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Paper Citation Record · LEDGER

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

As of 15 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

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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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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

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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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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

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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

This paper cites International conference on learning representations , year=.

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

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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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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=sha256:71584f3a4b2cc641334c2330c035ce023c43ca41cab9d2261d7772a581d1ff4c

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=sha256:1b042b4c9cf948bc802aab72b3cb6aa35149d74cca39be01cb94dc8f372f1871

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:fccb6140911a6552996cb31c9a198b86f654cc0ff7c909bd66d34eb39b578561

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=sha256:07df96a58706ac7b1cf52fe6ef0c357f16a72e5d911f27eceab3ec3d27b3b8a5

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=sha256:6902fd373a8513161d991b2788d9a1d6d57ac01c655c27e7b2c7223086fd413a

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:ebc4f53bc74f3e75163e0af0c77e9288657e99014bd81e7b2aa6975a9ac43875

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=sha256:7c045464dcbc146b48af2ef1d12ae77c24599ae135ad256f8c696c06da26b512

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:a12afa0fb30098e20791f80f22ba133f18505beaecccee94195a7d947dd4c28a

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:b06aefc76250dc176cadd1ac295ceb8635d81a46120327fded29f600557521f1

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:c9064cdbde303b1a8a24bfa374c05491d52ecb05775aa97c2eeffeaeb67f0466

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=sha256:988eda7113f03c486726de368014e0f47960eb83d255923b94ab41b738573312

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=sha256:8519e8bf64fcb215847aaf675a805fb50caa641b1b062be204203cf6a0e8d813

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:cfaa453a707b3d0b372e1132861de62fa0b6933d16182fbfc28b4ddc95b32485

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=sha256:12e128ba0c76aba342216a882d3d1a59566e4d2b601b538749f791ff726d5112

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=sha256:903ae5643a6c9ceac937ba91d359a6a7913a8ec864b48861e332f3a4fccded8c

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=sha256:59ec34c27f50ec174af4bd383ea3f45aa8a61294877a6781dceed1b289af6b6c

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=sha256:73c5c47a0a5eeb2bd80d13c6af4edb832a77f2a1ed53f54376780da5eb2e6b86

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:a34c931c3fd34e19e801bb3b4c848e757ccd1ea9807969520f0311c6bf425ab2

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:b237544181eee98cec1d297f374252822250ebf76ac1b9647ceb01e6497c4575

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:d38ee58e02a7aba15f83c7a0c0484ce7f0389b84b7f7a97519f48c2ffd150b50

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:a5296f79ebb2c91c07e86dbbb5d42932f0c07bee3e4f06650ddc1039040e007f

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=sha256:2b3e7fa36ea347d3aa56733db531ea376004671f4d866b7ec5f40278918409c1

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:d6b4b3ce419b5663723e113571d09cf8f5069c323244e7d541fa42b5388c99c0

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:ac32222007745aa3986635cc62d233f48136da92c8c5b81d1ae74e74a8fe9e2f

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:be1dd3a2200fbddfe4064255d8670d06c97f5899fbffc3329537216a70bfac2a

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:e809aa91802b942d7a588410a2aa0d498fb34bb3a4404c83095f9b5ded7b3679

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=sha256:33e6cc6c1784d7d3e435d7d119fd946935cab0435334969c54a25aa65b6dac05

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=sha256:57d5fee71b89b6807c7d14b062bc8490c183738db17f22c222e9736a943f0ade

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=sha256:6d3e5ff79e9d5e9e78b898383a5a1b12ba9c72463148ec5065f654449f65b425

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=sha256:02e3fc69c986d7c49165a05452c48e733f4fa1446c83c4e30373659451c3843d

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:e3d9984d02e05a5927dc39e2ab5572a4b3f88c39a6b4f8893b08298afb6ffea0

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:eecf135bd5761d19c5a78cea976ce6dd52b76ee5cd9f12e3c494ec7d6c516c9b

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:c52015ffc8ed330d036751f94ba3fae53925a918c53a8d53767d4e14f48115cc

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=sha256:233479b8632034523d9af8b32d08cdae4b4c7cbd12572f39b8b24b3ada2baf3d

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=sha256:82e43d1fd423e5201350bf389fdb65519f7a9d2fcc3f7279264f79fc5b0043c0

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:f4d8eb5e0a9e3b7b914d7bcb4cf5d3f45f84309ce7ebd6f2747ade169032a60f

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=sha256:94f596809486743ffbf10435482c6f89263d0d2ef3eebd87d3bb2ab98bde5e1c

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=sha256:0c319d47d09bea80ef831e5aecfa2a76a68439c2c842de1c08ca6fcbb3820d2e

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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source=arxiv_source observed=2026-08-12T00:39:40.532678Z digest=sha256:c16c6b5c50ddd167bf2cbffc3c62a60bb9e90b75585cf0643536151ce5f200f2

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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source=arxiv_source observed=2026-08-12T00:39:40.550284Z digest=sha256:5a8bc1990ece899f45fee438a73ee66d368d0848a5875ca7a56dd548407801b4

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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source=arxiv_source observed=2026-08-12T00:39:40.668429Z digest=sha256:0b2ae097ebe5a7c8ad98b31d71173832844be31796ca8d85e46d6b2f0f5e272b

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=sha256:8d2970cce19a3843fa91ea9016a8d15d8286269f460103c082b34d9dbef6d24c

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:ce7cd376a6213b2687e4a89b2b450fe81cd2310892fd0e83c812066c1b2efc73

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=sha256:1fcbd9dc1112d4ef0d1a96c330b6a6b495ba532cf13686b1a3959dff367fbd05

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=sha256:5e639514acab5330f8d6c7a7c4bfd128d793adcc55409fde888bb5449e01b049

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=sha256:0b65660e19ad18e5be48b612a98f92e67b3eae2daa628a8ca8e9516922c47303

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:e2bc81d5042755cc7c521c72ec9b9b1673841d2517c408bd1898118489eb39ab

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=sha256:821ef61cb70c0d180b6deed6bb0e4a0ead66904f21f5b078dfc37897b445e887

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=sha256:9eee3a53288377a253658a7922cd7ec8d18f9181912fe98bb50e830e4f03e55d

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:db6455d6c6ce62e35838f78419c73b5972d09a5bbe5cbc1a787dbb08a90db05b

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:f870be452ccb95b9737739d90caad61fc85e0d828954ff4ca4c0a1bfea6e712e

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=sha256:2695a2690d39d6ef1b9abb64f4ef2e265da2b0f1e2e5a819c4cbc0838d6bc34a

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=sha256:01bfa370d0a76a8efa6a89764f3784b738aea59848ad0095178781431ac65c77

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=sha256:35185d0e3088f0dc1f90521ee3b0964f897d708302c7a2342f7778d7788e8521

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=sha256:92258214bd8fbe9a07c150f43e975af64c643fe70c07a4d484b0d4bbd59676a4

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=sha256:25c4f88cc2939a5351992071044da0c1d4081459c655df8128cf06f743e2df8d

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