4aee009367f148558f5f251efbd

Observation 99bd1795-8252-4e9c-b054-6727e259ea6f · outbound

This paper cites Improving Scientific Hypothesis Generation with Knowledge Grounded Large Language Models.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Improving Scientific Hypothesis Generation with Knowledge Grounded Large Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-12T11:35:21.990325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:35:21.990325Z digest=sha256:39604b456779ec2e093047a6c63aa12b992486bfdbe8274313e04e9459958fbe

Observation fa9185ce-1ce4-40c2-a5c0-01e631b40eb0 · outbound

This paper cites Hypothesis Generation with Large Language Models.

Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities Hypothesis Generation with Large Language Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-12T11:35:21.994799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:35:21.994799Z digest=sha256:95ae543039bf060290800c4cf9adcb877555b91c9ac71f062efe2d8dc554d2ca

Pith citing papers

Observation 5e30115b-aec0-41f0-a9cf-ab45f6fbd2a3 · inbound

Interestingness First Classifiers cites this paper.

Interestingness First Classifiers Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities

Reference 66

Resolution
verified exact
local_arxiv, observed 2026-08-05T15:33:47.288314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:33:46.881977Z digest=sha256:0c27dfb9a7bb8068ef2755de4f8e194745be331ded31fa9266b4ff8cb053c088