Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2012.13091.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:47:32.491825Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T07:26:46.138805Z
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 91ad3e1d-1e6d-4bad-9d9e-75a10a69d546 · inbound
Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning cites this paper.
Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:47:32.491825Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 639c47ee-503e-4675-a411-4e041f8e0a5a · inbound
Uncertainty-Aware End-to-End Co-Design of Neural Network Processors: From Training and Mapping to Fabrication cites this paper.
Uncertainty-Aware End-to-End Co-Design of Neural Network Processors: From Training and Mapping to Fabrication Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T07:26:46.140293Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.