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Source: paper_references, paper_reference_links, observed 2026-08-12T00:39:40.739274Z
Paper Citation Record · LEDGER
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.
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Source: paper_references, paper_reference_links, observed 2026-08-12T00:39:40.739274Z
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Pith citing papers itemized under the disclosed page cap.
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100 of 294 outbound references displayed
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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
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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Clancey and Glenn Rennels , abstract =
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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Structure and Interpretation of Computer Programs
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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Brachman and James G
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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
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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models 1991 , publisher=
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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Complexity results for nonmonotonic logics
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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Hypertree Decompositions and Tractable Queries
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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models On the compilability and expressive power of propositional planning formalisms
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Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models Kingma and Jimmy Ba , title =
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