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Paper Citation Record · LEDGER

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

As of 7 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 2026-08-07T06:34:17.273281+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=sha256:7762ec4a3fd3a159394d47e53c74bc1f9ae5e34cb1bd07da1dddb38389af1a6a

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=sha256:70d9143f40067d5fae61d53053790392f54af48e232a1a3c1934f5847786fcb9

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:f155188ea476a1cd10657ce26f5923a6a78ee76da8c422099b83d847da32f501

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=sha256:3407796f634cf45d5a205a459a16b6171d953858f1b7be4e54c918cd73468b8a

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:ca10bf206e10a424f52a697e9c6e9300d99ed83fc76b56cff5fc4cd474e75a7f

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=sha256:039198bc4b6389e04477475ee92ddbd17d40b3c316cc65b412dfab08f4f581bb

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=sha256:4fec806a787798f0c9787729631b60fe32c7b508c03414f797b830015360e971

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=sha256:3aaef921b812ba7aeda6477a379ef0255f04972fd6e7312f78f72fe113548647

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=sha256:2fceb440df5948e171a14470f24812c186397a9d0cb63df09e7eeaa66c4d489a

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:ae25622ad83ffe51942d6c98ad268ed8866546562ed04773c4df09bca811a468

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:ec4119d61b7b59b22b028cbc12e9269b1ff03b38079b6b8fc9e1945634c887ab

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=sha256:878d97baba42b98e3b1007584ffc4847b85e176253b6df4283b8ec69a6405a53

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:d3f950d0ec2a9994a4f342f78c60db9b7c647d0eba06be82c7fe6d8ed78a2eb1

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=sha256:4ad65b8d41b850f0a29b0a644ed53ff34a29b0faf59507683516dfcf960d0394

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=sha256:936cf6852d3b7795b1f108efce05fd8c839812ab295d39892cf4229a960f496d

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=sha256:0875c3a2ae3e49b490d63a115b3477d16c0f8d8bc922f6a1afab353704f46799

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=sha256:303d360c5f4b374547026ee13664af7de97ba47bc7b1ca2d3ef9cdb19d93163b

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:f7d8f02f1e10632a0492a1d51e92a8ac5ec8aaa6976150d5d91ab1d6658f970b

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=sha256:301ff618f2788f4c75e6a0f976fc8da34de6c3b3dc021287005d0a09e2ff789f

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=sha256:706c3ca0bb6e85b98af8e170f9a624d228ba39d87e25e56af018f1654a041ef8

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:e3b910491851909631ab13d53a0f57cd0bb06da7c7d76bd024e6562153e3b6ac

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=sha256:7b0e36ca6395a1fbb90a757bb007923e4d3e676a503f21ebd931a30222f5adc5

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:f61599fc4b1026cdbd9daef6f9bb0c98456a5f77e800a263279f0aae9195bac4

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:ccee478ed670638e6594ac4f4e083450277696b24c3e37bcd794d933a095ea61

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=sha256:1fbe48bceaec3ec5f687e0082d05fbf6d0daa5da5af48c23bfe115008861211d

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:dfcfca76c859c15b42c26ca886d82cde612f74291d943215d459e5a5620b0dc2

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=sha256:14fe47e171d1662435ad87a9febb6b5f9bffed14378024403dcca0269b46c57d

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=sha256:9e5e4240fdddcc1cfbb56cdcc533b0ff41a27db8132512fcab467950df8c86f7

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=sha256:93783a0eec1564aa470a95005f45ab0763a9b864654bcb7f019f29ef94c20678

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:e56db046609cf6a31f1ca8b5524c6b4de1ebd86f305afc5921b94c7e7c41a61b

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=sha256:84197ddb4ab5f3f7f12ddd1b652215b06526a832ff8c2b3d8304f0c528d72296

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=sha256:2f8959c51c7bfc204dbbd372426534ff43ee5cd7da31e50da84fe345484876c2

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=sha256:63851c28f2e43c7cd2127004df497e3cdd9987fc299c1cf6865eeb827efa17a6

