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Paper Citation Record · LEDGER

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization

As of 22 August 2026, this Paper Citation Record lists 100 of 108 outbound references and 0 inbound Pith citation observations for arXiv:2607.10127.

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

pith.paper-citation-record.v1
2607.10127 v1

Coverage vector

measured 100 of 108 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T14:06:35.756620Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

100 of 108 outbound references displayed

  • verified exact4
  • verified fuzzy0
  • unresolved96
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 339d3f63-6c2f-4ef2-84a0-00117fabf9cb · outbound

This paper cites LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models

Reference 1

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:ebb9b38c733af33339439e55c4f9941f030cf7245073f6e037a171bda2f0f62f

Observation ac9d3a85-3d68-42f6-bcb2-bfcce1875953 · outbound

This paper cites 2025 , publisher =.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2025 , publisher =

Reference 2

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:8d5a3c9086d82a0c1a0a583da0a857189314e09a782df8b6e3cbce4386b0a9aa

Observation ec268213-3f79-4df9-807a-6480c6a20b15 · outbound

This paper cites Illuminating search spaces by mapping elites.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Illuminating search spaces by mapping elites

Reference 3

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:348f19d32f5b033c0448a803f1537529dd11284b591b9ad527c97c7a160060c3

Observation f26e5b77-1c7b-44e5-816e-ec77aa4b03ad · outbound

This paper cites an unresolved cited work.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Unresolved cited work

Reference 4

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:b0ad9387e887da98bc52cc14e255ca2e64f5470d787bc3d6e114eb73c9a90eda

Observation 0e5920fc-8060-41bb-9693-92bf2ceeea82 · outbound

This paper cites 2021 , organization=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2021 , organization=

Reference 5

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:e4ff94c14ce854015e78a711edbc0ef78a3f06ee924f6a503b02ca9c3d7d5a09

Observation 1bc7a42a-f519-4b4f-ae88-65f6e0d5fbbf · outbound

This paper cites Advances in neural information processing systems , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Advances in neural information processing systems , volume=

Reference 6

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:1c7bd5456a4ee7c58934efad7ff7ac4002351cf48279c463bf45e92549cc41a5

Observation 17574bc5-3796-4fa5-8ab3-f817a1126f30 · outbound

This paper cites Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets

Reference 7

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:18f316a226ae770ffcfe958e36a50ba45d07fa524889149b56d89f3381ccc26c

Observation f7c7db37-cc8d-48d6-9280-b617948b1bda · outbound

This paper cites Journal of chemical information and modeling , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Journal of chemical information and modeling , volume=

Reference 8

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:cf081162af4e69afaa6518dfcec0015763ef3d504dc8ba12f73fa5f70dadcabd

Observation f972ee21-f1dd-437c-9e7c-4535db2a32d7 · outbound

This paper cites International conference on machine learning , pages=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization International conference on machine learning , pages=

Reference 9

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:d998a8e0e73b00362f4f7917205ba4a5ec6ca74dd8f754e59b99479aace6f946

Observation ab29d74b-7e08-4d2d-8667-2eb20802b7d1 · outbound

This paper cites an unresolved cited work.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Unresolved cited work

Reference 10

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:9865a37fe9d843dbe1130ac48c93339318cb052adb6a8bddf55ec67e57d41a5f

Observation ef142fea-e65e-41c5-bba7-107e16baf8e3 · outbound

This paper cites an unresolved cited work.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Unresolved cited work

Reference 11

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:2ad37228c74eb0d039f807ddee332810e00d497b0d9caae008422ece89221195

Observation 5aa3280a-b4c3-4fa8-8c6c-a527a0fb7a53 · outbound

This paper cites International conference on machine learning , pages=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization International conference on machine learning , pages=

Reference 12

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:770b26381f8ca99361c7bb8d2ca70d49222e6fe0c9ee757f4ff330a69211041a

Observation 0b328700-d31c-41ab-87b2-89b48377f8a1 · outbound

This paper cites Proceedings of the aaai conference on artificial intelligence , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Proceedings of the aaai conference on artificial intelligence , volume=

Reference 13

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:8d7be4a490ae82cbe7d35c69ee08052ac9eb11cb1c70829955b73a1fd6f9f76f

Observation 13ba2ec6-2073-40df-9517-5f42db6dcd27 · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Neural Architecture Search with Reinforcement Learning

