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Paper Citation Record · LEDGER

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models

As of 12 August 2026, this Paper Citation Record lists 100 of 294 outbound references and 0 inbound Pith citation observations for arXiv:2608.08010.

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

pith.paper-citation-record.v1
2608.08010 v1

Coverage vector

measured 100 of 294 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:39:40.739274Z

measured 100 of 100 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

100 of 294 outbound references displayed

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External citation measurements

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Outbound references

Observation a1b4a79d-977e-455c-ac46-a0e03dacf73f · outbound

This paper cites Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

Reference 1

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Observation 31eb6647-888b-4d4f-8e1c-13462fa2b28f · outbound

This paper cites Classification Problem Solving.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Classification Problem Solving

Reference 2

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Observation 8adb295f-0549-432a-a512-7681b59507a2 · outbound

This paper cites , title =.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models , title =

Reference 3

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Observation ebe48ca4-25a9-4c43-aa74-0eb5d2b4ca41 · outbound

This paper cites New Ways to Make Microcircuits Smaller---Duplicate Entry.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models New Ways to Make Microcircuits Smaller---Duplicate Entry

Reference 4

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Observation 3bed65e2-2ab9-4c9f-ba09-76b3353d3a1f · outbound

This paper cites Clancey and Glenn Rennels , abstract =.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Clancey and Glenn Rennels , abstract =

Reference 5

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Observation cd3818f9-eb0a-495c-bd14-a3702cebec64 · outbound

This paper cites and Rennels, Glenn R.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models and Rennels, Glenn R

Reference 6

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Observation b3ab1c9a-452e-4c94-b0f3-38fb0bed42d8 · outbound

This paper cites Poligon: A System for Parallel Problem Solving.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Poligon: A System for Parallel Problem Solving

Reference 7

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Observation 1c8cf648-1742-4e2d-a839-1cfb3c287a4e · outbound

This paper cites Transfer of Rule-Based Expertise through a Tutorial Dialogue.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Transfer of Rule-Based Expertise through a Tutorial Dialogue

Reference 8

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Observation 3823fe2c-d888-4ac7-850e-ef4fc2fffd54 · outbound

This paper cites The Engineering of Qualitative Models.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models The Engineering of Qualitative Models

Reference 9

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Observation 47d552a7-4a02-4214-b08d-1b53d593399d · outbound

This paper cites 2023 , eprint=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models 2023 , eprint=

Reference 10

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Observation 0e3c10aa-8b72-4e47-9178-7b25a46c22f4 · outbound

This paper cites Pluto: The 'Other' Red Planet.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Pluto: The 'Other' Red Planet

Reference 11

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Observation a4baccb4-b367-4a7f-a6d7-2d579489fa00 · outbound

This paper cites Structure and Interpretation of Computer Programs.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Structure and Interpretation of Computer Programs

Reference 12

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Observation cf16c2f5-b316-4aa2-acde-264297b4a1ef · outbound

This paper cites Visual Information Extraction with Lixto.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Visual Information Extraction with Lixto

Reference 13

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Observation 58b64132-0bc8-4ed8-9286-93527ff13ee5 · outbound

This paper cites Brachman and James G.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Brachman and James G

Reference 14

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Observation 3420f60a-6cc3-4254-883c-c602c77d3c95 · outbound

This paper cites TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning

Reference 15

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Observation 3bdb3144-0d48-47cb-ac03-c4e52d2fdd0c · outbound

This paper cites 1991 , publisher=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models 1991 , publisher=

Reference 16

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Observation 688c5b77-b85a-4755-8956-3a59f68d2f2f · outbound

This paper cites Complexity results for nonmonotonic logics.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Complexity results for nonmonotonic logics

Reference 17

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Observation 4e6a1225-7e76-413e-8508-75e8ab302a1c · outbound

This paper cites Hypertree Decompositions and Tractable Queries.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Hypertree Decompositions and Tractable Queries

