Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T23:51:05.564651Z
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 12 inbound Pith citation observations for arXiv:2506.16211.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T23:51:05.564651Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T20:08:36.570832Z
A source-named dated measurement, never combined with another source.
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63 of 63 outbound references displayed
External citation measurements
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ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
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ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation
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ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning
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ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
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ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
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ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models Zhang, A
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ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo
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ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models Tyree, J
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ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models Locatello, D
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