Paper Citation Record · LEDGER

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data

As of 19 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 2 inbound Pith citation observations for arXiv:2505.24622.

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

pith.paper-citation-record.v1
2505.24622 v3

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:23:11.282302Z

measured 22 of 22 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:18:35.816193Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T15:31:33.630501Z

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy3
unresolved15
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d65659c8-3d5d-4eea-8d02-2d9ce8a1fe6a · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:13.132267Z

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.

Observation 960ae1b1-2de4-42d0-8c58-a10f6d323326 · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:13.030121Z

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.

Observation 93206b2e-6ca6-4fec-af3f-575973672f2a · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:12.880245Z

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.

Observation 4fd7ddfd-840e-4dec-838e-bf4895479bef · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:12.807888Z

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.

Observation 277d60f3-b100-4ac3-afee-d2d53dffde76 · outbound

This paper cites As a result, they were excluded from the final ensemble.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data As a result, they were excluded from the final ensemble

Reference 5

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T12:23:11.512170Z

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.

Observation 58a5aa32-f877-453c-bc83-9dff736edaab · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:12.204480Z

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.

Observation 3bb51adc-708a-4d1f-ac1e-c03f879914ab · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:12.033274Z

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.

Observation 67b01c7b-d0cf-4ded-b7e4-0124e90dd485 · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:11.918976Z

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.

Observation 1ce41b93-45b6-4af3-96dd-c2da88639642 · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:11.789693Z

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.

Observation 8c2b3b2a-a183-47a4-b66f-fb91839da09f · outbound

This paper cites Compute Resources and Cost Table 3 details the sequential runtime and API cost for evalu- ating 8,500 founder profiles across different models.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Compute Resources and Cost Table 3 details the sequential runtime and API cost for evalu- ating 8,500 founder profiles across different models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:11.723625Z

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.

Observation 07603caa-73bd-44e2-bcbd-022779ffd68e · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:12.751675Z

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.

Observation 710c83ae-3cda-4ce5-a9da-96ca274acc01 · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:12.682793Z

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.

Observation a546d38f-a1bd-448a-b558-612a67032628 · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:12.608246Z

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.

Observation 9f6b8131-9bd7-4e3f-81ee-a13835a90636 · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:12.479487Z

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.

Observation df1e042f-4a4f-4bda-a776-85871485c095 · outbound

This paper cites an unresolved cited work.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:12.300411Z

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.

Observation 59e978bd-aad8-4727-9b5c-7058fce47873 · outbound

This paper cites Finding the unicorn: Predicting early stage startup success through a hybrid intelligence method.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Finding the unicorn: Predicting early stage startup success through a hybrid intelligence method

Reference 2005

Resolution: unresolved; no resolver link, observed 2026-08-07T12:23:10.234560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a784835d-8c25-4d04-a234-92057d178e3e · outbound

This paper cites Jan Ruben Zilke, Eneldo Loza Menc´ıa, and Frederik Janssen.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Jan Ruben Zilke, Eneldo Loza Menc´ıa, and Frederik Janssen

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:13.386469Z

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.

Observation 79f18ec2-92f3-4437-9e1b-d0e22cb24252 · outbound

This paper cites InDiscovery Science: 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings 19.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data InDiscovery Science: 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings 19

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:13.280316Z

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.

Observation a9fc4e8d-075d-4bdc-81aa-d800c1070f20 · outbound

This paper cites Founder-GPT: Self-play to evaluate the Founder-Idea fit.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Founder-GPT: Self-play to evaluate the Founder-Idea fit

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T12:23:10.385614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fee785b2-703e-4e63-96e4-211f0974653e · outbound

This paper cites Tree Prompting: Efficient Task Adaptation without Fine-Tuning.

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data Tree Prompting: Efficient Task Adaptation without Fine-Tuning

Reference 2025

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T12:23:11.622635Z

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.

Pith citing papers

Observation 8bcef609-683c-404e-ae0d-fd3606f83619 · inbound

From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital cites this paper.

From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T21:18:35.816193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e1704e8-570c-4114-8993-f0fdb6f5075d · inbound

VCBench: Benchmarking LLMs in Venture Capital cites this paper.

VCBench: Benchmarking LLMs in Venture Capital Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-24T02:14:46.102041Z

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.