Reference 18

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Observation bbb7e726-d46b-45b4-a811-d04a1de4bbee · outbound

This paper cites Levesque.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Levesque

Reference 19

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Observation 2a822466-b717-4ab1-9930-b92be40316ea · outbound

This paper cites Levesque.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Levesque

Reference 20

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Observation 4a45db41-c414-4f82-b780-f886e090ba97 · outbound

This paper cites On the compilability and expressive power of propositional planning formalisms.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models On the compilability and expressive power of propositional planning formalisms

Reference 21

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Observation cf4dfac3-2bb5-4ef3-aefc-207af30789b1 · outbound

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models ICLR , year=

Reference 22

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Observation 11293d24-e605-4293-91ca-2097c9c20822 · outbound

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models ICLR , year=

Reference 23

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Observation a6d68388-7b03-4467-8bc0-47f5b6bb7bd2 · outbound

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models ICLR , year=

Reference 24

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Observation d801560e-3a0b-4f85-80c7-7600b2ad1c87 · outbound

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models NeurIPS , year=

Reference 25

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Observation f0a0a609-ae72-4681-8375-fd433b0aebf0 · outbound

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Autoformer: Decomposition Transformers with

Reference 26

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Observation 0738b952-d266-46a3-8cd4-8c5e42253b1c · outbound

This paper cites Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Reference 27

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference , pages=

Reference 28

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Observation 38e991a7-0c5c-462e-bf76-731375fcf654 · outbound

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models International conference on learning representations , year=

Reference 29

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Observation cdc6869c-268e-4422-a568-b476ab5083d3 · outbound

This paper cites Expert Systems with Applications , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Expert Systems with Applications , volume=

Reference 30

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Observation 3b6d4b09-589b-431d-bcb4-11e55431a729 · outbound

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models NeurIPS , year=

Reference 31

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Unresolved cited work

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Unresolved cited work

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Attention is All you Need , year =

Reference 34

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models , journal =

Reference 35

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models NeurIPS , year=

Reference 36

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Kingma and Jimmy Ba , title =

Reference 37

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Observation 6684227a-c984-4b30-b3e0-41d704a85899 · outbound

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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

Reference 38

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Observation f89a912e-b77e-42d7-bffb-a42a201daece · outbound

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

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 39

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source=arxiv_source observed=2026-08-12T00:39:40.348413Z digest=sha256:b60fbcaafaa1cca3e04d7dd26fbf1f5a8189075eff6d895dd52f94ac2152743b

Observation 24b55ed5-08dd-4ce8-8426-2e905b7fc5f7 · outbound

This paper cites NeurIPS , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models NeurIPS , year=

Reference 40

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source=arxiv_source observed=2026-08-12T00:39:40.353212Z digest=sha256:40cb452075bb83e64cd81602b0d7e220c33404225d45c76a7d3b8e5238d85cfd

Observation d0657305-4c48-421b-800b-bc9dc37cc157 · outbound

This paper cites SIGIR , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models SIGIR , year=

Reference 41

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source=arxiv_source observed=2026-08-12T00:39:40.357370Z digest=sha256:256c956dab80730eff284797cf42504d1d902a1b1f28f654bf580db51a1b9ace

Observation 6357e838-eae8-4957-a2fc-ccb177f5383a · outbound

This paper cites IET Intelligent Transport Systems , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models IET Intelligent Transport Systems , volume=

Reference 42

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source=arxiv_source observed=2026-08-12T00:39:40.361554Z digest=sha256:be5071c86f1a0a8dc3dc680ffe1091681d9c12a0650839b3cccb2805783adc84

Observation e0553e90-d62b-447c-965f-08778ebc2396 · outbound

This paper cites NeurIPS , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models NeurIPS , year=

Reference 43

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source=arxiv_source observed=2026-08-12T00:39:40.365646Z digest=sha256:edb4b06b4bb3859218e959cb47058b93d3185e58c4da92be8994e7a191c5ee38

