Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Models
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.08904.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T20:48:54.672783Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T17:45:26.247892Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation a8c85c25-9214-4496-8c39-2b06a8203922 · inbound
GBC: Generalized Behavior-Cloning Framework for Whole-Body Humanoid Imitation cites this paper.
GBC: Generalized Behavior-Cloning Framework for Whole-Body Humanoid Imitation AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Models
Reference 80
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:48:54.672783Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f5c72fd0-0d82-4953-a438-1e7eaa642c91 · inbound
Toward Seamless Physical Human-Humanoid Interaction: Insights from Control, Intent, and Modeling with a Vision for What Comes Next cites this paper.
Toward Seamless Physical Human-Humanoid Interaction: Insights from Control, Intent, and Modeling with a Vision for What Comes Next AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Models
Reference 117
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T17:45:26.249556Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.