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Paper Citation Record · LEDGER

AQuA: Recursively Self-Improving Quantitative Trading Research Agents

As of 16 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2608.12841.

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

pith.paper-citation-record.v1
2608.12841 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:14:12.659861Z

measured 55 of 55 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 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

55 of 55 outbound references displayed

  • verified exact16
  • verified fuzzy5
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch5

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3836d9d5-9928-4fa1-a341-058b17ed44fc · outbound

This paper cites Proceedings of the 32nd International Conference on Machine Learning , series =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Proceedings of the 32nd International Conference on Machine Learning , series =

Reference 1

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raw_fallback, observed 2026-08-15T22:14:14.333081Z

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=arxiv_source observed=2026-08-15T22:14:12.367925Z digest=sha256:6e0a8687438d900f0921cb3a3ff3ae15884bddcbe8bb08994cbd56377cbfe611

Observation 35bf828a-ff72-41b6-ac1f-8ae196e2bdcc · outbound

This paper cites 2024 , doi =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents 2024 , doi =

Reference 2

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raw_fallback, observed 2026-08-15T22:14:14.314458Z

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=arxiv_source observed=2026-08-15T22:14:12.376610Z digest=sha256:8c5cb564f5defac25b6c1fe33c371b533df2f568bc63700f415ed56b426a731b

Observation 6c085224-c4c6-42d3-b6fa-f453adec84cd · outbound

This paper cites 2025 , doi =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents 2025 , doi =

Reference 3

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raw_fallback, observed 2026-08-15T22:14:14.296321Z

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=arxiv_source observed=2026-08-15T22:14:12.383235Z digest=sha256:927c7ef0db66820c4a38fe33e3461f46cc580d974f71d3df16f37b7fbcf463bf

Observation 072ebc16-3b10-4948-92cf-5eae400b8035 · outbound

This paper cites 2026 , doi =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents 2026 , doi =

Reference 4

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raw_fallback, observed 2026-08-15T22:14:14.279109Z

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=arxiv_source observed=2026-08-15T22:14:12.389067Z digest=sha256:e5a666f63c18d82f296f73e12bcc8ea9024862b2da691ba0b2c1ced2ce438d78

Observation fa62b863-8fc0-4ae7-9484-eae986c4c6d9 · outbound

This paper cites The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Reference 5

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source=arxiv_source observed=2026-08-15T22:14:12.394143Z digest=sha256:fab607565b6db4a1aeef240e7a9139a9ece868f383bed3e59951bf34d39f8452

Observation 0a1fa2b4-bba2-4e10-bf32-9399cbd7d921 · outbound

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

AQuA: Recursively Self-Improving Quantitative Trading Research Agents QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining

Reference 6

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source=arxiv_source observed=2026-08-15T22:14:12.400037Z digest=sha256:4d1332a3b0ea4120cc1e1358a7288af941355533900a16d45b14fd2c5b666250

Observation 15071457-af5a-40e9-b571-41fe5cf554a5 · outbound

This paper cites DeepLOB: Deep Convolutional Neural Networks for Limit Order Books.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

Reference 7

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no resolver link, observed 2026-08-15T22:14:12.405794Z

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source=arxiv_source observed=2026-08-15T22:14:12.405794Z digest=sha256:36ed8d4616dc8cbc7809c04cd22feeb2dd753c925ea25451b0b96341c881e3c6

Observation a267b815-0df6-422e-bdd8-a04b973116b7 · outbound

This paper cites 101 Formulaic Alphas.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents 101 Formulaic Alphas

Reference 8

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source=arxiv_source observed=2026-08-15T22:14:12.411508Z digest=sha256:0d214543d1ee87b4de2fff4a043c67eef471e15a66c98123668425a326ec49d2

Observation 9f3cbceb-e021-452f-91b5-2bfb1acc1c72 · outbound

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

AQuA: Recursively Self-Improving Quantitative Trading Research Agents AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment

Reference 9

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source=arxiv_source observed=2026-08-15T22:14:12.418198Z digest=sha256:db8e5ca7e03c4c6c1487d1190fba7c117c4bd7701618a5ee669b0511651d5a3b