Observation 6805aea2-0042-4c20-8730-eece2cc4ac66 · outbound

This paper cites ICLR , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models ICLR , year=

Reference 44

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source=arxiv_source observed=2026-08-12T00:39:40.369817Z digest=sha256:d1338d372614aa7698501d189ae3c7d6f10d66a4ddb7c47755653accaabfca93

Observation 747c267b-587b-4450-917d-d6600b7c4f90 · outbound

This paper cites Scaling Laws for Neural Language Models.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Scaling Laws for Neural Language Models

Reference 45

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source=arxiv_source observed=2026-08-12T00:39:40.374154Z digest=sha256:0440b393472397cfb3c4cef83eaec688f9e7ba5b465dc18d9f8d270bc3f23bf0

Observation 8e75d9f8-ebda-4b51-bf59-268d9a95cfc4 · outbound

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

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models International conference on machine learning , pages=

Reference 46

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source=arxiv_source observed=2026-08-12T00:39:40.378692Z digest=sha256:62c8eaa6fca0492949cfc82f1a04ac70955c02a66cf5595aa01bc42eb7945ef4

Observation 9c467523-33ae-4829-8cf2-12fff1517226 · outbound

This paper cites KDD , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models KDD , year=

Reference 48

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source=arxiv_source observed=2026-08-12T00:39:40.387511Z digest=sha256:8893121dcf33fffd0cbf57c9560cd74678ecccfd630fc68ec82da565163b8f2e

Observation b3a8ec08-14a7-4b04-8dc8-fadba6ee7dd0 · outbound

This paper cites Long-term Forecasting with TiDE: Time-series Dense Encoder.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Long-term Forecasting with TiDE: Time-series Dense Encoder

Reference 49

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source=arxiv_source observed=2026-08-12T00:39:40.391794Z digest=sha256:731328b61d0850f85758c39eb25a8b78fd7a7cb52fc5ba596b5c53f26c5ffe11

Observation 43181f80-ef4f-4d42-8274-735493ec9faf · outbound

This paper cites Journal of the Royal Statistical Society.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Journal of the Royal Statistical Society

Reference 50

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source=arxiv_source observed=2026-08-12T00:39:40.396674Z digest=sha256:0441a9bcf535190aa1ccc4aebd9d4fb4e47d2591d45f50310957bfbad8bf5f5e

Observation b1c331d6-78c6-49d7-8de6-d48ec3ffacea · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 51

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source=arxiv_source observed=2026-08-12T00:39:40.401093Z digest=sha256:4bcdf62b9cedb0053f7d29d3e2eccf284f1ec3952474bd421eb6f7f52caf2a1e

Observation 601bf023-dc65-483c-add5-fa8cae18a3d6 · outbound

This paper cites ICLR , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models ICLR , year=

Reference 52

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source=arxiv_source observed=2026-08-12T00:39:40.405628Z digest=sha256:5f76716fd64af1d621195a72cd272fbed9d3175fbb531543052e4c77c6c620a6

Observation 6b4238bc-6dc2-4df8-8378-5bd3d192cb33 · outbound

This paper cites Neural networks , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Neural networks , volume=

Reference 53

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source=arxiv_source observed=2026-08-12T00:39:40.410124Z digest=sha256:b1242c7bf00cb9d129046fda843b333cf4786e3b7a64a9e81f5ecca1c05c6896

Observation 5865afe3-8799-432c-b67c-8dc670338f9d · outbound

This paper cites Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping

Reference 54

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source=arxiv_source observed=2026-08-12T00:39:40.414609Z digest=sha256:c3cf9f63a5b6a8ed1423f1645eaab78be60d1997f6b9ffaa0a6e5debd5a0b267

Observation 7e252469-3ed1-469e-ba10-70f0f96f7217 · outbound

This paper cites ICLR , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models ICLR , year=