Reference 14

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:054d8935de4f43fce2e690785bef944ceb82c30a19f59a3d1c3e5e79e20d209a

Observation ff63dff0-446c-443e-9b1e-f3cb3ad7e97c · outbound

This paper cites IEEE Transactions on Evolutionary Computation , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization IEEE Transactions on Evolutionary Computation , volume=

Reference 15

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:e5af597bf33d88d500b3f16fccdecef572acb888ed792d6fc38b2584c53318a2

Observation edffe0e4-0178-4d76-aeb4-b7e675d5d905 · outbound

This paper cites ACM Transactions on Evolutionary Learning , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization ACM Transactions on Evolutionary Learning , volume=

Reference 16

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:bf7da0bc96956a5e5021c520cb843890fe7eb93a2a3d6dbda3aedc0aa66aa6fe

Observation 718379be-b66f-4dfd-95e5-0681dca683b4 · outbound

This paper cites Advances in neural information processing systems , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Advances in neural information processing systems , volume=

Reference 17

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:76adffe64dc5e894bb69aef72201fe46ebc1a007c36d5c844c3d830b3fd6ee23

Observation da07a46a-1dad-4e83-a996-724f4152bd04 · outbound

This paper cites Proceedings of the IEEE , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Proceedings of the IEEE , volume=

Reference 18

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:e530f1663fdebf68cecfe49877d6d0388aa72350614aba454a5d163981da81ac

Observation 02853c9f-c56d-4f16-a32c-6a746d39fb74 · outbound

This paper cites International conference on machine learning , pages=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization International conference on machine learning , pages=

Reference 19

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:4f58ef619a05fca22b0422982ce9fd6050b5fefe1fbded7aca6f4506483a324a

Observation 5e5b82b0-e284-49b0-a8d6-90f4508ec4ee · outbound

This paper cites Proceedings of the AAAI conference on artificial intelligence , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Proceedings of the AAAI conference on artificial intelligence , volume=

Reference 20

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:aee822e962b3dbd1995066600dfcad011ce7fc91bcc87a9ee910ea3ef13ebbec

Observation 65292e8a-214b-422f-a6f4-a0e91b46f96c · outbound

This paper cites ThetaEvolve: Test-time Learning on Open Problems.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization ThetaEvolve: Test-time Learning on Open Problems

Reference 21

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:48d66752d564cca319c119a344e68f24733741dee53aec14e5d25bd0fb3092bc

Observation 30f50f19-3786-449c-a899-b4ccd76eb8aa · outbound

This paper cites Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning

Reference 22

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:c3a4ae501c4bd3dd80e093cb95fc76fbe2d7b97bf98e073c501e8e56c7018261

Observation 5946831a-8fb7-46a9-924e-9c578cff4fa6 · outbound

This paper cites Proceedings of the Genetic and Evolutionary Computation Conference , pages=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Proceedings of the Genetic and Evolutionary Computation Conference , pages=

Reference 23

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:3ebe6da5fd3f6527642660363771f224c41a6b9b2f1d1e590d034a4c698fc94c

Observation 53a8e035-908f-46c5-beac-e7d35835138e · outbound

This paper cites Proceedings of the Genetic and Evolutionary Computation Conference , pages=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Proceedings of the Genetic and Evolutionary Computation Conference , pages=

Reference 24

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:3bef47b0eedd871052a0ab407333ee35a1965282122f8bd49c30d5970742c531

Observation bb2e82be-4255-4d4c-b409-7cd9924d7fc4 · outbound

This paper cites Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning

Reference 25

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:ada152da8cc2db6243ba5ced5c47d14e99c4083cb605041075c7e78c273a14e1

Observation 9f32497b-a80b-4c88-be9a-cb9aa83abad0 · outbound

This paper cites Hugging Face Blog , year =.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Hugging Face Blog , year =

Reference 26

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:3cc01ed8b43a6c9ec7ba303d4862ed08b8256f537ddc97fe66f6114a7b0bf70d

Observation bd6379a7-5a96-4182-bd89-35e773a89fab · outbound

This paper cites , author=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization , author=

Reference 27

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:21b5692d3e8ddc680c6663218ad36412ca46f88162de7226c2063da6846d3aa1

