Paper Citation Record · LEDGER

Predicting Performance of Symbolic and Prompt Programs with Examples

As of 6 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2605.21515.

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

pith.paper-citation-record.v1
2605.21515 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T01:10:01.044650Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact9
verified fuzzy1
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6840fdb3-d9fd-4ddc-ba1f-daee2e2d7def · outbound

This paper cites GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning.

Predicting Performance of Symbolic and Prompt Programs with Examples GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-05-22T01:10:51.499617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d5d4677f-57f6-4859-95a4-41a89a793366 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Predicting Performance of Symbolic and Prompt Programs with Examples Evaluating Large Language Models Trained on Code

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-22T01:10:51.480849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3ab4194b-123c-4cca-95c3-54e53a8d43ec · outbound

This paper cites On the Measure of Intelligence.

Predicting Performance of Symbolic and Prompt Programs with Examples On the Measure of Intelligence

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-22T01:10:51.465651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ca6b8249-2f58-407c-bcf5-96ffa3433fb2 · outbound

This paper cites Holistic Evaluation of Language Models.

Predicting Performance of Symbolic and Prompt Programs with Examples Holistic Evaluation of Language Models

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-22T01:10:51.509602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7d7e74f0-daba-4236-9275-bd8ee43cafac · outbound

This paper cites State of What Art? A Call for Multi-Prompt LLM Evaluation.

Predicting Performance of Symbolic and Prompt Programs with Examples State of What Art? A Call for Multi-Prompt LLM Evaluation

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T01:10:51.491532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e21eef0d-5f96-4312-87b6-5a3c2a2e59e8 · outbound

This paper cites PredictaBoard: Benchmarking LLM Score Predictability.

Predicting Performance of Symbolic and Prompt Programs with Examples PredictaBoard: Benchmarking LLM Score Predictability

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-22T01:10:51.505079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bbd273dc-c414-4879-9436-6775be60b9bd · outbound

This paper cites Rethinking llm evaluation: Can we evaluate llms with 200x less data?arXiv preprint arXiv:2510.10457.

Predicting Performance of Symbolic and Prompt Programs with Examples Rethinking llm evaluation: Can we evaluate llms with 200x less data?arXiv preprint arXiv:2510.10457

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-22T01:10:51.486505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3cf58e6c-70ee-4307-bc91-615bdf42c734 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Predicting Performance of Symbolic and Prompt Programs with Examples Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-22T01:10:51.515085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 216bf09e-04a0-405f-b176-dcab6716d076 · outbound

This paper cites How predictable are large language model capabilities? a case study on big-bench.

Predicting Performance of Symbolic and Prompt Programs with Examples How predictable are large language model capabilities? a case study on big-bench

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T01:10:52.297960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cde12683-5273-462d-aedc-452a077f7ad6 · outbound

This paper cites Benchmarking Multimodal Regex Synthesis with Complex Structures.

Predicting Performance of Symbolic and Prompt Programs with Examples Benchmarking Multimodal Regex Synthesis with Complex Structures

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-22T01:10:51.471541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e1a61f1e-f36a-42f6-a825-9c303e334c6a · outbound

This paper cites URL https://www.nature.com/articles/ s41586-024-07930-y.

Predicting Performance of Symbolic and Prompt Programs with Examples URL https://www.nature.com/articles/ s41586-024-07930-y

Reference 11

Resolution: verified exact; doi, observed 2026-05-22T01:10:51.139835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cc955960-89bf-4a9c-9806-74e1f0a7db4f · outbound

This paper cites Predictable Artificial Intelligence.

Predicting Performance of Symbolic and Prompt Programs with Examples Predictable Artificial Intelligence

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-22T01:10:51.476097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

No inbound Pith citation observations are available.