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Paper Citation Record · LEDGER

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard

As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 2 inbound Pith citation observations for arXiv:2504.13125.

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

pith.paper-citation-record.v1
2504.13125 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:17:45.905315Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T14:50:35.584259Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T03:47:35.353068Z

Reference resolution

20 of 20 outbound references displayed

  • verified exact0
  • verified fuzzy5
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 188331ce-7c88-493b-b32d-f0f65b894c01 · outbound

This paper cites FinBen: A Holistic Financial Benchmark for Large Language Models.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard FinBen: A Holistic Financial Benchmark for Large Language Models

Reference 1

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no resolver link, observed 2026-08-16T12:17:45.793428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.793428Z digest=sha256:c751fe6d7a2ef7275a1c449ece5fb494779fba569ab86367fde5e97bd96496da

Observation 6b78aef5-f10d-4aee-9141-f74c44306074 · outbound

This paper cites Fnspid: A comprehensive financial news dataset in time series,.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard Fnspid: A comprehensive financial news dataset in time series,

Reference 2

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no resolver link, observed 2026-08-16T12:17:45.800089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.800089Z digest=sha256:93f36ccbd4cc60a7ba5699d7709e908dcc6b3cfece010a1152634e3bb065651a

Observation 11965bf8-c32f-4979-b0dd-a932f5f6c6a8 · outbound

This paper cites FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning

Reference 3

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no resolver link, observed 2026-08-16T12:17:45.806468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.806468Z digest=sha256:800b04117b362774afaa1e3ea11175436f3fc14152f7478ded8971e9b7210c81

Observation 4e219550-0e9f-4676-a0ff-3390db1d4a8a · outbound

This paper cites Dynamic Datasets and Market Environments for Financial Reinforcement Learning.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard Dynamic Datasets and Market Environments for Financial Reinforcement Learning

Reference 4

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no resolver link, observed 2026-08-16T12:17:45.814251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.814251Z digest=sha256:a79017eab1c2821d77c950aafdd08cac0b6ec0fd1eeb39f52d794bddc80132c4

Observation 229e52d3-59ef-43f2-b814-5ce14b3002de · outbound

This paper cites FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents

Reference 5

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no resolver link, observed 2026-08-16T12:17:45.820478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.820478Z digest=sha256:9ac923e36de63cd7ac528b3e849db1a869509bb299ccf1d66cd3ee41b392f99c

Observation d5101762-6c85-4411-bd89-31e5c412a033 · outbound

This paper cites A Report on Financial Regulations Challenge at COLING 2025.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard A Report on Financial Regulations Challenge at COLING 2025

Reference 6

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unresolved
no resolver link, observed 2026-08-16T12:17:45.827108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.827108Z digest=sha256:2b923a4a7da82358b7263d2c3f018a2cd5fc48f8c72ab8f4680da2d930f95226

Observation 82753bad-c8f8-46db-8d61-b7f827c21c06 · outbound

This paper cites FinMind-Y-me at the regulations challenge task: Financial mind your meaning based on THaLLE,.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard FinMind-Y-me at the regulations challenge task: Financial mind your meaning based on THaLLE,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:17:46.295063Z

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.

source=pdf_text observed=2026-08-16T12:17:45.833687Z digest=sha256:35feb29e1072af0ba38f8fb617021db52fa339065cecbcc7fa8e00de6e1bafbe

Observation d83a1af4-79ad-4f1e-b4c0-b4e132b27a2b · outbound

This paper cites Simulating financial market via large language model based agents,.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard Simulating financial market via large language model based agents,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:17:46.279129Z

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.

source=pdf_text observed=2026-08-16T12:17:45.838785Z digest=sha256:232134015d9c4dc014e2b3601df3b2392b9d850f2a65187f155b2d4b3c560c44

Observation a1fcd4f6-0542-456d-95eb-fc48f6b52127 · outbound

This paper cites EconAgent: Large language model-empowered agents for simulating macroeconomic activities,.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard EconAgent: Large language model-empowered agents for simulating macroeconomic activities,

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-16T12:17:46.260788Z

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.

source=pdf_text observed=2026-08-16T12:17:45.850474Z digest=sha256:ee485265784c2135f4a8b625b4bf1432f1e66d7b42fd8f1b699f18c5d9f9c1d1

Observation a5afd846-0207-4cbd-b7c0-d95c3c3f7946 · outbound

This paper cites The Curious Case of Neural Text Degeneration.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard The Curious Case of Neural Text Degeneration

Reference 10

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no resolver link, observed 2026-08-16T12:17:45.855162Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.855162Z digest=sha256:8486816ff4ce14b1700eb9f19c763a8a507c5948373d83d752be89b3812725c7