Observation aa9cfcce-8ed7-491b-90cc-b26d21b5d10e · outbound

This paper cites Chemical science , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Chemical science , volume=

Reference 28

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:2d616717069f2aae88d87af93b04628eba04c67125d36a9d9942979dd9fc3989

Observation 1e857371-4ec2-4a38-83e3-0f3b99355f8f · outbound

This paper cites International conference on machine learning , pages=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization International conference on machine learning , pages=

Reference 29

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:edad70564eab058c869c5d7ae63275330d86e30028542c109f5bc27af89c7d57

Observation 7e0b28ca-f2f6-4f4a-a86e-65585cb4184c · outbound

This paper cites Icml , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Icml , volume=

Reference 30

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:8658a15e322fa3c01a0e09c6ba12756fac6c255df36d74504ea0b83e6aab2280

Observation ca6892c1-9a81-4e6b-a7a5-e36e4f04a344 · outbound

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

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 31

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:ce80b6da5205304032a52a01362e502a90bf1ef47ddbfd886e213575f497f0fb

Observation 38ab82ac-237a-40d5-9fb5-7b27a7d74ac9 · outbound

This paper cites arXiv preprint arXiv:2601.10657 , year=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization arXiv preprint arXiv:2601.10657 , year=

Reference 32

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:e9b6546707540671e487399813413c49e8fb90f07bc425fefeecb087d5dd0d5d

Observation bf4727b4-9d96-4246-9704-7d11c2f6f46a · outbound

This paper cites LLM-SR: Scientific Equation Discovery via Programming with Large Language Models.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

Reference 33

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:244995e1928dfadf571a7bc6430ed57ca97ca7b318fe3e1916eadbafdfac7603

Observation 7501fb7c-96f9-406f-a104-3a2d36f60ccd · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Advances in Neural Information Processing Systems , volume=

Reference 34

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:e4736380b10cec86d0996cbca34c01df39861dad83bb84330dd34545c3dc3d45

Observation 8b7e3919-8121-42c0-8d36-d1db5eb63f8d · outbound

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

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization AlphaEvolve: A coding agent for scientific and algorithmic discovery

Reference 35

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:c75f5ee0b87bd6ea9028d0094a1d7b568c9b59e2fe3162cfe9ef9a2edb3d952d

Observation 14d3a50c-dc0a-449e-8a13-0250f3784c79 · outbound

This paper cites CodeEvolve: an open source evolutionary coding agent for algorithmic discovery and optimization.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization CodeEvolve: an open source evolutionary coding agent for algorithmic discovery and optimization

Reference 36

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:ef8e871b5fe820c635f4fd2c62aba040aefbf802686b77577180fcdfa84e886d

Observation f5c01136-a1f3-4618-b020-35c2a67ecb7c · outbound

This paper cites Digital Discovery , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Digital Discovery , volume=

Reference 37

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:822565973c0fa627aed9f0260bb40324ae4952b05f154f6943f44a355aa54f97

Observation 42b82afd-cd7f-4301-ab56-dd3ccd08b6ff · outbound

This paper cites ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution

Reference 38

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:1be0b0f6bf758ab80076c4bec02b0c349d1799c19eb1d49e10b2efcd7649c29b

Observation c3df3008-f288-4637-8b24-d09feb25ed1e · outbound

This paper cites 2019 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2019 , eprint=

Reference 39

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Observation c119cc60-ce4e-4e30-87c0-1310752a4c5d · outbound

This paper cites 2019 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2019 , eprint=

Reference 40

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Observation f8563182-5825-411e-a135-d04b9f5df022 · outbound

This paper cites Molecular contrastive learning of representations via graph neural networks , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Molecular contrastive learning of representations via graph neural networks , volume=

Reference 41

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Observation d603050b-6b90-474a-8d4f-2a0a0cb8ae3c · outbound

This paper cites 2024 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2024 , eprint=

Reference 42

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:6c4efa96547ec34f974b0a97fd4d3018199e193462a903730798480b7d185303

Observation fd614474-8dfc-4822-b63c-4bb1061b4cae · outbound

This paper cites an unresolved cited work.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Unresolved cited work

Reference 43

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:df32a25309caa0f93aecc0b476fb8b311140d16bcdf83f90b5be65680e4c1d81