Observation 6c3e2308-4210-46a5-8b50-e97b855da29b · outbound

This paper cites doi:10.1145/3448016.3457324 , booktitle =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents doi:10.1145/3448016.3457324 , booktitle =

Reference 10

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source=arxiv_source observed=2026-08-15T22:14:12.423941Z digest=sha256:8ba8fe8d32a8b9e51d90668ae47cb3d22f8e88e95377f95d66c55bd1624bd210

Observation ba93d070-1d42-4f41-84e3-e4457e7efe2f · outbound

This paper cites Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning

Reference 11

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verified exact
local_arxiv, observed 2026-08-15T22:14:13.676139Z

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=arxiv_source observed=2026-08-15T22:14:12.429640Z digest=sha256:07e9108526b909a345a96316df02a773f162cff047b1a39f8e5a3a9aea4da1d2

Observation 55523763-2469-4fb9-9c78-8c96547b0495 · outbound

This paper cites Notices of the American Mathematical Society , author =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Notices of the American Mathematical Society , author =

Reference 12

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Unavailable: canonical work link unavailable.

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Observation db95d1f0-3d0b-4a45-82b6-8ddf0cc8e1d8 · outbound

This paper cites The probability of backtest overfitting , issn =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents The probability of backtest overfitting , issn =

Reference 13

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source=arxiv_source observed=2026-08-15T22:14:12.441374Z digest=sha256:44fd2f104c3c343e03b53039a9f2d7335c8ebba2723765bdbdb5187b92402c14

Observation 24f3455d-5c5d-4cef-83b1-a610b7ac80db · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 14

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Observation 9fe47032-a48b-4cb9-bf8f-1f149be559d0 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 15

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no resolver link, observed 2026-08-15T22:14:12.452184Z

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source=arxiv_source observed=2026-08-15T22:14:12.452184Z digest=sha256:a4b3225caed0c489d48076c04b927890d97a8786c5d04a6fe4e9d510cec8bcf4

Observation c29da2a7-0179-4240-a8ad-c4d7a88b6914 · outbound

This paper cites Attention Is All You Need.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Attention Is All You Need

Reference 16

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source=arxiv_source observed=2026-08-15T22:14:12.458285Z digest=sha256:64e08bdaa26e9a4fa9d584819f9c5161d8210b8c5f2027a9b8aab5903f469fdd

Observation 9f06d17a-6522-4a87-85d8-e02118126828 · outbound

This paper cites 2026 , note=.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents 2026 , note=

Reference 17

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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=arxiv_source observed=2026-08-15T22:14:12.463927Z digest=sha256:f0b28607a9f86aaca4a43e452ea10f4137fd59405f406a3ede5a5b8f450b7440

Observation c406d12f-7297-4e9f-bf5e-8bd389289761 · outbound

This paper cites an unresolved cited work.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Unresolved cited work

Reference 18

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raw_fallback, observed 2026-08-15T22:14:14.242508Z

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=arxiv_source observed=2026-08-15T22:14:12.469058Z digest=sha256:6e1c020384c6b7766c10822bdcd476c8713695b22ce97058ded599189c62e248

Observation a6b20f78-e387-46db-ab2d-762efb84edfa · outbound

This paper cites FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment

Reference 19

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local_arxiv, observed 2026-08-15T22:14:13.571232Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 33f23523-7f58-4d28-ac8f-4a86c5ba941d · outbound

This paper cites doi:10.48550/arXiv.2602.11917 , publisher =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents doi:10.48550/arXiv.2602.11917 , publisher =

Reference 20

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verified exact
doi, observed 2026-08-15T22:14:13.546160Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:14:12.479612Z digest=sha256:2ec392a75f9c0be3dd84dd7031ac3c4d2323f0f096430cc864c9e9ccd688c04d

Observation 4789eb68-466a-477d-b124-72f4450b00d3 · outbound

This paper cites doi:10.48550/arXiv.2602.14670 , publisher =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents doi:10.48550/arXiv.2602.14670 , publisher =

Reference 21

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verified exact
doi, observed 2026-08-15T22:14:13.475936Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 808702c7-fbe9-4c83-8e97-03f6aaa41ea7 · outbound

