Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Hierarchical Transformers are Efficient Meta-Reinforcement Learners
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2402.06402.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T18:52:14.250302Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T03:25:20.951468Z
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 ea4c9466-2d31-4be8-96e1-dbb92d1eec48 · inbound
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers cites this paper.
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Hierarchical Transformers are Efficient Meta-Reinforcement Learners
Reference 97
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T18:52:14.250302Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b4bb8301-5f51-4aa2-b5b1-50c5354d4b21 · inbound
Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning cites this paper.
Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning Hierarchical Transformers are Efficient Meta-Reinforcement Learners
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T03:25:20.955072Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 4e1b2104-a671-4810-966f-d0678211e325 · inbound
Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent cites this paper.
Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent Hierarchical Transformers are Efficient Meta-Reinforcement Learners
Reference 144
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T20:46:35.658156Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.