Observation cc5bf122-db00-43a4-b52c-661f83630f13 · outbound

This paper cites UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning

Reference 44

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:b0bd2fc33f7998f52188e1564c8fb19b93617b9e9798aebb9a32e2eda1e391b0

Observation 40196a16-828f-4e11-af56-69f9acefa1df · outbound

This paper cites Directional Message Passing for Molecular Graphs.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Directional Message Passing for Molecular Graphs

Reference 45

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:220633a15a3bd14e4aa1282157919c7d53bab10bad9d14740bed15bd091d26af

Observation ade9490d-79d2-4e37-92a5-ce515913338f · outbound

This paper cites The Journal of chemical physics , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization The Journal of chemical physics , volume=

Reference 46

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:5ba483522e8b70fd233b0b87d3fb6d2a9b16a6fb1e616c63f0357191a8b191d5

Observation 6b5c7aed-7bc0-4c2f-8d79-259f126631f6 · outbound

This paper cites Nature Machine Intelligence , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Nature Machine Intelligence , volume=

Reference 47

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:0e5d936553ab9bed2dc01179c2fb31d34649e5f1f45365f613797403bd9b6ad8

Observation b919e9fd-9a54-4372-8caf-12c7ed8ef16e · outbound

This paper cites Journal of medicinal chemistry , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Journal of medicinal chemistry , volume=

Reference 48

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:8c223aa71702d768499565bed642b12a4440645af2c5025b95a8c029ff2bcc81

Observation 06a38dea-2f94-429a-927d-90eb364f5881 · outbound

This paper cites Journal of chemical information and modeling , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Journal of chemical information and modeling , volume=

Reference 49

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:62ff08a13cf141cf708a04f7e66d9602eb23a353dd43eec4e3ba2e59cefacf04

Observation 58a48d40-f032-4aa7-a6d9-5b52e08bbac2 · outbound

This paper cites Advances in neural information processing systems , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Advances in neural information processing systems , volume=

Reference 50

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:4441bea179cecd4ed9258e550c5bb7ee9a0694c9d291ce138f88f7db5f7c3be6

Observation 32735873-47eb-41ba-b9c7-eae1a2f165e5 · outbound

This paper cites Machine Learning: Science and Technology , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Machine Learning: Science and Technology , volume=

Reference 51

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:dc35bbc1f5878732c8e8c97f0a949144d2af2961a053931ef051c7f1906685fb

Observation 619bbca4-b401-41d3-bce1-d79df6216994 · outbound

This paper cites M ol TRES : Improving Chemical Language Representation Learning for Molecular Property Prediction.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization M ol TRES : Improving Chemical Language Representation Learning for Molecular Property Prediction

Reference 52

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:5a0d6c2e133a88c10bbf134cb0f4fa7c95ecbcca87e5c688097c87b300192166

Observation 2f12037e-c8c3-44ec-a9a9-ead98968727f · outbound

This paper cites 2023 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2023 , eprint=

Reference 53

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:c805b8477c9c4539f6714e0d1fae0c172e21db4a6e08a3191687729397fa7dbd

Observation da7089d9-23a6-4524-ad09-ba8117d24f83 · outbound

This paper cites Self-referencing embedded strings (SELFIES): A 100 volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Self-referencing embedded strings (SELFIES): A 100 volume=

Reference 54

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:285218e61c38b9212b78318833d5e1c5ddf8afc23f748fc8c03ba2e6fd960383

Observation 8286421b-0cfc-4a79-b645-36ee20c532c1 · outbound

This paper cites 2022 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2022 , eprint=

Reference 55

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:d4461118fd8a02757e42ab53f3b671228bbc3ce8399420199652f5fc9128d1c2

Observation adf0b9b2-12b1-4b1f-bbbd-cfc33ad36b71 · outbound

This paper cites 2022 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2022 , eprint=

Reference 56

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:16da205b13b0cd56e01c77ae50b6ed27ed519e1b7f0e03d6a12e79a97541fd1c

Observation 4f8feb01-8bf2-48ec-8810-2b2cd062744a · outbound

This paper cites 2020 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2020 , eprint=

Reference 57

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:a59041fb43014553534843b373e1ef228a9b0c9e3ab09253f9472590e1fab3f8

Observation 0494cba3-e946-4040-90f4-398c5f45e6da · outbound

This paper cites 2022 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2022 , eprint=

