Paper Citation Record · LEDGER

BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2105.00579.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2105.00579 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:05:35.083962Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T13:58:21.621045Z

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 91c4db19-c7b7-4a7c-b828-4184bf1993eb · inbound

Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies cites this paper.

Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning

Reference 184

Resolution: unresolved; no resolver link, observed 2026-08-07T13:05:35.083962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c748fc02-93b7-42d3-bc99-3714c2db734b · 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 BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T19:47:32.531262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4af959bc-14b5-41cc-93ac-cd6eeb747e52 · inbound

State Backdoor: Towards Stealthy Real-world Poisoning Attack on Vision-Language-Action Model in State Space cites this paper.

State Backdoor: Towards Stealthy Real-world Poisoning Attack on Vision-Language-Action Model in State Space BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T12:18:23.824751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9358b655-da2a-4972-b1a2-1450f8d23b30 · inbound

BehaviorGuard: Online Backdoor Defense for Deep Reinforcement Learning cites this paper.

BehaviorGuard: Online Backdoor Defense for Deep Reinforcement Learning BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:56:07.110465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 08d70c12-4a67-4180-b83b-b190e4cde2ca · inbound

Auditing Near-Optimal Policies Can Be Exponentially Hard: Conditional Query Lower Bounds via Occupancy Rashomon Capacity cites this paper.

Auditing Near-Optimal Policies Can Be Exponentially Hard: Conditional Query Lower Bounds via Occupancy Rashomon Capacity BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:16:00.608902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2d928352-459f-4fb4-b298-e185799caffd · inbound

PolicyGuard: Towards Test-time and Step-level Adversary (Backdoor) Defense for Reinforcement Learning Agent cites this paper.

PolicyGuard: Towards Test-time and Step-level Adversary (Backdoor) Defense for Reinforcement Learning Agent BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:58:21.622584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.