Paper Citation Record · LEDGER

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation

As of 7 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.00718.

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

pith.paper-citation-record.v1
2507.00718 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:15:10.572025Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

30 of 30 outbound references displayed

verified exact0
verified fuzzy10
unresolved17
parse uncertain0
malformed identifier2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 90531137-a916-4bd6-9c25-2ffc85a383a8 · outbound

This paper cites InProceedings of the 2024 Joint In- ternational Conference on Computational Linguis- tics, Language Resources and Evaluation (LREC- COLING 2024), pages 10124–10145.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation InProceedings of the 2024 Joint In- ternational Conference on Computational Linguis- tics, Language Resources and Evaluation (LREC- COLING 2024), pages 10124–10145

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:15:37.363145Z

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Observation 5be92e1a-b1a9-4a16-be0c-8ced10cf3c6d · outbound

This paper cites an unresolved cited work.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:15:37.292345Z

Source-reported events for the cited work

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Observation f2b8abc4-e189-4218-9ee8-66a48cca7bdb · outbound

This paper cites BADGE: BADminton report Generation and Evaluation with LLM.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation BADGE: BADminton report Generation and Evaluation with LLM

Reference 5

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Source-reported events for the cited work

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Observation 1cf3395a-c10d-4ddc-8449-da3b012d91d7 · outbound

This paper cites The Llama 3 Herd of Models.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation The Llama 3 Herd of Models

Reference 6

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Observation b1225094-d564-42f9-9844-36f267d40443 · outbound

This paper cites Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark

Reference 7

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Observation c92c9f2f-38e8-4a68-8f9a-3be0a2615591 · outbound

This paper cites LLM-based NLG Evaluation: Current Status and Challenges.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation LLM-based NLG Evaluation: Current Status and Challenges

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T21:15:09.677820Z

Source-reported events for the cited work

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Observation d2b15512-0789-4b18-865e-b8d44fce0b20 · outbound

This paper cites InPro- ceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13190– 13200.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation InPro- ceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13190– 13200

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:15:37.243807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 830e0c57-6bb6-4f65-910c-312cf4173c1b · outbound

This paper cites InProceedings of the 2024 Joint International Conference on Compu- tational Linguistics, Language Resources and Evalu- ation (LREC-COLING 2024), pages 773–783.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation InProceedings of the 2024 Joint International Conference on Compu- tational Linguistics, Language Resources and Evalu- ation (LREC-COLING 2024), pages 773–783

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:15:37.205020Z

Source-reported events for the cited work

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Observation cc6c5d5b-182a-49aa-8b0b-487092318186 · outbound

This paper cites InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 408–422.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 408–422

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:15:37.139115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4df87185-9c90-4a76-be6b-d3af725daa9b · outbound

This paper cites FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models

Reference 12

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Observation 62a60b45-325f-41c0-9435-b3e584e66bd0 · outbound

This paper cites InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2511–2522.

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:15:37.055417Z

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Observation 2fbfc671-1628-4df9-86f3-aac6645d56a4 · outbound

This paper cites A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges

Reference 15

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Source-reported events for the cited work

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Observation d0e1f1b5-8b91-4c50-8bdd-fe6717a4b41d · outbound

This paper cites GPT-4 Technical Report.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation GPT-4 Technical Report

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T21:15:09.978545Z

Source-reported events for the cited work

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Observation 200d6284-d1df-4565-bd25-dcceeb2e4f5d · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Gemini: A Family of Highly Capable Multimodal Models

Reference 17

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Observation 7972e8d4-24e8-46f6-88fd-8c63440b8d97 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 18

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Observation c5a5af36-0502-40ee-b9b8-add8c5f28a16 · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation BloombergGPT: A Large Language Model for Finance

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T21:15:10.113038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c92b1f75-0204-41f2-8b25-38adcf944d88 · outbound

This paper cites Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T21:15:10.164820Z

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Observation b16bd4ea-ebdf-4c96-8486-a29e3b9a0328 · outbound

This paper cites Closing Prices: Date Close 2020-04-28 8607.7 2020-04-29 8914.7 2020-04-30 8889.6 2020-05-01 8605.0 Task: Long report generation with numerical technical indicators.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Closing Prices: Date Close 2020-04-28 8607.7 2020-04-29 8914.7 2020-04-30 8889.6 2020-05-01 8605.0 Task: Long report generation with numerical technical indicators

Reference 21

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Observation 661f2233-6b49-4a4f-b6ee-6120f916499c · outbound

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AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:15:36.792167Z

Source-reported events for the cited work

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Observation 9ac865d2-4baf-4ffa-82f4-c70a0e984392 · outbound

This paper cites Despite these challenges, occasional recoveries occurred, indicating investor resilience.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Despite these challenges, occasional recoveries occurred, indicating investor resilience

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:15:36.739322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a40c60ae-10f5-4958-899d-159eb7c3588c · outbound

This paper cites an unresolved cited work.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:15:36.683572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 86b4344c-c8ab-4586-a7a2-c313d53c2f9f · outbound

This paper cites From July 2021 to December 2021, the index experienced a general upward trajectory, increasing from approximately 4250 to 4800.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation From July 2021 to December 2021, the index experienced a general upward trajectory, increasing from approximately 4250 to 4800

Reference 25

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7e342364-0504-4264-acf4-bfb0c0e3dd96 · outbound

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AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Unresolved cited work

Reference 27

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Source-reported events for the cited work

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Observation 57bb3de0-8c8f-4473-ad1f-fa721b4b7c77 · outbound

This paper cites However, a modest recovery began in early December , as the index rebounded to close at 94.2 by December 25, 2024.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation However, a modest recovery began in early December , as the index rebounded to close at 94.2 by December 25, 2024

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5bd2176c-f21c-4c89-b060-828bbad45fd9 · outbound

This paper cites Despite this, the GMI remains relatively stable, exhibiting a volatility coefficient of 1.21 , indicating moderate price fluctuations.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Despite this, the GMI remains relatively stable, exhibiting a volatility coefficient of 1.21 , indicating moderate price fluctuations

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:15:11.698614Z

Source-reported events for the cited work

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Observation ce0d6978-1997-4953-8ff9-325b4a1463ac · outbound

This paper cites From January 1 to January 10, the index saw a gradual increase in closing prices, reaching a peak of 94.1.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation From January 1 to January 10, the index saw a gradual increase in closing prices, reaching a peak of 94.1

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:15:11.613289Z

Source-reported events for the cited work

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Observation adb8cc80-a5ef-49cd-a8fd-2b3cd5eb27f8 · outbound

This paper cites The index then experienced a correction, dropping below 4,300 by the end of November.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation The index then experienced a correction, dropping below 4,300 by the end of November

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:15:12.040872Z

Source-reported events for the cited work

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Observation 11e995a6-d1f9-4dd8-989a-3c48d5066b71 · outbound

This paper cites an unresolved cited work.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:15:36.940548Z

Source-reported events for the cited work

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Observation 5c1581f7-ee77-47d1-a692-6101a01684a1 · outbound

This paper cites Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams

Reference 2023

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Source-reported events for the cited work

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Observation 1b17a951-c123-4065-9ddd-cf447dac38aa · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T21:15:09.406027Z

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Unavailable: canonical work link unavailable.

Pith citing papers

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