Reference 55

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source=arxiv_source observed=2026-08-12T00:39:40.419819Z digest=sha256:5b291ed664d97462c4652f9b3f4f50f38bd5d1e19caf04e676f823bc887cf8d5

Observation 550705d2-41ab-4e80-991e-7a8dfc976c4a · outbound

This paper cites ICML , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models ICML , year=

Reference 56

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source=arxiv_source observed=2026-08-12T00:39:40.424174Z digest=sha256:f5bd63cdbd2f8dfd0d35861d5707e54b14798b686059c46a2b1173c64ae4346f

Observation a1816ee4-83db-4a5a-a10a-49662fa59419 · outbound

This paper cites ICML , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models ICML , year=

Reference 57

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source=arxiv_source observed=2026-08-12T00:39:40.428406Z digest=sha256:b62b10e6b80a1fb1a2f368b5ff1075742f615d8a5e71fa2e2ca4f27d9fa010fd

Observation e9ef2e67-7702-4e68-93e8-410282eecfb0 · outbound

This paper cites SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

Reference 58

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source=arxiv_source observed=2026-08-12T00:39:40.432735Z digest=sha256:c5b9f0c9e84a2b8454bf57ec260d228b72dbda6600df215dc7848e13230c4f55

Observation c5c466eb-1a0f-45fe-89fa-b700be28e665 · outbound

This paper cites NeurIPS , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models NeurIPS , year=

Reference 59

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source=arxiv_source observed=2026-08-12T00:39:40.437202Z digest=sha256:430e1b724c3568c83f5131cbb4c2da42a1fc5a2841055e30fd09366ae9c3815f

Observation ffdf587e-93c0-4f8e-aa42-c49fc451032e · outbound

This paper cites International Journal of Forecasting , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models International Journal of Forecasting , volume=

Reference 60

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source=arxiv_source observed=2026-08-12T00:39:40.441638Z digest=sha256:e303c661c90c8d41804d450c772ad9888d67cd49cf9f02a9190bf09c6e9aff95

Observation 753ace23-40b6-42a7-9d1b-09131dd962e1 · outbound

This paper cites NeurIPS , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models NeurIPS , year=

Reference 61

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source=arxiv_source observed=2026-08-12T00:39:40.446211Z digest=sha256:98244c256c666f8160933bf1d0507adaf70725549e3565289a963a0cefae415c

Observation e5b2da04-a8e7-4165-8086-f42820050a3c · outbound

This paper cites The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

Reference 62

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source=arxiv_source observed=2026-08-12T00:39:40.450753Z digest=sha256:b5b9153af1d82bf77f5a58e577838870857dee95558660162636a23aeb1d4dda

Observation 935ee5a2-755c-427f-9e7e-6402d3cdbb8b · outbound

This paper cites Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors

Reference 63

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source=arxiv_source observed=2026-08-12T00:39:40.455714Z digest=sha256:f9c9fc48d00f1a7bfa449a608ac52581d3b8de915fc47f950f6fe0ebd53c075f

Observation a5fc9ad2-a4a9-4028-9a65-764fd2418220 · outbound

This paper cites Proceedings of the 32nd ACM International Conference on Information and Knowledge Management , pages=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Proceedings of the 32nd ACM International Conference on Information and Knowledge Management , pages=

Reference 64

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source=arxiv_source observed=2026-08-12T00:39:40.460435Z digest=sha256:0b00d02127e48d67d1aa3a8f54c4f9ffecb783a5f8f121becdc47716a63c8f73

Observation 9115868a-b97d-4815-bcb7-0a6508669d93 · outbound

This paper cites Human-Centric Intelligent Systems , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Human-Centric Intelligent Systems , volume=

Reference 65

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source=arxiv_source observed=2026-08-12T00:39:40.464712Z digest=sha256:fc5a9544f18f37468debb4a941ed0fb6393fc6b4bc637b07e980132c0d1f39f8

