Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-04T21:18:36.221181Z
Paper Citation Record · LEDGER
From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital
As of 8 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 0 inbound Pith citation observations for arXiv:2509.08140.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
7 of 7 outbound references displayed
- verified exact3
- verified fuzzy1
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 8bcef609-683c-404e-ae0d-fd3606f83619 · outbound
This paper cites Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data.
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 e30d6ca2-555c-458b-99b8-c88f4fbf9dd3 · outbound
This paper cites Founder-GPT: Self-play to evaluate the Founder-Idea fit.
From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital Founder-GPT: Self-play to evaluate the Founder-Idea fit
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-08-04T21:18:36.366912Z
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.
Observation bd4364b8-19d1-4242-a9bc-d603feb7813e · outbound
This paper cites GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees.
From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T21:18:35.909845Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8f0f1716-879e-409a-ac8d-091149832da9 · outbound
This paper cites Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques.
From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques
Reference 4
- Resolution
- verified exact
- local_arxiv, observed 2026-08-04T21:18:36.819995Z
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.
Observation a4615e8d-1287-41b3-b72c-28698c1f429f · outbound
This paper cites ZNorm: Z-Score Gradient Normalization Accelerating Skip-Connected Network Training without Architectural Modification.
From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital ZNorm: Z-Score Gradient Normalization Accelerating Skip-Connected Network Training without Architectural Modification
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-08-04T21:18:36.666186Z
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.
Observation 9bd867fe-10ca-481e-8fe8-31efc4d1ff89 · outbound
This paper cites (2021, April 26).
From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital (2021, April 26)
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-04T21:18:37.026820Z
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.
Observation d80c22ac-c018-4265-a068-fbd00f0ebdc4 · outbound
This paper cites an unresolved cited work.
From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital Unresolved cited work
Reference 7
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-08-04T21:18:36.527138Z
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.
Pith citing papers
No inbound Pith citation observations are available.