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Paper Citation Record · LEDGER

Heuristic Learning for Active Flow Control Using Coding Agents

As of 17 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 2026-08-16T06:30:59.297886+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

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=sha256:602de75d2c980cd1badfb16d7a1d2f762b8a35a0f0ccf07efe9fe9ddbfeede3d

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

source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:4372bd52e90f2ba8667b66f5b5cc2d75352a3cc693af230901d196d94124491b

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=sha256:551dbd2cca7ddd3ecdd143ec1a5a9967e383494a0f55150c20c7065825fa94d6

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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source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:3c253d97fa536676af4d10b20bdb8d6f5f5c3c59bfbdde7fab7bf53b8b64820f

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:fceee5dac71679eff74d19646d72af4f28770f1c894b7fb29fb147fead0dfb3c

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=sha256:55fe5ed8d9e858a71512e82d65e39d4f8d32744c56edfd4e5e977a314b76a67c

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=sha256:7f6c2ff404d9c9a5b0bd76b52563dfb2c1e2316aa418e168ba06cadd4e1f5082

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:d630e279c069cae5d79c59cc4210a69ef5470d5d44f0a2e3ce30987d0a7b19d3

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

source=pdf_text observed=2026-07-14T04:44:42.371749Z digest=sha256:8696a1f7c3134415ad81efc0faa9cacc4fec9fae7043d032651b0be9a294eea1

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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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=sha256:7d577df9bac546e0f2384b3645112828ed1767104f0b1292e8cd71bf872ac2d0

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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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=sha256:1f07e162813bac03896d85093d5a45ad459a9485976e60af3a9b976a20bf188d

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=sha256:007ca718b84b57e50fc8c6040101f6639a60827a3125d2c7646a3d08567f2888

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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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=sha256:4a657a9d6367885d8d2ef5e89ed5d476e01a4951073f42c17fbcb6ea52359ce3

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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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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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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