This paper cites Hubble: An LLM-Driven Agentic Framework for Safe, Diverse, and Reproducible Alpha Factor Discovery.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Hubble: An LLM-Driven Agentic Framework for Safe, Diverse, and Reproducible Alpha Factor Discovery

Reference 22

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source=arxiv_source observed=2026-08-15T22:14:12.489250Z digest=sha256:9c9d765a36c073d97a3440fc90111e77be548649548dd0eda55b8e916f20500c

Observation 7a882c17-af19-4237-8172-9e472a5a1e30 · outbound

This paper cites doi:10.48550/arXiv.2603.20247 , publisher =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents doi:10.48550/arXiv.2603.20247 , publisher =

Reference 23

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verified exact
doi, observed 2026-08-15T22:14:13.367987Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:14:12.494251Z digest=sha256:600057badf59981fac9c1c6916750d0b0cd0b631c5eabc8c678d119dd92bac47

Observation cadb4b47-bd3f-49a0-abdf-512131dca573 · outbound

This paper cites AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents

Reference 24

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local_arxiv, observed 2026-08-15T22:14:13.289202Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T22:14:12.499122Z digest=sha256:e1594a89e024417d4d3c545f20974f91095c64567bab993a7f172a9dc9706fc6

Observation 015d67b1-8258-4a82-9c8d-b4c900f82cc0 · outbound

This paper cites From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery

Reference 25

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Observation c3597b02-9ac4-4644-b6d6-e99f730db957 · outbound

This paper cites The Review of Financial Studies , author =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents The Review of Financial Studies , author =

Reference 26

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Observation 6b2ee9b7-6d9a-403f-bb0b-6a84f13555b0 · outbound

This paper cites Review of Financial Studies , author =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Review of Financial Studies , author =

Reference 27

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Observation 9cdcdbf1-c361-453b-9eec-6e2fdc855b39 · outbound

This paper cites Neural Computation , author =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Neural Computation , author =

Reference 28

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source=arxiv_source observed=2026-08-15T22:14:12.520934Z digest=sha256:2e0a6539119fbb8b4303e83e5ae6e6ea720d70e16aef8f0e66775401a662b839

Observation 3892daeb-f099-412d-b8a6-913ee0c3224c · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 29

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Observation e8bb3a87-b249-4f1d-8152-66c9e1dd1797 · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents xLSTM: Extended Long Short-Term Memory

Reference 30

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Observation cfa2fffe-74a7-4b6d-b8de-f0e7bd3dd106 · outbound

This paper cites Nature , author =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Nature , author =

Reference 31

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Observation 0d8ffd47-0c27-460e-89c6-a4b99ebcda35 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , author =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Proceedings of the AAAI Conference on Artificial Intelligence , author =

Reference 32

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Observation da9519b8-da6b-4ec1-96e6-bab16a9096e4 · outbound

This paper cites doi:10.1109/TSP.2025.3576781 , journal =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents doi:10.1109/TSP.2025.3576781 , journal =

Reference 33

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raw_fallback, observed 2026-08-15T22:14:14.152791Z

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.

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Observation 8cec9f00-195e-4c07-85ae-92613e985770 · outbound

This paper cites doi:10.1145/3711896.3736838 , booktitle =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents doi:10.1145/3711896.3736838 , booktitle =

Reference 34

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Observation f2963326-4e1a-435a-b6ad-dbc238843dfb · outbound

This paper cites doi:10.1109/ICASSP55912.2026.11463591 , booktitle =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents doi:10.1109/ICASSP55912.2026.11463591 , booktitle =

Reference 35

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Observation 16e344b9-db74-4b9b-b6e1-891b97ce3345 · outbound

This paper cites Chain-of-.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Chain-of-

Reference 36

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verified exact
doi, observed 2026-08-15T22:14:13.146654Z

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=arxiv_source observed=2026-08-15T22:14:12.560679Z digest=sha256:5ea06aaa6e9c0891bed696ad07f35c2d4844833507fc9112c39959e527bc2917

Observation d5ae5aa8-89a5-4b2e-8b39-d3168c06a680 · outbound

This paper cites Learning from Expert Factors: Trajectory-level Reward Shaping for Formulaic Alpha Mining.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Learning from Expert Factors: Trajectory-level Reward Shaping for Formulaic Alpha Mining

