Paper Citation Record · LEDGER

100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2409.03563.

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

pith.paper-citation-record.v1
2409.03563 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:08:46.069427Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:36:44.887531Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 85be6463-29c7-4331-8188-af8664c70f2f · inbound

What should an AI assessor optimise for? cites this paper.

What should an AI assessor optimise for? 100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-09T19:24:07.403555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a53c8169-a05e-4f10-a958-77e1b9311bfb · inbound

Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization cites this paper.

Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization 100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T21:08:46.069427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a27f10b-e3da-4e11-8b1f-2892246834e9 · inbound

MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models cites this paper.

MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models 100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T14:08:11.562127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c101de66-854a-412a-97a9-21b2d6f251a1 · inbound

Capability Salience Vector: Fine-grained Alignment of Loss and Capabilities for Downstream Task Scaling Law cites this paper.

Capability Salience Vector: Fine-grained Alignment of Loss and Capabilities for Downstream Task Scaling Law 100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:01.431425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 942eebdc-6744-4848-a405-09fff4522e72 · inbound

On Time, Within Budget: Constraint-Driven Online Resource Allocation for Agentic Workflows cites this paper.

On Time, Within Budget: Constraint-Driven Online Resource Allocation for Agentic Workflows 100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:56:09.094860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7cac024d-b245-48a1-bb83-eb5fae5b2879 · inbound

On Time, Within Budget: Constraint-Driven Online Resource Allocation for Agentic Workflows cites this paper.

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:50:55.016079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6bcb9df7-67c6-4446-925a-c48b8ee25d64 · inbound

Efficient Benchmarking Is Just Feature Selection and Multiple Regression cites this paper.

Efficient Benchmarking Is Just Feature Selection and Multiple Regression 100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances

Reference 65

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T00:24:05.140715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cffa78bc-0b7b-42e7-bb39-54bd6342fc46 · inbound

Validity Threats for Foundation Model Research cites this paper.

Validity Threats for Foundation Model Research 100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances

Reference 74

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T07:36:44.888977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.