From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery | Paper Citation Record

Observation ad706de9-3d00-405e-affa-71e7dad3d3ce · outbound

This paper cites AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment

Reference 1

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Observation e781bf42-1358-4299-8970-2b8d308615ba · outbound

This paper cites Alpha Mining and Enhancing via Warm Start Genetic Programming for Quantitative Investment.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Alpha Mining and Enhancing via Warm Start Genetic Programming for Quantitative Investment

Reference 2

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Observation e21024ea-f425-4410-b0cf-d675a6fba8c8 · outbound

This paper cites 101 formulaic alphas.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery 101 formulaic alphas

Reference 3

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Observation 1175895f-f6de-4c91-92cd-a73ec512ccc3 · outbound

This paper cites Multiple regression genetic programming.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Multiple regression genetic programming

Reference 4

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Observation 54b6fef3-1146-4bbe-ae80-2d70a2e05c0e · outbound

This paper cites Alpha discovery via grammar-guided learning and search.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Alpha discovery via grammar-guided learning and search

Reference 5

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Observation 31cc164d-9f20-4563-b887-be948259d8cb · outbound

This paper cites Riskminer: Discovering formulaic alphas via risk seeking monte carlo tree search.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Riskminer: Discovering formulaic alphas via risk seeking monte carlo tree search

Reference 6

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Observation ffee040b-b83d-4847-b571-9887465c511a · outbound

This paper cites Generating synergistic formulaic alpha collections via reinforcement learning.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Generating synergistic formulaic alpha collections via reinforcement learning

Reference 7

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Observation e4baaaae-e0f3-47c6-826a-6d6693f28192 · outbound

This paper cites $\text{Alpha}^2$: Discovering Logical Formulaic Alphas using Deep Reinforcement Learning.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery $\text{Alpha}^2$: Discovering Logical Formulaic Alphas using Deep Reinforcement Learning

Reference 8

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Observation bd243977-0bc9-41db-a80a-f14fece1479a · outbound

This paper cites Alphaqcm: Alpha discovery in finance with distribu- tional reinforcement learning.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Alphaqcm: Alpha discovery in finance with distribu- tional reinforcement learning

Reference 9

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Observation ba3258be-e696-4aa8-b0ba-1edb0572ddb3 · outbound

This paper cites Alphaforge: A framework to mine and dynamically combine formulaic alpha factors.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Alphaforge: A framework to mine and dynamically combine formulaic alpha factors

Reference 10

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Observation bcbcdf6a-56ff-4321-9d10-842b64754d03 · outbound

This paper cites AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration

Reference 11

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Observation dec02696-8e36-4a3d-b868-8edfc9f5239e · outbound

This paper cites A survey of aiops in the era of large language models.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery A survey of aiops in the era of large language models

Reference 12

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Observation 0eec8e55-d3d9-4b5f-9eea-6eaba84072c1 · outbound

This paper cites E-log: Fine-grained elastic log-based anomaly detection and diagnosis for databases.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery E-log: Fine-grained elastic log-based anomaly detection and diagnosis for databases

Reference 13

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Observation dbde6636-c8d6-4f6f-a217-f0b67d1d6491 · outbound

This paper cites Towards close-to-zero runtime collection overhead: Raft-based anomaly diagnosis on system faults for distributed storage system.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Towards close-to-zero runtime collection overhead: Raft-based anomaly diagnosis on system faults for distributed storage system

Reference 14

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Observation b225c227-f418-46b9-bbd1-8c8efe59ac6d · outbound

This paper cites Multivariate log- based anomaly detection for distributed database.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Multivariate log- based anomaly detection for distributed database

Reference 15

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Observation e8cce409-02a4-4da9-bae1-736d4059cd47 · outbound

This paper cites Reducing events to augment log-based anomaly detection models: An empirical study.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Reducing events to augment log-based anomaly detection models: An empirical study

Reference 16

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Observation 7262480a-dbba-419b-bcc0-14443108ba16 · outbound