Observation 4f7090f0-6808-463e-8a83-74b13e629484 · outbound

This paper cites Learning dynamics of llm finetuning,.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard Learning dynamics of llm finetuning,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:17:46.243478Z

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.

source=pdf_text observed=2026-08-16T12:17:45.860679Z digest=sha256:6396822190010e3077a624470cde3cb443bee94dbbe3dc69990017a916903cbd

Observation c753810d-78c8-49bf-9b75-4e84a8d3a98f · outbound

This paper cites Llamafactory: Unified efficient fine-tuning of 100+ language models,.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard Llamafactory: Unified efficient fine-tuning of 100+ language models,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T12:17:46.224649Z

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.

source=pdf_text observed=2026-08-16T12:17:45.871026Z digest=sha256:5f18e9496146389b32f4c3f2faba03273e68d5219326a992f865081676a4a177

Observation 64296b61-f5c8-463f-ba56-7402044be34e · outbound

This paper cites Deepspeed ulysses: System optimizations for enabling training of extreme long sequence transformer models,.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard Deepspeed ulysses: System optimizations for enabling training of extreme long sequence transformer models,

Reference 13

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no resolver link, observed 2026-08-16T12:17:45.882781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.882781Z digest=sha256:a1fe73e78cb2736f4fce28505002ae7437e5823a8f3c89cd929053374f0f2998

Observation cc41c489-6201-4ace-8f27-240c3152507a · outbound

This paper cites Scaling Laws for Neural Language Models.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard Scaling Laws for Neural Language Models

Reference 14

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no resolver link, observed 2026-08-16T12:17:45.893869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.893869Z digest=sha256:9652c5138995ec6ba5e5634de8d8cb399f972ad73f8cd0b9177bb0502f2156e5

Observation 7c20448d-ff8d-43d1-9d1a-8e88472afbcd · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 15

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no resolver link, observed 2026-08-16T12:17:45.876436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.876436Z digest=sha256:bc5ee37d4cd89e893ed8107924796d1b277aa1f455c971888408680390b190b6

Observation a8e71cbe-e40c-4ca9-bd98-93544cc50ab1 · outbound

This paper cites Phase Transitions in Large Language Models and the $O(N)$ Model.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard Phase Transitions in Large Language Models and the $O(N)$ Model

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-16T12:17:45.905315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.905315Z digest=sha256:4bfd84369493393dddf765996d177e7c39584670fb35b70f76780f22efe043d8

Observation 6a08e271-d2a1-4688-b251-b77dd1f337ae · outbound

This paper cites Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

Reference 19

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.899473Z digest=sha256:1d90d757eab25166cab3eabe4f9adfdb4cf177c4c47ae7397ceabc7a1493483c

Observation dc9838ca-6eee-4107-ae08-682dad674e0b · outbound

This paper cites DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

Reference 2023

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.887589Z digest=sha256:c19871e73316f298a7464d690a18f81377923363bdc1e407b28a1d9fef01b9b7

Observation e101047a-f196-4318-9c9d-29fc91ea7255 · outbound

This paper cites Simulating Financial Market via Large Language Model based Agents.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard Simulating Financial Market via Large Language Model based Agents

Reference 2024

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unresolved
no resolver link, observed 2026-08-16T12:17:45.844245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.844245Z digest=sha256:ebd2c1ff5e4530b26a03effc70afa5c30d0209f62139c844f58637081a61eeab

Observation 38ee6ecc-740e-4f64-9bd5-510abd10105a · outbound

This paper cites Learning Dynamics of LLM Finetuning.

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard Learning Dynamics of LLM Finetuning

Reference 2025

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unresolved
no resolver link, observed 2026-08-16T12:17:45.865908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:17:45.865908Z digest=sha256:d7b878158f36eb3b79d3fb80fa445357c0e1e7ff364c7f82d23c62c9603faeda

Pith citing papers

Observation 90aeffae-f90f-47c0-914c-53222fffaf2f · inbound

Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning cites this paper.

Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard

Reference 7

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verified exact
arxiv_id, observed 2026-05-15T04:59:45.903015Z

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.

source=pdf_text observed=2026-05-15T04:55:24.482203Z digest=sha256:611bda4ca6b602d522b4df23142519e76b7ee2ca8d4b602ea8c2de5a74bf8b7f

Observation e754a0b9-0fea-4504-90d2-627be635d519 · inbound

FMplex: Model Virtualization for Serving Extensible Foundation Models cites this paper.

FMplex: Model Virtualization for Serving Extensible Foundation Models LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard

Reference 56

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verified exact
arxiv_id, observed 2026-07-03T03:47:35.354539Z

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.

source=pdf_text observed=2026-06-27T14:50:35.584259Z digest=sha256:64a3300ceea96fabd6069b3c43f6404607b9b44c4bb396a2c0f41f2e005ec3a0