Paper Citation Record · LEDGER

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents

As of 9 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:2502.07393.

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

pith.paper-citation-record.v1
2502.07393 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:59:23.365496Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T19:35:11.289439Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T21:37:25.616774Z

Reference resolution

12 of 12 outbound references displayed

verified exact1
verified fuzzy0
unresolved10
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6eba4287-4a1d-443e-8001-aa854bd489fe · outbound

This paper cites TradExpert: Revolutionizing Trading with Mixture of Expert LLMs.

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents TradExpert: Revolutionizing Trading with Mixture of Expert LLMs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T12:59:23.326033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c25afcd-62a2-4cd8-873e-2e3dbe36fa55 · outbound

This paper cites FNSPID: A Comprehensive Financial News Dataset in Time Series.

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents FNSPID: A Comprehensive Financial News Dataset in Time Series

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T12:59:23.329571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 025b6aae-94cd-42d7-a560-598163f08a23 · outbound

This paper cites The Llama 3 Herd of Models.

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents The Llama 3 Herd of Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T12:59:23.333094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a74fefd2-db4d-4daa-af7a-357f0145dd3c · outbound

This paper cites org/abs/2304.07619.

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents org/abs/2304.07619

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T12:59:23.342838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f05a2dd-523d-422e-8499-206cbccb56ca · outbound

This paper cites Qwen2.5 Technical Report.

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents Qwen2.5 Technical Report

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T12:59:23.345987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30e8ab7c-060d-45bd-9f2b-dc7ed43ccc41 · outbound

This paper cites Financial News-Driven LLM Reinforcement Learning for Portfolio Management.

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents Financial News-Driven LLM Reinforcement Learning for Portfolio Management

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:59:23.568395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 50f3418c-3087-4439-be2e-cf1109c64dae · outbound

This paper cites an unresolved cited work.

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents Unresolved cited work

Reference 13

Resolution: malformed identifier; no resolver link, observed 2026-08-08T12:59:23.358765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eeedae85-d45f-4a04-ab64-d2fd72e17ed6 · outbound

This paper cites FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making.

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T12:59:23.365496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15ce29c5-e8c1-401b-95ae-5a5986c2de7e · outbound

This paper cites Proximal Policy Optimization Algorithms.

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents Proximal Policy Optimization Algorithms

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-08T12:59:23.349237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdcb3ed9-60ff-4153-bad7-971a7f0b0f6f · outbound

This paper cites FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance.

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-08T12:59:23.339940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e6bdf2d-955a-4019-939a-d5551c8187bb · outbound

This paper cites ChengYang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, and Jun Zhu.

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents ChengYang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, and Jun Zhu

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T12:59:23.355562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 555fb950-d288-47cd-a5bc-05525f05a5f0 · outbound

This paper cites DeepSeek-V3 Technical Report.

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents DeepSeek-V3 Technical Report

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-08T12:59:23.323090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 53a80082-c127-4bdc-b41a-caf28f8f8681 · inbound

Semantic State Abstraction Interfaces for LLM-Augmented Portfolio Decisions: Multi-Axis News Decomposition and RL Diagnostics cites this paper.

Semantic State Abstraction Interfaces for LLM-Augmented Portfolio Decisions: Multi-Axis News Decomposition and RL Diagnostics FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:55:59.204646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8e1497f6-d55d-48fa-b281-083b480753dd · inbound

Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems cites this paper.

Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T21:37:25.618339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.