This paper cites Scalalog: Scalable log-based failure diagnosis using llm.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Scalalog: Scalable log-based failure diagnosis using llm

Reference 17

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Observation bffb9279-de5b-4e4d-bfe3-d3b07fd8d513 · outbound

This paper cites AgentFM: Role-Aware Failure Management for Distributed Databases with LLM-Driven Multi-Agents.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery AgentFM: Role-Aware Failure Management for Distributed Databases with LLM-Driven Multi-Agents

Reference 18

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Observation a8ea2feb-2f05-41c8-936e-0d9ca71d2598 · outbound

This paper cites arXiv preprint arXiv:2504.18776 , year=.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery arXiv preprint arXiv:2504.18776 , year=

Reference 19

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Observation cbed4980-c3c7-4275-b7b0-935ac356ba52 · outbound

This paper cites Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices

Reference 20

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Observation 7510fb1b-d0b8-4f98-8ff8-d09fff1c83b0 · outbound

This paper cites LogDB: Multivariate Log-based Failure Diagnosis for Distributed Databases (Extended from MultiLog).

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery LogDB: Multivariate Log-based Failure Diagnosis for Distributed Databases (Extended from MultiLog)

Reference 21

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Observation 51a10d05-bad2-4bed-ae36-158f68ecd2f1 · outbound

This paper cites Xraglog: A resource- efficient and context-aware log-based anomaly detection method using retrieval-augmented generation.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Xraglog: A resource- efficient and context-aware log-based anomaly detection method using retrieval-augmented generation

Reference 22

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Observation 146b82ad-a823-4d51-9708-1dedeeb56e66 · outbound

This paper cites A survey on parallel text generation: From parallel decoding to diffusion language models.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery A survey on parallel text generation: From parallel decoding to diffusion language models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:47:36.320979Z

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Observation d2aeabda-f564-4e52-a0b1-1b231ea25bb7 · outbound

This paper cites Time-tired compaction: An elastic compaction scheme for lsm-tree based time-series database.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Time-tired compaction: An elastic compaction scheme for lsm-tree based time-series database

Reference 24

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Observation cecef55d-a8ab-4323-980d-659d71de94ea · outbound

This paper cites Separation or not: On handing out-of-order time-series data in leveled lsm-tree.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Separation or not: On handing out-of-order time-series data in leveled lsm-tree

Reference 25

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Observation b5a30f81-95e5-4415-be16-90a05fe64a64 · outbound

This paper cites Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought

Reference 26

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Observation 293a8778-4fb8-403b-9aab-84eee2fa95ed · outbound

This paper cites Ora: Job runtime prediction for high-performance computing platforms using the online retrieval-augmented language model.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Ora: Job runtime prediction for high-performance computing platforms using the online retrieval-augmented language model

Reference 27

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Observation be40c601-3745-4031-a8cf-d40a1e6c22ae · outbound

This paper cites Microremed: Benchmarking llms in microservices remediation.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Microremed: Benchmarking llms in microservices remediation

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:47:36.331849Z

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Observation 02e9ae15-a085-4bf8-b708-c5388c5d870e · outbound

This paper cites arXiv preprint arXiv:2508.07173 , year=.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery arXiv preprint arXiv:2508.07173 , year=

Reference 29

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Observation 36013edc-89f9-439d-81b8-2ba86d44f319 · outbound

This paper cites Walk the talk: Is your log-based software reliability maintenance system really reliable?.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Walk the talk: Is your log-based software reliability maintenance system really reliable?