Reference 58

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:0c171abd29f9b47d22131ab9df0ccdbc4b672004e62119a530308538e547a530

Observation 9108c04d-672f-4a44-abde-d11bc80a33e9 · outbound

This paper cites an unresolved cited work.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Unresolved cited work

Reference 59

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:79eb0f5892bc86674a522f65d4c9a691bddff36d02d556f0a7ff95c8b7add353

Observation ba9af258-7b62-4c13-a455-c8aa8f686524 · outbound

This paper cites 2023 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2023 , eprint=

Reference 60

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:f83ebdf5678b3270fb8cc6436685c057d42b0733ed09fecb4e2103936f238c70

Observation 1e1008bb-7cf8-4c77-b366-e0f8e59e53bc · outbound

This paper cites Nature , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Nature , volume=

Reference 61

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:fde6c4bfdc3e5d7bdeef7999fb5f3005d415121c02a286c7873e228e63b9cfa4

Observation b90fd7ab-1e45-47ce-aed2-49530932b231 · outbound

This paper cites BMC bioinformatics , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization BMC bioinformatics , volume=

Reference 62

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:2fec15964b063a1bae4951dd5e4ffd56dff167e93531d6039b83ed33ae5cf80c

Observation 7921bc12-bf73-481d-bfce-40d179263d59 · outbound

This paper cites 2020 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2020 , eprint=

Reference 63

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:86703d226318fb8e48736077528e4257b53308f47ea6cbcb4da8b906e599d32d

Observation 509d8fdb-dc78-486d-ab30-d150fe22d41f · outbound

This paper cites 2017 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2017 , eprint=

Reference 64

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Observation 1f32f838-e7cd-4977-95f6-b126543f7a70 · outbound

This paper cites Proceedings of the genetic and evolutionary computation conference , pages=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Proceedings of the genetic and evolutionary computation conference , pages=

Reference 65

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:fc9abf162abbb59f41118775efa7f5763cec1ba0e91b50f61f628b695e0b8965

Observation 7bc37929-9b89-46d2-af50-a60696d6abb2 · outbound

This paper cites International conference on machine learning , pages=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization International conference on machine learning , pages=

Reference 66

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:6449e494c8766c458dad095b8b316084a784056791c38513faee1e442fc33d26

Observation 35a42c61-2250-4a99-9471-5b72f8730aba · outbound

This paper cites Designing Neural Network Architectures using Reinforcement Learning.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Designing Neural Network Architectures using Reinforcement Learning

Reference 67

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:b36b9426b6b041513c5b8b9db7e8a34ced4be79f4ddbc8d9302b43ac5586ec70

Observation e7fb6751-9ebf-42f7-b4bc-794b101be158 · outbound

This paper cites DARTS: Differentiable Architecture Search.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization DARTS: Differentiable Architecture Search

Reference 68

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:147f7b3cd4bed003b139ebbd07e3d86d2467eed026006d7cec0d78f5edf38fac

Observation 4427aa1e-4494-407e-b388-4b35d4826387 · outbound

This paper cites Journal of chemical information and modeling , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Journal of chemical information and modeling , volume=

Reference 69

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:669fc67f59090e597a85478f90c3190187843c1ed2d14bd9e673a78bd7e000de

Observation d95821f8-5a72-41b6-a6d4-4266614254a8 · outbound

This paper cites Nature Mental Health , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Nature Mental Health , volume=

Reference 70

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:a94c1e9436785c4797a5a3fdefe6866e4b9157673839db0950596e8014ec48f1

Observation 21f704ad-4558-4d91-af8c-fb837e63ab03 · outbound

This paper cites Journal of anxiety disorders , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Journal of anxiety disorders , volume=

Reference 71

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:27616082333970a93111ed041bd5877ea6067de6738f07d1b1181479f4008d24

Observation 7b92cf57-7d88-4bbf-a672-6f4e5ba8faa3 · outbound

This paper cites Proceedings of the National Academy of Sciences , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Proceedings of the National Academy of Sciences , volume=

Reference 72

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:0a3a10c8ea654d726dc1fd296eb828ecfd9dc8b3fc8b5da8d1f1fcd36a355b6a

Observation 7c64c781-0912-4275-8530-6b43b1102921 · outbound

This paper cites Behavior research methods , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Behavior research methods , volume=