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=sha256:400f4cd8ab35e71e8611d00d666c2b57d25f1f3d7dfae7b0c606dace72192a69

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:d7d10a71f17e8ac29d86da1937f0e1bad4ed3d8d850909a15ee8bc67c37f61db

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=sha256:90e89dd009e9daba8e4ad96501ee3a648a704ec080d79f50dada8178bb4265e9

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=sha256:4f16b1d0edf24f122dbe90511f7279d8706c6aee808296c280ee181981c94da8

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=sha256:84ecf8ad827e581996946daaf4b45f18bf6946fd26342b2365c64f9063d5e6b6

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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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:b2ebcc41563db81440f3b7d73348661db9e00bcae1c9bd2079657a7585d3d0a2

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=sha256:3192b82c9b6246909bf85faa7fdfa8f376e77f96ad7ca8d19f111505b06cf441

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=sha256:3bbde5246757b2edea5c054b4fec4357cde3a4f9782d83f5001194c3d7da1cd2

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=sha256:8d722c18704bd3c91a7d5797489a40c8c796f6402605e28602b6be871af5af05

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-reported events for the cited work

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:4f1d5e8d6fc6478834e51a12f40d2aa9d5bc5bccef8bcc539aac82b55b559585

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:fd3dfb5d2257115e32856332b12b5c79974e8ed28c476bfb79d3a4861ea457ad

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:eb1f5653c94fa43b84032f574bd63ebb7fd6dfc43ab1aa206c025390f4fed57f

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=sha256:929280b2a128d3d1f7c804d19be2a16ca9ebcc05a130a865cdc3a0809a10cff6

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:c9709b23ce5c8f9c00798563b50e324dfa2b720691c5c2b6a9ca4e9525b3cbe2

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=sha256:82e1c141c0827110f3e211cbfbc90954b3c155da139f0a809192b92c61d3477b

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=sha256:5e3e3330300a626260e48ededb6b49de89e83d2296696197f79feff333b5623e

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:6302a93cbaa847740a601d74014f34798fed89edca8a9a12801cc67c9ec2e91c

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:dd4aec38cec48e5f943fd1b759a616c8fe1166c11aae3b807587cfdf525db657

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=sha256:90e602dc4ee6c2e4bc171e70c5b597251e82d59f830c2a39a3768dd4d66ae53b

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:e90fbda0550d44220f208c895582e41d32bf96252be0267b763406556e32b334

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=sha256:337493a7b5c32d72a7256cd0e7225c32ab3f6d88085a973495d61e7b0c7e4934

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=sha256:32043c134a64c649dd60a160ab0045534f3426cf963da55313d3b89fef84dcaf

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=sha256:1f568f5830d4ce61316b06ee7e3865bdc06318258d4cd82130b3eaef5b18f1cb

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

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:d4680beb60cf25dcc9898664d875ee4c39cf2cdb739cc5011b3d5bc8a2b6d662

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=sha256:1d6ee38d6a4f51fd8402b62dd33ebe6f40418d1e8cd3dab170b92d47410a3bce

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:a48799fdebbedf39c4e42fd24fd009908900d1b293b5fa984365e676b0408fc3

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:f68eabf4f79db6dd98eb2905e258c097eb9869e59813e178d9adbedb4c290d39

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:f9d1d5b408e2929d009e766c9fe31a92027e82a5407509bc772b688f5bf7ba93

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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Source-reported events for the cited work

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:1a522b7ffd646c1871e01785a6b63be7a3560bbfd3a24323fc98a9dbebe8882a

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=sha256:6d8782918b67841d5076ce9f8666b746399c3a8688d462fa3bd6e60573b554e0

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-reported events for the cited work

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:beebea127464829f1d124c892b6a452dff993e1070af5b78d8174383de443fa7

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=sha256:219c4e2977c5cf34f0cc6be32d156208142d05087c39d8eff9ee9da70e9b27b2

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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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:ef1fc1e03bf9dab6b4952ea682e3eddf3ad9ee0844443b2f179e48936e37e12e

