Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:23:11.282302Z
Paper Citation Record · LEDGER
Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data
As of 16 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T21:18:35.816193Z
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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+00:00.