Reference 73

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:e8c8d95a2320266a4d3fab042b5fc900ec5b7369c3679c3d699763b00c4b0351

Observation d425e211-e290-493c-8cf8-dc917632f0fd · outbound

This paper cites Journal of vision , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Journal of vision , volume=

Reference 74

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:da974654b0842dcad15fd88ae9965538e2ab8120c363dabbb8c98e2bccebddfa

Observation 052b0c73-3cee-4cb3-a6f9-8031f920dd33 · outbound

This paper cites CoRR , year=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization CoRR , year=

Reference 75

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:e78ab7d16b7af0401b50f0d34dea0f179b9a9a569fb7c24ec0b546e596a7d307

Observation 279b0370-18bd-4aaa-877e-ec1547361bbc · outbound

This paper cites Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=

Reference 76

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:f6d1558c6a50ccee5952667cbb51525432b0a098239f00b8d98e9155dadde3cf

Observation 78a51d5a-80d4-4c2f-a1b1-c91dd3d4f775 · outbound

This paper cites 2020 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2020 , eprint=

Reference 77

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:ba874289a9945e77a581a34f72b56b6c7234646ab0a2080db9d7fc784d14604f

Observation 670a2333-4b87-46ce-8822-a36424c4a93e · outbound

This paper cites Communications in Statistics - Simulation and Computation , volume =.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Communications in Statistics - Simulation and Computation , volume =

Reference 78

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verified exact
doi, observed 2026-07-14T14:10:36.663013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:7f12494ce5ab0971de1ba7b23819eae14a7f09c9597b7523e09a1741c43ec1bb

Observation 33619c55-6a86-4684-a4b7-a0f64d16e46d · outbound

This paper cites Proceedings of Thirty Sixth Conference on Learning Theory , pages =.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Proceedings of Thirty Sixth Conference on Learning Theory , pages =

Reference 79

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:61d5530ebc7f763b97bdae008930f220541bb8bb65081ee791f8d1dea19fc3c0

Observation 7bdaed13-60d6-43df-90b8-ede113076cfa · outbound

This paper cites 2023 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2023 , eprint=

Reference 80

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:0b003197bb2956594743164af9c66378ee7325d22a9bb558ffaf83b230c01ad0

Observation 20e6a673-62db-4827-a241-557b25abb557 · outbound

This paper cites 2024 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2024 , eprint=

Reference 81

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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:242f78c8d86f065a30575e15a30d9573a551ebbfb28d10b958bf0a7f419755cd

Observation 8cfeaed1-1559-41fa-b844-0ca1e738b0e4 · outbound

This paper cites 2016 , eprint=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization 2016 , eprint=

Reference 82

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no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:ee52d2cfd03a36502e5d6129d87dc2952abd87e56561f49d5739cd6a068eaaad

Observation 0bf06aac-eea0-4209-b900-3df08453abaf · outbound

This paper cites Frontiers in Psychology , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Frontiers in Psychology , volume=

Reference 83

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no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:8cc7f3baca81c38d000b13b5d59cc357ad2d8006e0f872a234865978c18c378b

Observation 51657377-a24c-4670-aca3-13f3c8629f36 · outbound

This paper cites Frontiers in psychology , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Frontiers in psychology , volume=

Reference 84

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no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:eff47e65f35922c8a32868d2bc32f5520c8b3074bf59ff241a8322c8f73454bc

Observation b3d56549-5099-410a-b616-b6272a0bf368 · outbound

This paper cites Kingdom and Nicolaas Prins , keywords =.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Kingdom and Nicolaas Prins , keywords =

Reference 85

Resolution
verified exact
doi, observed 2026-07-14T14:10:36.678647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:83fbabde7baf94fca03e642c8c7a63245a9b928905cd93f7e5213d7d9284e02d

Observation a938755f-ecf6-4201-ad81-e4c1597d5e8d · outbound

This paper cites Kingdom and Nicolaas Prins , keywords =.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Kingdom and Nicolaas Prins , keywords =

Reference 86

Resolution
verified exact
doi, observed 2026-07-14T14:10:36.674305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:49072cec6672cbffcb134bd057fed5ba43db8e73b5cbd94f326a4276257d5ec0

