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Paper Citation Record · LEDGER

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

As of 14 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

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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

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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

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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

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=

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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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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=sha256:8bc06f212c73ba7431b10eb3eb3e1d60cf3f503775bca2e080e09ad7585236f0

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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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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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:a4448831146aecc7a25715d9800be2c7b4951cb49ba91d04da73419eb23d432c

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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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:bec707d38a83818b32ef0e97c06d1cbfb74259acfcd815d7e118092168685d6c

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:fced29106e747ddc1fe1b465ac363597ef812d38b519cfd6e19f2590e178212c

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=sha256:187c0c150cc65c380e19cc29b1e128a5c730fc318a84eb9a2ea474b6faef1924

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=sha256:8f3caa50eda07d4f28707d0e7824ada48136088672409320931f6ca5f1eb36a8

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:f45e4e306572ac7d1eb5ddd14f20934fc5f42f59fdc8b9168ee79b255a9aaf64

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:da959688129f776c41cdb0a6cab89128218bc799d5215d2cab4138bd9ffa75a0

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=sha256:1b38659519b9d393cf4e6640c45ae062d2eb18c065f2365214c87b683c3afc7b

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:fb6ab90ba31cc4c8ed0af6e596730148b4d960fff449a38735da7c5d1ae3f715

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:c7f460294004cfa84c977785cbf3cb8fa2628bfb80aee7574e840654fa756816

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=sha256:981c4c45287e33ab13b192c1d4028fcb1457e0d049ae60f4c13ee8adc37c691a

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=sha256:11955515c6da4f97ab7fdca5428ae4e08ea84dade5caae1a037c4ccf86e4f1a6

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=sha256:74cfa2488bdc8a87caae5d70cbe618529230d565d4eae5766fec4d47fe5de7c0

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=sha256:00a557aaaebae0b090c09489558a411313b9765d84862b53205d72408bb90602

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=sha256:0930fe6a39f1312ab4175f4d43b70eb6a71a99ec4f171c1feaf7ccdb685f1804

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=sha256:82d07b5b9ed041bdda4605cb13cc94853ee52b6152790fbafb53c78215747151

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=sha256:400e5e4f884f14949ab243507ec77daff38451de03c5c83a93155fa7816674d3

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:cff48c8a108657e6f9d33155bdfca9921ce769df9effb7d4a1084e63d6a37da6

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=sha256:2474a24b8bf5782c8c2d678b0583c076f05bb88db5bccaedcdeb6448bd85329a

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:c2b35da4ca73800303070793caf09806d11a751e435e12db7f036faf50ba8da6

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=sha256:309e6d13852ebd4df9fef8f8c7654cb7e00c9f63a181ad849eeb9dc02064cf5e

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=sha256:8fad297866b7dd95141fd26735a88e1393850caceeb75ff6ab2615a8165e9764

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=sha256:7ba0619c70b751582ca82d86573932d0e1a6ef885b291b034e4df80097a40c0c

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=sha256:2f057da3af760655412a4f8702efa893ed62f2707034c79ea726e7ef08cc45e5

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:a2a07fb3cb0eac0bb353ae94f13803cfa9c7f55f1d321db027539261633f86ce

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=sha256:1c320d3b50a7b1c88cb4f9c6237893174dd843e31c0560a55e03f9da76090ce3

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=sha256:53f995c4ed4eaf91601aaf409a28eed5fe042db8fb52d3427ebfc2e748019c00

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=sha256:958c4bbff38b4abdb2c500e368f899e5996b1b79f1ebf7fc88efba041e61f70c

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=sha256:4036b4e09408e1c047cbcd0639b9a51a5b95f840744759b2d2fa33f343e1ecc9

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:f87e7515243c6dc966be0d9b834459725b949500169d7e93785e98248c46522b

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:a4a73ea374b63c3309f255135b3b96233c73e3a45ddbd7f73f8ba49153e87b94

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:d5a7875889cab7d9b2a178b7d3694d4b238afd998ee6abbd9ca11faebc9a7653

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=sha256:738a24369d067a4dd95032d1d22ada689568b354e1389e9f879aca05f4563afe

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=

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source=arxiv_source observed=2026-08-12T00:39:40.528033Z digest=sha256:e03b40dd4ae99000cd92a7dae6b1fa3b4593c0ae19cdb273d8fd4f88d643acd3

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=sha256:084415f0d357e3e41c8472e90855db61b0974c8ae889bc912df0a4befd9b3cdf

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=sha256:124ab6c4b7eb47a4c3c7421a87a0fae7f7c9c9fcb9aead5a495e8a392406bb9c

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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source=arxiv_source observed=2026-08-12T00:39:40.541646Z digest=sha256:c253a6dfe8d4f0d9b0066c1e9f0431b5ba50234f1f4f2b3cd38ccbc4d5cb6709

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:f562d29aa3ca07180502fafe8d36e94c71d5c2d16e64a051205b3560de888613

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=sha256:0ebf1bb03901516fbd39de9f4ccf763efa2da7e8fe019648047899ca798925f7

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=sha256:1c5c67318acd3fb7e70644203c56c14182a3c1b099a565f7118c902e0d8edb87

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=sha256:7e9d0fab4624adc1908f4ad8566ac2e3c2b35762f43c29acf2bfd8ed4ece8f5d

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:bf50eec95683939df9c137145a3982d151bb43c341ba0ffdaf0c379754b69b19

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:e6c65cc7ac2aacbf5d5cb44357677491ef774530d92ac6ecad78a6a5948b96e7

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:a34be0981450afd04a604d8575782b4733167ae109eab3add7c5fcb9bddbf34e

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:d80e82c68619d08212210513683bc2b8f7de8a9852d3822729d29f02d0d33945

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:a10c3a6f89a18f3de90a6a94b81cb2aec1f76e0f8036f4804d417e5c0fe839f5

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=sha256:39a4f61225bb782b536105132d0424fcc9bfc43fec36878d383b87214373a8d4

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:f547a93c2dc3ba723b5c98392e611241d1e45e2d4bbf7845394bfea026c0d563

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:bd884aeebf20d2243f0f72931db475a6135d8b8e307ea1040ea61e9a12f2ac1e

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:a8b4c0e8b77b4c7098cb3dba6453037528460a493ea25886be86dd25621dbf54

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:e6180e7e1648b8f755dc682fae474a37a9139f009d2cc3f80f7efda713d0136a

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:fb85618f276f049bc76d97e10b9d666151617d32b0645c686d32c4120e8abdfc

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:c59ee69f8804d2135581accf4fcb98b5d4e4113f575e46cd21c34ded4d6adf5e

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:be30c60d71eecabba3fd54d171b90050c2be602e1972b8956f5a7795e19f4d74

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=sha256:8a798e2464c4db690061a4ea068e424d16dc80e451816715afecb185d38a1d97

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=sha256:5ec59a648b60a7994bb55f375c2193e72da183eebb5ef7f585738eabad0352ca

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