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:03f1ad7f6764233a45592f29f7cf240bdb2f92eef3392de88c5a8c2ba23ea8e8

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:8f0da156008b5bbba5b4ebea5ccf9c9346267778b1c697c77ece492ecbcafd33

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:4176de34eab24c364f4e5c16eaec8fc4852b2b04843b7af14151f068bc40ca28

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:c37643047e483999944e6e5d2c8f2d4db62380bb1276d8e1c2bb53cb0067aff3

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:bda591af886afb9f254d1610ec7079a9b4995c02e9cc4237aa013cc2f6c237b0

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:45640e9fdcad4c1fd791b90ebfae7072ea378a4a817b2c33c06fd7a0b296f905

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:50852ab6f0a2466145eca44bd587ed30a06f6a39f0d60438b4bafa3373ce72de

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:f43d1de0bca55cf1fa4fb22828f69c0411240f4077d5835f915d5e1a8e73ff2e

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:63063d66f07857b7dcb7054055d33b2511bfd42e02c72044cd6f2911c9030087

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:b888cbeb3ae03200668bb497f91d4d2b06dfae7088d824a327865757b4ab1e59

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=sha256:04e201d2801acf7c703e8644ae464ee3e823f374f0a023d5dc0199f44d98d578

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:d5a7cfcadad6432c51e83a699f3db6b0ceec95ba9a78bc74b8ff8746a50a2e66

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:dda5ee4738c3db42c2f6a5b18753e9027e018cf7b1c3ddaee548dddaa8502a97

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:c5d2eaf2c153ef4b9d7662c5cb42625f16bbf4b1e3103d3f7e1a7318459a8a8b

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=sha256:21222c596fa1245a7ebfb2d79c54106770a8f7962c1ce0b21e38088151f11ea1

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

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=sha256:879429153144b2e31f8a4cc2858de6b99f34cd88d918c48545d5e1d2964a4bd0

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:5f673cb87e42c6c52795549c373c11778213a59ca724a673b87f3e9d717c4554

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:0b533e45c9a47009248a36c05533c2eab6a227ff8690ced9afa572a1048bd034

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

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:2ae5ea502352cb527da60caf76881839a5279f654b2d4d3311dbb92148f75c25

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

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source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:93455e619e63f35e8b58e8bc688c78cc0170a4804891e083ffa3b0670484f0ec

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

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=sha256:5bf4d71f12eed258cf51f3e848cc1870ce4b8eed0ff95b4fa72bda6a5c03eaff

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=sha256:56e3e069d1b1835061f822bc60bbab296c269e08bdc06cf861011f1195057ba5

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
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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=sha256:5ea536f60dc3cfcb37243e57ab1d0d4a5bb030408a168d8f78a46d1cc8da232c

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

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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=sha256:175b75f507685c189e8584b0f12ef4d286b5b3730d4c0bc5f7ace5feb1897487

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=sha256:1c3caefa30f52bd4e39b8e5292bacfb3cab13f73d177a90394bafa63ace25cf2

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
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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=sha256:3318163ba69f015686b8062cd84b4a1c5f355ee0390e72a4b90e1c8406e6243d

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=sha256:2ad0c12e689e4902919a0c76c0bf5ad3889cf979dee7fb064e8de6c33c207b42

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
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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:e38d7fe338c3eb887d4ff6a4b6d1860933bfae27b272b780442b6d4adb6075ac

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

This paper cites , author=.

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

Reference 97

Resolution
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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=sha256:132fea417fe9a02625a0944f86d833935b52118aa08230e4bff94f79d08357d2

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:fcb18b6b1dede9657fad1a72769e29b2fdbd49a6ae0aa287ea899f09b297e603

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:e226afecb3f01351e3cb70b87e47e6e127dc560be7dea1e56b63997aedcb1b12

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
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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=sha256:2b8aca52249721614b5b9bb77f11d84298df8401dc4fbee78d71be664374cc95

Pith citing papers

No inbound Pith citation observations are available.