Observation 86cba196-1bae-43ae-a8c4-bc61ea77b3fd · outbound

This paper cites Neurophysiologie Clinique/Clinical Neurophysiology , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Neurophysiologie Clinique/Clinical Neurophysiology , volume=

Reference 87

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no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:bb80d64a4d781830d2f25a1b7a77a91c612a00d2ffbcf3a226f6f9b54073b607

Observation 556aab21-2057-4d5b-b15a-9e3d2f2a4958 · outbound

This paper cites Frontiers in human neuroscience , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Frontiers in human neuroscience , volume=

Reference 88

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no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:14f803a5ea93120f60c1a9ef5e4eee68b7c5f74663c7cef2a3dc797e5ff218bf

Observation 9570d501-79c0-41b1-90b8-7d96bbc96636 · outbound

This paper cites Social cognitive and affective neuroscience , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Social cognitive and affective neuroscience , volume=

Reference 89

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no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:4eca104fb194cf37e19bba588d5fbb1699eda27c5c669052be408e450db7ff40

Observation 868270d0-8349-4ae9-addb-c796cee1f92a · outbound

This paper cites Frontiers in psychology , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Frontiers in psychology , volume=

Reference 90

Resolution
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no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:3a1aeca6db6bac1073dd9c77412fad9491d37f4f6e757d4a82525574a4374b4b

Observation 53965bb4-546f-45c6-8159-b6c2990cc271 · outbound

This paper cites , author=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization , author=

Reference 91

Resolution
unresolved
no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:654ea4c78dd3c9820b29427f6bd074abd13532d11d0dc20c0ce5075b7149faa7

Observation b1842730-64e0-4dfc-9709-699343f0afb1 · outbound

This paper cites Journal of affective disorders , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Journal of affective disorders , volume=

Reference 92

Resolution
unresolved
no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:52b97b86af949580b66dcc9cdb95fe8bb8dd5e5a2cbb2c942373c26ff40bb66e

Observation 46082768-f42a-4fe6-b736-bb343bbc4501 · outbound

This paper cites , author=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization , author=

Reference 93

Resolution
unresolved
no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:b5cb30d5f156270aaf7e4f00378749d38dfb377f0e4dd8109f1c4de5dff18e57

Observation 346d921c-3f95-4a2f-b8ec-da9a69cee994 · outbound

This paper cites , author=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization , author=

Reference 94

Resolution
unresolved
no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:32c395cb78db83763ed69b8ad7323212faf629e296fcd326bab07c11a7be8fdc

Observation 1174c719-5410-42da-a4b8-8348688458c3 · outbound

This paper cites Psychiatry research , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Psychiatry research , volume=

Reference 95

Resolution
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no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:98b59f9dceb0a9b956a2247d04d519ae6b85a692336d991344172f33ce094830

Observation f33eeb6d-9712-442e-a454-2a47dac62698 · outbound

This paper cites Australian & New Zealand Journal of Psychiatry , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Australian & New Zealand Journal of Psychiatry , volume=

Reference 96

Resolution
unresolved
no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:2f02e9ad8c1af04d7eaa2d81bb53551d4f7e2aa1c9ab95641d0e70c43854ea72

Observation a781056d-6b45-428e-b9ef-acdeb7157860 · outbound

This paper cites , author=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization , author=

Reference 97

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no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:c4fd3d13e63d5b771dddec21b4ed8a0af9c0d5cc8e4127529e7dbdd85ff92474

Observation 1b67f052-5bc6-457b-b1cb-4b4e5b7cdd90 · outbound

This paper cites Ethology and sociobiology , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Ethology and sociobiology , volume=

Reference 98

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:369feac0e1314dfed5b1edac8a17eccb25486cfe062154a3d7cac5ceade6a80b

Observation ede4282a-ac75-400e-bb35-a7bca45c3b22 · outbound

This paper cites , author=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization , author=

Reference 99

Resolution
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no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:1218014fda6c7f2f5b0c5185eea8f627b8d466a420d9eab1f905e65241a944c5

Observation c2d18528-6727-435f-a240-3629ed31fc24 · outbound

This paper cites Psychiatry research , volume=.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Psychiatry research , volume=

Reference 100

Resolution
unresolved
no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:9cff497827ee02d779e78bc575662d3d06d14cf9ca177bfd46a24d2a64a1b8ca

Pith citing papers

No inbound Pith citation observations are available.