Observation 622b3b62-99b9-460b-b53c-e518891101ff · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Adam: A Method for Stochastic Optimization

Reference 66

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source=arxiv_source observed=2026-08-12T00:39:40.469229Z digest=sha256:08c91deab79ab4a2954076357e70ed2e9b7f01b1baa1bcc12be8df1f7592516b

Observation 8d7b15ab-bc47-4d66-a4ec-28d2a9b3b806 · outbound

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

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Advances in neural information processing systems , volume=

Reference 67

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source=arxiv_source observed=2026-08-12T00:39:40.473807Z digest=sha256:d680e83d81f3b59b5ee68526d6dc2abbb529edfd03547758d145426ed2578c25

Observation 39a508b2-83b0-4da4-964b-2a1b6a10696c · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 68

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source=arxiv_source observed=2026-08-12T00:39:40.478015Z digest=sha256:07aeafd93636c67db5cdae264e1cbd302af4f5e0b085c17c4156022ca457d31f

Observation 91a67d45-1e59-4c0c-9ca4-8297e9a9837d · outbound

This paper cites Conditional Positional Encodings for Vision Transformers.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Conditional Positional Encodings for Vision Transformers

Reference 69

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source=arxiv_source observed=2026-08-12T00:39:40.482608Z digest=sha256:0418f4c4de76ef76cf7b868fbbb88f2121689cdab6859a561e1d3be3bf597c19

Observation a2013fa9-3d44-4930-abb1-4ee0f6b24b3f · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 70

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source=arxiv_source observed=2026-08-12T00:39:40.487294Z digest=sha256:974226193f90f41b4de78e716e97b7b0e6cd80ae19f8419301d06f03d7bb84e3

Observation 3e9a2e33-771a-4da2-a734-5ffe0bf3c4ee · outbound

This paper cites European Conference on Computer Vision , pages=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models European Conference on Computer Vision , pages=

Reference 71

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source=arxiv_source observed=2026-08-12T00:39:40.491552Z digest=sha256:75f155a9943847d2a49b5deea9f3671be62a7350f806b42d8c05da9aa2c4bfa1

Observation d74949af-85bc-461f-8874-303cee2712db · outbound

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

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

Reference 72

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source=arxiv_source observed=2026-08-12T00:39:40.496028Z digest=sha256:42af891dc4b59cfbc85a424cd73f865de9058235d031c07770c49b71b139e4f2

Observation f314aa1b-5dcf-47c4-aa76-5e1aa9fd14a8 · outbound

This paper cites Proceedings of the IEEE/CVF international conference on computer vision , pages=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Proceedings of the IEEE/CVF international conference on computer vision , pages=

Reference 73

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source=arxiv_source observed=2026-08-12T00:39:40.500486Z digest=sha256:3d325c07bddcf42d931954d8e4c5a365b9bed7b3b5b5e868cec9399e3010d8c6

Observation 59b6d19d-45b1-4b3b-a71d-d217fa8f3256 · outbound

This paper cites International journal of environmental science and development , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models International journal of environmental science and development , volume=

Reference 74

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source=arxiv_source observed=2026-08-12T00:39:40.504815Z digest=sha256:ee12017f11911fd34624af8c8bade38b1a91caa2480a9c939c0750b8a0027e97

Observation 4a9ad753-57e1-4c1c-a32b-fd9d37bf3404 · outbound

This paper cites Renewable and Sustainable Energy Reviews , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Renewable and Sustainable Energy Reviews , volume=

Reference 75

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source=arxiv_source observed=2026-08-12T00:39:40.509246Z digest=sha256:fa031636f1b3f0aebda39226ac6de16810826ac3bb48e830251b8fe18c8e9b26

Observation 0a01e674-4dbf-4401-bfd0-3ad2d0cc6090 · outbound

This paper cites Renewable and sustainable energy reviews , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Renewable and sustainable energy reviews , volume=