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:47:36.257289Z

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Observation 71e4a089-2448-432b-a1d4-6acee72be0f2 · outbound

This paper cites d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models

Reference 31

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Observation b81409c5-5a0d-4953-a896-57420c71ff32 · outbound

This paper cites Cslparser: A collaborative framework using small and large language models for log parsing.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Cslparser: A collaborative framework using small and large language models for log parsing

Reference 32

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Observation 3d6c43c9-90a4-41bb-a5f2-25d05d183aa3 · outbound

This paper cites United we stand: Towards end-to-end log- based fault diagnosis via interactive multi-task learning.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery United we stand: Towards end-to-end log- based fault diagnosis via interactive multi-task learning

Reference 33

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Observation 4ddcc394-6fb3-450c-b87c-b2dfb1885b5c · outbound

This paper cites Hypothesize-then-verify: Speculative root cause analysis for microser- vices with pathwise parallelism.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Hypothesize-then-verify: Speculative root cause analysis for microser- vices with pathwise parallelism

Reference 34

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Observation 1c3cb501-4de4-4bc4-bd7a-1b923e0f2b38 · outbound

This paper cites Uda-rcl: Unsupervised domain adaptation for microservice root cause localization utilizing multimodal data.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Uda-rcl: Unsupervised domain adaptation for microservice root cause localization utilizing multimodal data

Reference 35

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Observation 0b112839-e8ac-4dae-a22a-177d80b8e05c · outbound

This paper cites Aaad: Asynchronous inter-variable relationship-aware anomaly detection for multivariate time series.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Aaad: Asynchronous inter-variable relationship-aware anomaly detection for multivariate time series

Reference 36

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Observation 803c6591-bdba-4362-a8dc-f4cb7c2120db · outbound

This paper cites Logaction: Consistent cross-system anomaly detection through logs via active domain adaptation.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Logaction: Consistent cross-system anomaly detection through logs via active domain adaptation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.623196Z

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Observation a914dd5a-e2e6-4d71-bc87-72b58fb5fa4e · outbound

This paper cites Runtimeslicer: Towards generalizable unified runtime state representation for failure management.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Runtimeslicer: Towards generalizable unified runtime state representation for failure management

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:47:36.342208Z

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Observation c5a91968-3e0f-4bb7-a8de-f55508b5c774 · outbound

This paper cites Efficient failure management for multi-agent systems with reasoning trace representation.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Efficient failure management for multi-agent systems with reasoning trace representation

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:47:36.212457Z

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Observation 79545063-c56f-426f-95f6-3602a32376a1 · outbound

This paper cites E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:47:36.289553Z

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Observation d3f61822-85be-498e-8c91-77b0af6e515b · outbound

This paper cites Coorlog: Efficient-generalizable log anomaly detection via adaptive coordinator in software evolution.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Coorlog: Efficient-generalizable log anomaly detection via adaptive coordinator in software evolution

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.639158Z

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Observation 62c3f35f-3748-4689-b30e-92f227fe6101 · outbound

This paper cites Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:47:36.250658Z

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Observation ef387fb2-e226-413d-a45a-58775ef38089 · outbound

This paper cites Alpha- gpt: Human-ai interactive alpha mining for quantitative investment.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Alpha- gpt: Human-ai interactive alpha mining for quantitative investment

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.714573Z

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Observation c7dec49a-8c68-41f3-92ac-9aa887f8d2a4 · outbound

This paper cites Can large language models mine interpretable financial factors more effectively? a neural-symbolic factor mining agent model.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Can large language models mine interpretable financial factors more effectively? a neural-symbolic factor mining agent model

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.633163Z

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Observation ec5d7af7-bc6c-4748-a0e7-bbf54841cdcd · outbound

This paper cites QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T14:47:36.325982Z

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Observation 00885b09-5999-43ce-a615-6645e3e3453b · outbound

This paper cites Al- phabench: Benchmarking large language models in formulaic alpha factor mining.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Al- phabench: Benchmarking large language models in formulaic alpha factor mining

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.700082Z

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Observation a55db157-cea9-457c-8142-9c3553a7daf3 · outbound

This paper cites Alphaagent: Llm-driven alpha mining with regularized exploration to counteract alpha decay.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Alphaagent: Llm-driven alpha mining with regularized exploration to counteract alpha decay

