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Paper Citation Record · LEDGER

Heuristic Learning for Active Flow Control Using Coding Agents

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.11565.

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

pith.paper-citation-record.v1
2607.11565 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T04:44:42.371749Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

35 of 35 outbound references displayed

  • verified exact5
  • verified fuzzy0
  • unresolved29
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 85eb0089-2ca2-4bc0-a15f-cfdfee2a327b · outbound

This paper cites Vincent Belus, Jean Rabault, Jonathan Viquerat, Zhizhao Che, Elie Hachem, and Ulysse Reglade.

Heuristic Learning for Active Flow Control Using Coding Agents Vincent Belus, Jean Rabault, Jonathan Viquerat, Zhizhao Che, Elie Hachem, and Ulysse Reglade

Reference 1

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:4c6d9edd621f24a96218939083250a16eea2d5f7c1b62683946edceea846e06f

Observation f24e6ce1-935b-41b4-994f-b2c5e1f04e3b · outbound

This paper cites Gerben Beintema, Alessandro Corbetta, Luca Biferale, and Federico Toschi.

Heuristic Learning for Active Flow Control Using Coding Agents Gerben Beintema, Alessandro Corbetta, Luca Biferale, and Federico Toschi

Reference 2

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doi, observed 2026-07-14T04:50:15.712835Z

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:88e2e34a35aff3ca23f958c99f012be404044c37390263943562f1000a522b64

Observation c0e21613-bdf5-4a7c-9bf9-9b3725273396 · outbound

This paper cites Elie Hachem, H.

Heuristic Learning for Active Flow Control Using Coding Agents Elie Hachem, H

Reference 3

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Observation ef636b84-2a25-4e10-82d7-dcb582c57db6 · outbound

This paper cites an unresolved cited work.

Heuristic Learning for Active Flow Control Using Coding Agents Unresolved cited work

Reference 4

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:ec26cc557ba0f572e1ba451d36530cb96c99810d945b45cec934bde284ea5140

Observation 046f93e8-88db-4657-aa80-1c15c91819ac · outbound

This paper cites Siddhartha Verma, Guido Novati, and Petros Koumoutsakos.

Heuristic Learning for Active Flow Control Using Coding Agents Siddhartha Verma, Guido Novati, and Petros Koumoutsakos

Reference 5

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doi, observed 2026-07-14T04:50:15.702742Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:efba93aa27cbd6616a272ff2886d7f8b59afeda85278d5a07cba38daef928413

Observation c894699e-d299-4087-89cc-29ed1107d0a5 · outbound

This paper cites Jonathan Viquerat, Jean Rabault, Alexander Kuhnle, Hassan Ghraieb, Aurélien Larcher, and Elie Hachem.

Heuristic Learning for Active Flow Control Using Coding Agents Jonathan Viquerat, Jean Rabault, Alexander Kuhnle, Hassan Ghraieb, Aurélien Larcher, and Elie Hachem

Reference 6

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:a6731b248911095dfcf0648df3d4061913f6ea823fc86d8128effcbf6695513f

Observation 2e69a357-72e1-40bd-8f8f-f0081db7fcde · outbound

This paper cites Dixia Fan, Liu Yang, Zhicheng Wang, Michael S.

Heuristic Learning for Active Flow Control Using Coding Agents Dixia Fan, Liu Yang, Zhicheng Wang, Michael S

Reference 7

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:4a9601f52a54445e562ef2120d56ffac95792ef4ec381201bb46d87a2931c39c

Observation ee8cd1cf-9a4f-4f16-bb78-031d5eeece58 · outbound

This paper cites Jean Rabault and Alexander Kuhnle.

Heuristic Learning for Active Flow Control Using Coding Agents Jean Rabault and Alexander Kuhnle

Reference 8

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Observation 790632fd-e784-466f-af5a-8d7be2e63df5 · outbound

This paper cites Jonathan Viquerat and Elie Hachem.

Heuristic Learning for Active Flow Control Using Coding Agents Jonathan Viquerat and Elie Hachem

Reference 9

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:9a2ffac9730e0f4fe440174bf4ee70987f4f9170ad1a54f5f6bd200f6165fb94

Observation bfa643fd-3f84-4774-9cca-4b518dbf7203 · outbound

This paper cites an unresolved cited work.