Reference 76

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source=arxiv_source observed=2026-08-12T00:39:40.513482Z digest=sha256:c27b448196365d631d85bd2539769e9de5cea6a09b0fe6d8c4e3cf6bcbcfde5d

Observation 85aba4f1-7edb-4bef-84c9-a98b6e63ed96 · outbound

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

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Advances in neural information processing systems , volume=

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source=arxiv_source observed=2026-08-12T00:39:40.518830Z digest=sha256:e37d63120531087c9d66532e46c4ebe8cc913ba9da3e85fe5a453bb30615e63c

Observation d89f1d04-7dc5-400d-9700-cfe145f9c841 · outbound

This paper cites Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case

Reference 78

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source=arxiv_source observed=2026-08-12T00:39:40.523229Z digest=sha256:b5e780e00aac2396bded2227bd07bbda2bc52af0bfe0ed9c20d6f808e7334fcc

Observation 1851ee5f-5a8e-4274-bf71-6ccd0ae452c2 · outbound

This paper cites International Journal of Forecasting , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models International Journal of Forecasting , volume=

Reference 79

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Observation 75f79dc7-2fe0-4324-860b-8156b78f9286 · outbound

This paper cites Noise reduction in speech processing , pages=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Noise reduction in speech processing , pages=

Reference 80

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Observation ee7a8e71-4144-4cbf-86bf-2e83d47ca1a4 · outbound

This paper cites Probability Theory and Related Fields , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Probability Theory and Related Fields , volume=

Reference 81

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Observation 4a3c3687-70c1-4a94-a7e5-47dc9c9a5edf · outbound

This paper cites Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23--27, 2020, Proceedings, Part III 27 , pages=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23--27, 2020, Proceedings, Part III 27 , pages=

Reference 82

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Observation 2ec7d8d7-bfef-4f2b-a16e-a9efa2d8ec7a · outbound

This paper cites Neural Networks , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Neural Networks , volume=

Reference 83

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Observation 5939558a-9dd3-402b-a962-875ff7b675aa · outbound

This paper cites Applied Intelligence , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Applied Intelligence , volume=

Reference 84

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Observation f29a16e2-b8cc-4523-9722-ff55478ef94f · outbound

This paper cites Applied Intelligence , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Applied Intelligence , volume=

Reference 85

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Observation 20ca69bf-ae28-4340-b566-c821ff986390 · outbound

This paper cites Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?

Reference 86

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Observation e05a7cc8-e18e-4f14-8f6a-52346b8bce37 · outbound

This paper cites Proceedings of the 32nd ACM International Conference on Information and Knowledge Management , pages=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Proceedings of the 32nd ACM International Conference on Information and Knowledge Management , pages=

Reference 87

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Observation 118ca86a-8093-4f72-8b24-9344d426ca79 · outbound

This paper cites ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 88

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Observation c09443d2-32f3-4fa4-b4d6-c78e779d1e69 · outbound

This paper cites Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem

Reference 89

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source=arxiv_source observed=2026-08-12T00:39:40.682113Z digest=sha256:804cd970881df78362b75e05eed010a4519054029d057e0b0ce49401b0ccee16

Observation e14003b2-284b-4e03-8277-2e1c687fc37e · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 90

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source=arxiv_source observed=2026-08-12T00:39:40.686897Z digest=sha256:f08ddc84308c3a97b55124dd505a51801a3a7f4eb5e9112c82b972b05da2c3a1

Observation caaf59a1-15ff-47ad-abf6-3bc256826816 · outbound

This paper cites Neurocomputing , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Neurocomputing , volume=

Reference 91

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source=arxiv_source observed=2026-08-12T00:39:40.691739Z digest=sha256:bc12a4df5ffe4fa6c0a3c369bfe45ee880ea11fad805b01bb0c88de602bdca6f

Observation a6bfb6c0-24bc-4fe7-99de-7ae16fa5cd8d · outbound

This paper cites 1999 , publisher=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models 1999 , publisher=

Reference 92

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source=arxiv_source observed=2026-08-12T00:39:40.696159Z digest=sha256:7b7f23c7026f352d0c53ff8941c1aa80d2ae93371f0f421e9fe7a3898ccbbb29

Observation 05cf98b0-4ae1-461d-922d-dafe6856e76f · outbound

This paper cites How Much Position Information Do Convolutional Neural Networks Encode?.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models How Much Position Information Do Convolutional Neural Networks Encode?