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.631321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a290ce47-72dd-4ce2-aa93-c54a4b1a2043 · outbound

This paper cites Navigating the alpha jungle: An llm-powered mcts framework for formulaic alpha factor mining.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Navigating the alpha jungle: An llm-powered mcts framework for formulaic alpha factor mining

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.701932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 651fba28-30d7-47e5-9510-150ee0383d0b · outbound

This paper cites R&d-agent- quant: a multi-agent framework for data-centric factors and model joint optimization.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery R&d-agent- quant: a multi-agent framework for data-centric factors and model joint optimization

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.635051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 455ff51a-0288-415d-a9fd-16ee47d07f08 · outbound

This paper cites QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:47:36.310589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4e2037a5-a597-42fa-961c-1b8900f6032a · outbound

This paper cites Deep reinforcement learning from human preferences.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Deep reinforcement learning from human preferences

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.637242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 007c2136-f782-4ffd-a8c6-58988cf53fdd · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Fine-Tuning Language Models from Human Preferences

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:47:36.239121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5bdc8b63-09bb-4124-9919-2cbb136e32f1 · outbound

This paper cites Proximal Policy Optimization Algorithms.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Proximal Policy Optimization Algorithms

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:47:36.247436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 05b2011a-ab4d-4ba5-9c25-28fee6cb0c5d · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Direct preference optimization: Your language model is secretly a reward model

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.686482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7f0d32ce-03ee-406e-aad1-4ae5d7fb3cce · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:47:36.275263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9d0fe3e8-af28-4be7-b8fd-4fec8dc024a9 · outbound

This paper cites Large language model agents in finance: A survey bridging research, practice, and real-world deployment.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Large language model agents in finance: A survey bridging research, practice, and real-world deployment

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.609754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f4702f7e-7bd3-4936-9ef8-6d649dd24be9 · outbound

This paper cites Ectsum: A new benchmark dataset for bullet point summarization of long earnings call transcripts.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Ectsum: A new benchmark dataset for bullet point summarization of long earnings call transcripts

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.642915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6e388a45-fe37-4315-b7e0-1a7f0e3a0e78 · outbound

This paper cites Ab- stractive financial news summarization via transformer-bilstm encoder and graph attention-based decoder.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Ab- stractive financial news summarization via transformer-bilstm encoder and graph attention-based decoder

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.706543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 30cb479c-64d6-4552-b948-feb20c8db348 · outbound

This paper cites Finred: A dataset for relation extraction in financial domain.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Finred: A dataset for relation extraction in financial domain

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.718588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3ed20da5-7ba5-4a7e-8c3a-4b1c0fa66307 · outbound

This paper cites Finbert: A pre-trained financial language representation model for financial text mining.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Finbert: A pre-trained financial language representation model for financial text mining

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.688371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d15e896-acde-4ef3-8f90-aea13c4d4da7 · outbound

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

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery BloombergGPT: A Large Language Model for Finance

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:47:36.260094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d529f88d-6074-4a06-8910-8ebedf170944 · outbound

This paper cites Fingp t: Open-source ﬁnancial large language models.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Fingp t: Open-source ﬁnancial large language models

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:47:36.224261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1a5d46e0-8d14-4462-91a2-321b132110d2 · outbound

This paper cites Pixiu: a large language model, instruction data and evalua- tion benchmark for finance.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Pixiu: a large language model, instruction data and evalua- tion benchmark for finance

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.690252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 128cde12-d392-43dc-bc3a-4e71e3c7effa · outbound

This paper cites InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:47:36.269386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4dc23fde-bc29-4619-8321-39a964dc5eaa · outbound

This paper cites Fintral: A family of gpt-4 level multimodal financial large language models.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Fintral: A family of gpt-4 level multimodal financial large language models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.680248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4396d6d8-cf5a-4891-aa9c-c2e22139932d · outbound

This paper cites No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:47:36.301523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba328254-a753-4da0-8037-53ec4e882af3 · outbound

