Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-19T08:32:20.566798Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 2 inbound Pith citation observations for arXiv:2506.16796.
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 2026-05-19T08:32:20.566798Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T07:38:50.467496Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-16T20:58:31.910659Z
72 of 72 outbound references displayed
External citation measurements
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RealSR-R1: Reinforcement Learning for Real-World Image Super-Resolution with Vision-Language Chain-of-Thought Unresolved cited work
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RealSR-R1: Reinforcement Learning for Real-World Image Super-Resolution with Vision-Language Chain-of-Thought Unresolved cited work
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RealSR-R1: Reinforcement Learning for Real-World Image Super-Resolution with Vision-Language Chain-of-Thought Unresolved cited work
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RealSR-R1: Reinforcement Learning for Real-World Image Super-Resolution with Vision-Language Chain-of-Thought Unresolved cited work
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RealSR-R1: Reinforcement Learning for Real-World Image Super-Resolution with Vision-Language Chain-of-Thought Unresolved cited work
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RealSR-R1: Reinforcement Learning for Real-World Image Super-Resolution with Vision-Language Chain-of-Thought Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step
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RealSR-R1: Reinforcement Learning for Real-World Image Super-Resolution with Vision-Language Chain-of-Thought DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
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