Reference 93

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source=arxiv_source observed=2026-08-12T00:39:40.701277Z digest=sha256:50192b857873ecbbd90a7e24ba214f11a526946a1a75bee7672c66c019bc8a74

Observation 3394d58e-9a33-4403-9dbe-32c388ff0923 · outbound

This paper cites Proceedings of the IEEE conference on computer vision and pattern recognition , pages=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

Reference 94

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source=arxiv_source observed=2026-08-12T00:39:40.706864Z digest=sha256:e3a7f90ac1910baf93e21de72918f0f40db5a76263446f40b9483d0143a6e034

Observation 4d68ad04-36e8-4f77-bd90-db442cbfa062 · outbound

This paper cites IEEE Transactions on Information Theory , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models IEEE Transactions on Information Theory , volume=

Reference 95

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source=arxiv_source observed=2026-08-12T00:39:40.711637Z digest=sha256:822ca6d107872889ee1af6d916f40ba821b6b52d72cf57427b03799df2c48d8e

Observation 48e1e589-a9f0-409a-8561-bd89724b6b57 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

Reference 96

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source=arxiv_source observed=2026-08-12T00:39:40.716834Z digest=sha256:a1ce831d8e6895e665252867390b3138a5474ebef73b4af6615a698ca513c9be

Observation f0253882-19da-420e-8d20-a17d613e8d65 · outbound

This paper cites Bioinformatics , volume=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Bioinformatics , volume=

Reference 97

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source=arxiv_source observed=2026-08-12T00:39:40.721245Z digest=sha256:61d53b56efcdcd889bf00b7c9d7f05b41fcc1143e73e312142de0fefc5bf77aa

Observation f4e27433-8618-4d99-9bc2-ee3774e21456 · outbound

This paper cites Biocomputing 2000 , pages=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Biocomputing 2000 , pages=

Reference 98

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source=arxiv_source observed=2026-08-12T00:39:40.725530Z digest=sha256:1ba6c5cad2706029127d4ab20b84771c1da21a8a51088ee2eb3ec9fd672928d3

Observation f5ed6330-c61b-4dd7-8edf-c9d73148e3d7 · outbound

This paper cites IEEE Transactions on Knowledge and Data Engineering , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models IEEE Transactions on Knowledge and Data Engineering , year=

Reference 99

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source=arxiv_source observed=2026-08-12T00:39:40.730054Z digest=sha256:d9783d7d39d12681d0be0cfb20a3a2a7ecd2df688c366d02b7d9e4be1c07a1ff

Observation dbd7f645-a15b-42c3-8138-301e12266033 · outbound

This paper cites IEEE Transactions on Knowledge and Data Engineering , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models IEEE Transactions on Knowledge and Data Engineering , year=

Reference 100

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source=arxiv_source observed=2026-08-12T00:39:40.734588Z digest=sha256:bb6fc75e1461300818a894ce22d1689321370191c862b0e0916f57e56e92a30b

Observation 1bf931ca-9611-4bb4-bc5f-798a5f9c9e0d · outbound

This paper cites IEEE Transactions on Knowledge and Data Engineering , year=.

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models IEEE Transactions on Knowledge and Data Engineering , year=

Reference 101

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source=arxiv_source observed=2026-08-12T00:39:40.739274Z digest=sha256:ce0279e9fa5ad96dc634fe5ab6c0a5b307a22aae51c3a126c045f4e14e2a0fe2

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