Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.16854.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T00:22:25.044749Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T06:25:58.577972Z
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 a515bb84-2da3-4549-a36e-0dfdcd90649f · inbound
Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G cites this paper.
Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:22:25.044749Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d562c035-0d91-4224-b13e-28c56f427c23 · inbound
Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey cites this paper.
Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning
Reference 77
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T06:25:58.581010Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.