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Paper Citation Record · LEDGER

Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2106.07798.

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

pith.paper-citation-record.v1
2106.07798 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:47:32.478998Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T19:16:00.601482Z

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 da0798f3-51ad-4443-a5ed-083a64caa3db · 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 Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-06T19:47:32.478998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:47:32.478998Z digest=sha256:a1026e0aae86bdf9c40ad0c35b4a379b78b108842d53c7759c64af6538ac3b20

Observation c7c2c57c-0884-4678-a95a-3527c2bc3779 · 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 Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-01T19:16:00.603264Z

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.

source=pdf_text observed=2026-06-28T22:55:56.432874Z digest=sha256:0e34612439d2a03358d2f0d751f6b0488d54545bfd5dbefd434b2448ac89ab06