This paper cites Fednlp: an interpretable nlp system to decode federal reserve communications.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Fednlp: an interpretable nlp system to decode federal reserve communications

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.692092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b863898c-4775-4238-be4c-ced62623959a · outbound

This paper cites Trillion dollar words: A new financial dataset, task & market analysis.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Trillion dollar words: A new financial dataset, task & market analysis

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.698218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7585ef5b-6ebb-449f-b62b-65e776718e4c · outbound

This paper cites Impact of news on the commodity market: Dataset and results.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Impact of news on the commodity market: Dataset and results

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.695810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 15ccc756-9e46-4bb0-abc3-b74266131a94 · outbound

This paper cites Harnessing llms for temporal data-a study on explainable financial time series forecasting.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Harnessing llms for temporal data-a study on explainable financial time series forecasting

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.708879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ea6fae76-7cd6-435e-b7a6-7c66b4c731b4 · outbound

This paper cites FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:47:36.236103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ec45ee6e-b130-4635-87f0-6dc9ceebacd6 · outbound

This paper cites GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:47:36.306912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8bf974c8-aaeb-42a8-a378-31e23e86eac9 · outbound

This paper cites Finben: A holistic financial benchmark for large language models.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Finben: A holistic financial benchmark for large language models

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.654130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 40a7e525-b480-4997-935a-105273cbf5b9 · outbound

This paper cites Investorbench: A benchmark for financial decision-making tasks with llm-based agent.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Investorbench: A benchmark for financial decision-making tasks with llm-based agent

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.614736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f1d75017-7511-4705-8df4-9700e1c982c2 · outbound

This paper cites Strux: An llm for decision- making with structured explanations.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Strux: An llm for decision- making with structured explanations

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.658442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 39f4cd51-20e1-4ae7-a915-383e9fb0d46b · outbound

This paper cites Finmem: A performance-enhanced llm trading agent with layered memory and character design.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Finmem: A performance-enhanced llm trading agent with layered memory and character design

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.716791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e25a5035-417d-4eb6-8e41-dc0f89be25e1 · outbound

This paper cites CFGPT: Chinese Financial Assistant with Large Language Model.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery CFGPT: Chinese Financial Assistant with Large Language Model

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:47:36.283958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 917f3932-ecdb-48c5-a090-434528d50501 · outbound

This paper cites When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-06-24T02:14:19.804532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation abbc0d37-cebd-456a-8597-73cefdbd7a38 · outbound

This paper cites Tradingagents: Multi-agents llm financial trading framework.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Tradingagents: Multi-agents llm financial trading framework

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.650516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e9673466-28c8-40fa-b6be-dce388eed046 · outbound

This paper cites Convfinqa: Exploring the chain of numerical reasoning in conversational finance question answering.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Convfinqa: Exploring the chain of numerical reasoning in conversational finance question answering

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.656177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5974c9a5-044a-4210-b56d-295450fafa58 · outbound

This paper cites Finder: Financial dataset for question answering and evaluating retrieval-augmented generation.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Finder: Financial dataset for question answering and evaluating retrieval-augmented generation

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.641052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d8394bc-33bf-42cf-b0ed-e3379b17d7e1 · outbound

This paper cites Al- phafin: Benchmarking financial analysis with retrieval-augmented stock- chain framework.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Al- phafin: Benchmarking financial analysis with retrieval-augmented stock- chain framework

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.646803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 11e9a090-f8ff-4b8e-9d4f-449edc6a637f · outbound

This paper cites Substituting human decision- making with machine learning: Implications for organizational learning.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Substituting human decision- making with machine learning: Implications for organizational learning

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:47:36.627568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 70ef0511-6a29-414b-9bb8-d5e933044967 · outbound

This paper cites FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:47:36.292296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b5a6723b-53a0-42af-84ef-c46f364078bd · outbound

This paper cites Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models.

From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:47:36.221245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Reference resolution

Outbound references

Pith citing papers