Heuristic Learning for Active Flow Control Using Coding Agents Unresolved cited work

Reference 10

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:aa600960d3fc87ccb194248709d91bead4abaed9a53c686c26c7c29ea4f86044

Observation 96ef7870-4715-490f-a3db-ae778572ad45 · outbound

This paper cites Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio.

Heuristic Learning for Active Flow Control Using Coding Agents Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio

Reference 11

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:93f59999ae6ec9f005741124701e76bae6e7b7624c31a690a668f4e0ffefb3dc

Observation a0ce6ad1-b7df-4333-8fb1-d32335ca86ef · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Heuristic Learning for Active Flow Control Using Coding Agents Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 12

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:a63fecc223162a21b564abc7571afdf04dcbd1ee0f0f0dce06bdd6c691b2968e

Observation 924c4649-52b4-42cb-96ef-e1cf5751ab68 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Heuristic Learning for Active Flow Control Using Coding Agents Evaluating Large Language Models Trained on Code

Reference 14

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:0a54b92d0546671a1a62607ec3fab6e30b91528ad5e4105eff0dcbe62e5fdeb0

Observation fb7f2fbf-c3c9-4092-a280-8dd243b01b25 · outbound

This paper cites Anthropic.

Heuristic Learning for Active Flow Control Using Coding Agents Anthropic

Reference 15

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:9e7a8010c35f0b8ce0a45241f96b866eb0186e960988643446263a1df8f0eb67

Observation 6c1e9b97-2215-4bd5-ae3c-a827b1bb261c · outbound

This paper cites Introducing the codex app.

Heuristic Learning for Active Flow Control Using Coding Agents Introducing the codex app

Reference 16

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:179e29a987aa66754461202a172369c15dbb327ceaf97773abe252d5ab888a9d

Observation 9aaf3db7-9f20-4f98-93a1-13066bf45a2a · outbound

This paper cites Feng Ren, Jean Rabault, and Hui Tang.

Heuristic Learning for Active Flow Control Using Coding Agents Feng Ren, Jean Rabault, and Hui Tang

Reference 17

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:2161a6158924573260c9d97d48f48a337eb7c2a30135125b6c5605007ba18e8f

Observation 5764d328-0a9e-4d95-8065-e30fb34066c1 · outbound

This paper cites Paul Garnier, Jonathan Viquerat, Jean Rabault, Aurélien Larcher, Alexander Kuhnle, and Elie Hachem.

Heuristic Learning for Active Flow Control Using Coding Agents Paul Garnier, Jonathan Viquerat, Jean Rabault, Aurélien Larcher, Alexander Kuhnle, and Elie Hachem

Reference 18

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:6456a726f375c28783dc1804af589c8fe122dc349525db22e6152c6aed51ff7d

Observation 1bd35c3c-d5db-46a2-8ec0-aa681393e56f · outbound

This paper cites 2021.104973.

Heuristic Learning for Active Flow Control Using Coding Agents 2021.104973

Reference 19

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Observation d619cd80-480d-4ee1-b405-01668b85f051 · outbound

This paper cites Emanuel Todorov, Tom Erez, and Yuval Tassa.

Heuristic Learning for Active Flow Control Using Coding Agents Emanuel Todorov, Tom Erez, and Yuval Tassa

Reference 20

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Observation 831b2650-2b48-43e9-8521-9506fb9556fd · outbound

This paper cites an unresolved cited work.

Heuristic Learning for Active Flow Control Using Coding Agents Unresolved cited work

Reference 21

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Observation f1e6fdd9-5e72-4735-b105-429cf61baff0 · outbound

This paper cites Jannis Becktepe, Aleksandra Franz, Nils Thuerey, and Sebastian Peitz.

Heuristic Learning for Active Flow Control Using Coding Agents Jannis Becktepe, Aleksandra Franz, Nils Thuerey, and Sebastian Peitz

Reference 22

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:e28b2a81f727ab9bc35bc3dbcee0e8d0dcca5c9842ac7985fb5f5a401add0f39

Observation 14f64a54-58bd-4bb1-b304-17e315ce1da9 · outbound

This paper cites Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control.