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-15T22:14:12.565598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:14:12.565598Z digest=sha256:3bd4d7f617330a4e746045458a976bf02741da2f18f1afe7be967b294cf84c49

Observation 7947d7c5-620c-4569-81c3-e74f78061549 · outbound

This paper cites an unresolved cited work.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Unresolved cited work

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-15T22:14:12.570758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:14:12.570758Z digest=sha256:116d9233f049f268d03f40f2d269c37e89c7be53d1245a876db30f12a8a4ac06

Observation de4667c3-b4ab-4419-b9ed-8e8782d6dfe1 · outbound

This paper cites Exploring the.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Exploring the

Reference 39

Resolution
verified exact
doi, observed 2026-08-15T22:14:13.046766Z

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=arxiv_source observed=2026-08-15T22:14:12.575667Z digest=sha256:af581c8267ada099b86f15d1871695c44626df50b07aa7a2d49bc06b2996adb8

Observation 34dc652f-95d9-4628-819b-f1420d2b6165 · outbound

This paper cites EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-15T22:14:12.581033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:14:12.581033Z digest=sha256:3915227309fb428cc07ebd621e3e8a92d722797798c4021ae9fbd918f97b80f3

Observation 593f3293-0684-4276-a0ac-6121c85e01bd · outbound

This paper cites Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-15T22:14:12.586921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:14:12.586921Z digest=sha256:3cec8d86a0d63e2fbef1d8adab7fd1c8c42489e5744bca98bf8ebf8628cf433d

Observation a2076432-6327-4359-a214-6c6b1530c1bd · outbound

This paper cites an unresolved cited work.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Unresolved cited work

Reference 42

Resolution
verified exact
doi, observed 2026-08-15T22:14:12.925073Z

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=arxiv_source observed=2026-08-15T22:14:12.592150Z digest=sha256:035b65b581c9a77c64c8d4650bf91c5584c4c26cfceaadef0791a19b9301014b

Observation 4c99400c-bfc3-4b91-a9d5-0e49ebb16001 · outbound

This paper cites doi:10.2139/ssrn.5580590 , author =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents doi:10.2139/ssrn.5580590 , author =

Reference 43

Resolution
verified exact
doi, observed 2026-08-15T22:14:12.908152Z

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=arxiv_source observed=2026-08-15T22:14:12.597470Z digest=sha256:0c203b29f6c82d3c8cf6b71053d158ccc8523c2806203d94fba20776fce27647

Observation 2e8ef099-a137-4db1-b340-70ff491162af · outbound

This paper cites 2025 , pages =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents 2025 , pages =

Reference 44

Resolution
verified exact
doi, observed 2026-08-15T22:14:12.891013Z

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=arxiv_source observed=2026-08-15T22:14:12.602121Z digest=sha256:417dc71ed013b62fac8331ea4d13b10d745090eb3f0dbde9bd5ea375e901d86d

Observation ed9cf89d-bbed-4ba5-8810-19131f44a7d4 · outbound

This paper cites Journal of Risk and Financial Management , author =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Journal of Risk and Financial Management , author =

Reference 45

Resolution
verified exact
doi, observed 2026-08-15T22:14:12.873487Z

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=arxiv_source observed=2026-08-15T22:14:12.607069Z digest=sha256:e8dae8247aaefc4d01fb01fa54d7bc5c3f741a0eadf9b80e2e502e870a949712

Observation 2de136e1-1108-4d3d-a3d5-d462d63f1228 · outbound

This paper cites Forecasting , author =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Forecasting , author =

Reference 46

Resolution
verified exact
doi, observed 2026-08-15T22:14:12.856331Z

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=arxiv_source observed=2026-08-15T22:14:12.612520Z digest=sha256:26552918c456c729ddcb72536ae68b3479a37553a96e420e1bdc2fb3140b7b2c

Observation 56b713c3-efb2-4261-9b6a-b49580d304d6 · outbound

This paper cites Deep learning and machine learning models for portfolio optimization:.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Deep learning and machine learning models for portfolio optimization:

