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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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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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Observation ff3a4fd2-d4cb-45a9-b3bc-8f997c43ca16 · outbound

This paper cites Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference , pages=.

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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Observation dd1055d1-7480-49c0-a9ac-c7b08c9f32c3 · outbound

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

Reference 33

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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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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:bd871bc8016fc118fb2dedcfe6a1892520cb4ffcec8d87e5c62d04a3d046a736

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=sha256:808dafb7b9b029cb08e5d1db7e9ea46e9e4fce0ccc46ead77b7841c2669f8c55

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:ed23a16a7e27ff7c007c69eb58c075ea7aee1ce31da56317e0bc3f06eb001260

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=sha256:308808314e79a06bb2d0744c110e06a913cfe1818f048c37d9c7f23a6b5775b1

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=sha256:859800be00433cab1644e9899d28c09263b73a8b7ab50a778f526a5492e8b9f0

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:fdf32793e9317c3eaec48261b682fd75debd65fcb61c18b72df6eaa491f484c8

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=sha256:0af25d316f1bb3c7a5722651ca6489d8d7f3ff5bbb26b0bd9786ec45db1e3b7e

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:dc238adddb3bd4e8146289f51fbb2b3dd07dd5f3d91a30eb2c3aef3f5c7a1b3b

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:e99a14464bfcf248a79f568e7251391182f39d3f07a04ebadfef2ce699a9a8fa

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=sha256:008e850bb5509e0becb61eabe3c289956c824b683f41a46891097f9de0c6098c

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:f3ddbb0480eedde7778ca1198441da395322f60930368d838d804bd45a85dc05

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=sha256:66f6daa662009eb1b4a3d3f080ebc7358d9c8e48a49a078c4cd8b758775de30f

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=sha256:5aa44f6fadd0c2d815bc5afd0bdbb456756afa189d74b95d53f1c76274ff3137

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:c4a246eddd21005a941180ad0c1a7e44fc6a5e9630cf5da5c2935c2ed484857b

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=sha256:50c64d0712d32fd90fe82d49e08fce6b29843baebffee97a69169c2bdd039a92

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=sha256:448e800b250532a7127124dea1306745c0a7b86d03f16b0cfa5d9b361b469e83

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:a58dfb2a3083437ff20cea60519fb3860bb59511825dbe1e303bff69b51a4d4b

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:e682f6d8c88edbaa25ae3545014c2abac88edcb30c3103f1941089d3275e8da4

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=sha256:0c382d8ce49e892e07a14898d1f9dfc8376a03d3723af44f46b46c0b894b3db8

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=sha256:673f9d92d5b0e73b5ee17bf50892a8ce08976defe51053cc55b9a0836984e1a8

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=sha256:10bb8064d9682d06eeabcb77dd194d60e94aca9b8d700353ee4d398b582a6cdc

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:c5039d9b992603a0a67391a339bbd4c784a0782252a68f348fdaf21b221c71bd

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=sha256:59b98aada384212e2494a6360bf402d99789065dd7579dfad3427c720dbf40b7

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:aab627d97ae9fbb4b929ff8641110a931cb2734cd31cb81f6c3917306a1a8eae

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:f7d99aefce0054aa24f97161fd3204757d35ed293fbac9e6c98aaa02e09550f6

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=sha256:988a287b5d6b30ea407ea74fd356eee2f4a82bed27cc2a995a360b6cb0dfe460

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=sha256:3942e6ad480e3409f81efe37c559fd9b14eb0caf5e3d89958cc895748f481756

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:d871d9ac172426a00b8cd12669ff15e3cba352026072fe798769a109b8685680

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:e44305b29c874f7fc5047cdb91e3496421944c317d1fcba723d5881adc13d584

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:ca1b02252f14afefcc9ec7d4d193dfb21f776d6bf98b213dbdaf0c8285f11b28

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=sha256:15570e07be58c01ac8d0ecf8dcdb6de74c8ca2bc0f517a2e85bde5b8bbd1e5c5

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=

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source=arxiv_source observed=2026-08-12T00:39:40.496028Z digest=sha256:e4c544274aacede9123171100c6b76f685558f398fec6a94466f6fec05adc70f