Heuristic Learning for Active Flow Control Using Coding Agents Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control

Reference 23

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:353ef5d4d71eae3dcf13023da52c71188a39588893778f0b516e36f284dfcaf3

Observation 2a9c5ece-1b3e-4ee8-ae05-258335f07db5 · outbound

This paper cites GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning.

Heuristic Learning for Active Flow Control Using Coding Agents GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Reference 24

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Observation faa636a4-b382-437e-bdca-723a4f9ea488 · outbound

This paper cites Automatic Prompt Optimization with "Gradient Descent" and Beam Search.

Heuristic Learning for Active Flow Control Using Coding Agents Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 25

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Observation 28752b13-c636-4131-a940-388aa997969c · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.Advances in neural information processing systems, 36:46534–46594,.

Heuristic Learning for Active Flow Control Using Coding Agents Self-refine: Iterative refinement with self-feedback.Advances in neural information processing systems, 36:46534–46594,

Reference 26

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Observation 25f1451d-cbae-4a7d-8052-13ae3e423aba · outbound

This paper cites TextGrad: Automatic "Differentiation" via Text.

Heuristic Learning for Active Flow Control Using Coding Agents TextGrad: Automatic "Differentiation" via Text

Reference 27

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Observation 4f79d55a-0418-43b7-9e2f-f452da6a0f05 · outbound

This paper cites AlphaEvolve: A coding agent for scientific and algorithmic discovery.

Heuristic Learning for Active Flow Control Using Coding Agents AlphaEvolve: A coding agent for scientific and algorithmic discovery

Reference 28

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Observation f8251422-9507-49b9-bb52-3fa158c849cf · outbound

This paper cites Evolution through Large Models.

Heuristic Learning for Active Flow Control Using Coding Agents Evolution through Large Models

Reference 29

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Observation 27817339-906b-449c-bd8f-7d42c7eaaf98 · outbound

This paper cites AFlow: Automating Agentic Workflow Generation.

Heuristic Learning for Active Flow Control Using Coding Agents AFlow: Automating Agentic Workflow Generation

Reference 30

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Observation 84b61347-58df-44be-88a4-eac6373f5a14 · outbound

This paper cites What learning algorithm is in-context learning? Investigations with linear models.

Heuristic Learning for Active Flow Control Using Coding Agents What learning algorithm is in-context learning? Investigations with linear models

Reference 31

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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:3b7f6751f62c947a1811863831f257ad3c57d41777e73c9532dc621f19aabd7d

Observation 3049eb68-6955-47ee-b808-8a6bb8d35b7f · outbound

This paper cites Using gpt-5.5.

Heuristic Learning for Active Flow Control Using Coding Agents Using gpt-5.5

Reference 32

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Observation 83033ce1-3c24-4e76-a235-4e41468cd2f6 · outbound

This paper cites Model configuration.

Heuristic Learning for Active Flow Control Using Coding Agents Model configuration

Reference 33

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Observation 7431b262-e126-4959-b6cf-360357fa6742 · outbound

This paper cites Accessed: 2026-06-10.

Heuristic Learning for Active Flow Control Using Coding Agents Accessed: 2026-06-10

Reference 34

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Observation f53e8e05-2720-452f-a974-4697e3439c64 · outbound

This paper cites Codex cli.https://developers.openai.com/codex/cli, 2026e.

Heuristic Learning for Active Flow Control Using Coding Agents Codex cli.https://developers.openai.com/codex/cli, 2026e

Reference 35

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Observation f24aafc3-8ddd-4fc9-9437-da3f019c2f36 · outbound

This paper cites A Environment Suite The benchmark suite contains 13 environments: 6 larger FluidGym flow-control cases and 7 compact BEACON control cases.

Heuristic Learning for Active Flow Control Using Coding Agents A Environment Suite The benchmark suite contains 13 environments: 6 larger FluidGym flow-control cases and 7 compact BEACON control cases

Reference 36

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Pith citing papers

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