Reference 47

Resolution
metadata mismatch
raw_fallback, observed 2026-08-15T22:14:13.864885Z

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=arxiv_source observed=2026-08-15T22:14:12.617426Z digest=sha256:9fc6fdcb530edb46f47bc90a9ea52ff9c1be8206f047cac06748147f95e45f37

Observation e5907b98-5ba0-4b9b-87f3-3cf4cd4e5170 · outbound

This paper cites Enhancing.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Enhancing

Reference 48

Resolution
verified exact
doi, observed 2026-08-15T22:14:12.837772Z

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=arxiv_source observed=2026-08-15T22:14:12.622670Z digest=sha256:675b5fc491500de1428f44a13db27855d9a285934044796edb6b3a0e50fa2b21

Observation ac622067-03ad-4331-84c6-c5061bd72e3b · outbound

This paper cites doi:10.2139/ssrn.6085266 , author =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents doi:10.2139/ssrn.6085266 , author =

Reference 49

Resolution
verified exact
doi, observed 2026-08-15T22:14:12.819427Z

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=arxiv_source observed=2026-08-15T22:14:12.629062Z digest=sha256:b44beba4600b55afcebdfe5f9bd3bb2e63be16f8010ccca7399fbea7d30dcea1

Observation ff0169e4-9d6d-4b7c-abe3-189b4f733872 · outbound

This paper cites doi:10.2139/ssrn.5166656 , author =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents doi:10.2139/ssrn.5166656 , author =

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-15T22:14:12.634320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:14:12.634320Z digest=sha256:42419ec7dd5836a1a11af99e378319b254e59ffa4f6fcf465fa789618cd944af

Observation 28619646-db8a-4185-bd68-76bc781c00c6 · outbound

This paper cites doi:10.2139/ssrn.6906675 , author =.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents doi:10.2139/ssrn.6906675 , author =

Reference 51

Resolution
verified exact
doi, observed 2026-08-15T22:14:12.791477Z

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=arxiv_source observed=2026-08-15T22:14:12.639469Z digest=sha256:bdb9674fd3037b23a7dd39431ea0b64d18cb2bf6758265fa7c79adc7387a2222

Observation cbcf93c7-94fd-4576-a2ed-47fd140c5273 · outbound

This paper cites From Hypotheses to Factors: Constrained LLM Agents in Cryptocurrency Markets.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents From Hypotheses to Factors: Constrained LLM Agents in Cryptocurrency Markets

Reference 52

Resolution
verified exact
local_arxiv, observed 2026-08-15T22:14:12.774905Z

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=arxiv_source observed=2026-08-15T22:14:12.644224Z digest=sha256:574627582bdcaa707fbc0a194afef08960dab20c5c1feef5e9f1f6b195a09a8d

Observation 87d6f0ba-0f46-4e69-bb9e-afee8e37ca2e · outbound

This paper cites AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Mining.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Mining

Reference 53

Resolution
metadata mismatch
local_arxiv, observed 2026-08-15T22:14:12.751205Z

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=arxiv_source observed=2026-08-15T22:14:12.649482Z digest=sha256:90040a8a768fc74fedd0b62226dbf17bce2447ebee98d2f4b633195a8e321f9d

Observation bcca0885-d8d6-4cb8-9798-13b2c0896a81 · outbound

This paper cites Cognitive Alpha Mining via LLM-Driven Code-Based Evolution.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Cognitive Alpha Mining via LLM-Driven Code-Based Evolution

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-15T22:14:12.654762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:14:12.654762Z digest=sha256:fc3b80510cb0943268354c08edf7aef893968639db4a2ffaea54ca23de50d331

Observation 644d1471-d813-4323-9535-260af9191680 · outbound

This paper cites Towards Autonomous Formulaic Alpha Discovery: An Evolutionary Computation Perspective.

AQuA: Recursively Self-Improving Quantitative Trading Research Agents Towards Autonomous Formulaic Alpha Discovery: An Evolutionary Computation Perspective

Reference 55

Resolution
verified exact
local_arxiv, observed 2026-08-15T22:14:12.708626Z

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=arxiv_source observed=2026-08-15T22:14:12.659861Z digest=sha256:a0ebcb475d073d641697e45ac2ea3c1e0ad65c6263db1f0a0d31c8cc19c78356

Pith citing papers

No inbound Pith citation observations are available.