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:b03ce838c34ec54397d7ad89c4c067a3ac4ba273d14fd7602050d930c85b56a5

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:d4aa035631735f6ceacc76d6939596db4ea15a345a2e4f5ce72f637de0f7eea7

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=sha256:51b9c3c26949ede604920869abdf995dba62f52d66a50248f809fc2b5d585057

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=sha256:13f7320692b4aeb2859b016dd00b42cb6fcc75618bd9525e243a13613d2be413

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:e62d12d2c3a8ae093dc643b1126f1b84a7420989d16acd4365d542ce6b803b49

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:abc25ff3e17beef52c940c5028ec0d233299c983720d77fad9929c19532fea57

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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source=arxiv_source observed=2026-08-12T00:39:40.528033Z digest=sha256:37725325b09c08000736748fdaff07d133aa5ac842eabe3f1dfb994f994ab8af

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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source=arxiv_source observed=2026-08-12T00:39:40.532678Z digest=sha256:7e68c0d4a980d456f4254918dd6146f6c6f0f57fcc0af7cd7ca416780e1e974d

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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source=arxiv_source observed=2026-08-12T00:39:40.537204Z digest=sha256:5e8cd459f441db647be7e7dd7e1ee3ec8be864acb1057a235b963fd48a5eb3f6

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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source=arxiv_source observed=2026-08-12T00:39:40.545930Z digest=sha256:3b5b44674d554bfed63dad399c8bf5fb837ef243347b89d0bb74fc9c352ce6b5

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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source=arxiv_source observed=2026-08-12T00:39:40.550284Z digest=sha256:50c0d493313c184ea4baec8c514a1b758678255148aaae5747f705d7c86cd29c

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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source=arxiv_source observed=2026-08-12T00:39:40.663690Z digest=sha256:c3c98483451467412643981786748d176e91b45e6411b46f1ec758b30c3966c1

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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source=arxiv_source observed=2026-08-12T00:39:40.668429Z digest=sha256:471b688e844aca22ad1bfed1f14ab6634ff6f09693bb5fdc6d90dd12df48c6e5

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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source=arxiv_source observed=2026-08-12T00:39:40.673231Z digest=sha256:0a7cf3a4e5f54f50768601736fa50579f3e296c877d64c5506f3aee45a68fbf5

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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source=arxiv_source observed=2026-08-12T00:39:40.677671Z digest=sha256:e72d2f6c2f0e607b0ca4cad84e86e55449840d352bd048d693eb5cc308e9b9f3

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:ccfe5e5e16ee3b982590337bf2caf324148c02bcab777e3fa1fa3d40be8eb343

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=sha256:0d13aac426c1c0864edcaac543207e1e16ae9579e3e218379fcddce6ca856638

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:d7aec8171a9f5dd048bafcb2960f8f9de85bbdd08f070f0ad8d0f29f891c28aa

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:c30f4e9520cbf8bf3beaa5dd3080f8e4af65e418d58edca629cdd641b727d568

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:b372031619a8bc54c3c67b35eafeb68b5dadeefad4abb1f9ff562c5003d7c62d

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=sha256:88288de5cc6a6f40a6028d08f99d3f3ac0f7bc3f2be0f640f11a5eb44bf37eb7

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=sha256:5693b878b14cf75bca0c35c3c8159ca33ff6662cfc537b4c6e2bd50443c797f8

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:c3beb53f617b9cfd28dbaaffbbf0de456c258b158b1b23202251ea4310d8ce0e

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=sha256:2abb3e80ef6598b7b07b54fc28a5bdd0b9a4362aa5c32bcef62861e0b4ec5198

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=sha256:503bb754a6c65a53d09618a9bcd7927a66d4902e6e3e2a562512ee00c58dda90

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:d3c5fd314957f8410d496aa007be63aca077cf57aedebc6c41b396e090eccf53

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:eb6e2e0a53d032fc81cf52afdd9ab4b91426c12e427b7ca110e6e2a4c47506ab

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:d8fefa35f0c5f169b99d663af0a1efc5778fdee02806444c38e1fe4ddba4898a

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