Paper Citation Record · LEDGER

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments

As of 23 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2412.00797.

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

pith.paper-citation-record.v1
2412.00797 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:06:17.785227Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy14
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2ff4956e-cf40-4681-81ca-f49ab971a10c · outbound

This paper cites Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T05:06:17.702243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c68d1b09-12d3-4888-aaae-d2393111f98e · outbound

This paper cites Generative adversarial user model for reinforcement learning based recommendation system.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Generative adversarial user model for reinforcement learning based recommendation system

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:18.057832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 704bf826-e375-4c48-b521-d22a1e19098b · outbound

This paper cites Badrl: Sparse targeted backdoor attack against reinforcement learning.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Badrl: Sparse targeted backdoor attack against reinforcement learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:18.045521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a871d55a-873a-4269-a5d4-a37e1ffb337c · outbound

This paper cites Sbeed: Convergent reinforcement learning with nonlinear function approximation.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Sbeed: Convergent reinforcement learning with nonlinear function approximation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:18.032843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 13d8d294-c30b-45fa-8541-a0a169c0fe0e · outbound

This paper cites Execute Order 66: Targeted Data Poisoning for Reinforcement Learning.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Execute Order 66: Targeted Data Poisoning for Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T05:06:17.719254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c54d4ff-aa9e-4904-816b-60c18f990911 · outbound

This paper cites Approximation Methods for Bilevel Programming.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Approximation Methods for Bilevel Programming

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T05:06:17.723470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29d0671b-1e8d-4027-b350-22a82b9471e7 · outbound

This paper cites Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T05:06:17.728031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d6f5cf8-2cfb-4436-9391-8fd2eed573ec · outbound

This paper cites A two-timescale stochastic algorithm framework for bilevel optimization: Complexity analysis and application to actor-critic.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments A two-timescale stochastic algorithm framework for bilevel optimization: Complexity analysis and application to actor-critic

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T05:06:17.732094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9c94093-48fe-47ca-a667-d55e4d4d7001 · outbound

This paper cites Trojdrl: evaluation of backdoor attacks on deep reinforcement learning.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Trojdrl: evaluation of backdoor attacks on deep reinforcement learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:18.012764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f5eb3cf2-a93c-4ec8-8f91-56c54c51dad3 · outbound

This paper cites Provably efficient black-box action poisoning attacks against reinforcement learning.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Provably efficient black-box action poisoning attacks against reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:18.000623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a2fabc64-2446-4206-8a12-9b7eaa5b83d4 · outbound

This paper cites Efficient adversarial attacks on online multi-agent reinforcement learning.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Efficient adversarial attacks on online multi-agent reinforcement learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:17.988384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e6c6f464-8ad7-41a7-ae5a-cef4bfc6b3eb · outbound

This paper cites Policy poisoning in batch reinforcement learning and control.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Policy poisoning in batch reinforcement learning and control

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:17.976154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f1c55176-5fd0-4294-891c-62c35bb6c369 · outbound

This paper cites Convergence rate of a penalty method for strongly convex problems with linear constraints.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Convergence rate of a penalty method for strongly convex problems with linear constraints

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:17.964954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b7ee0310-a4c2-4cf3-8400-043d11d4804e · outbound

This paper cites Talking to bots: Symbiotic agency and the case of tay.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Talking to bots: Symbiotic agency and the case of tay

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:17.953542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fdf0f1bc-cf60-400f-95e1-1f900bd8543b · outbound

This paper cites Scalable end-to-end autonomous vehicle testing via rare-event simulation.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Scalable end-to-end autonomous vehicle testing via rare-event simulation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:17.941219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 56475e9b-693a-456c-9ad2-867ea7de038f · outbound

This paper cites Policy teaching via environment poisoning: Training-time adversarial attacks against reinforcement learning.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Policy teaching via environment poisoning: Training-time adversarial attacks against reinforcement learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:17.929152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4cc6e3a0-a54c-4803-91a9-6ba3571ffe9e · outbound

This paper cites Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T05:06:17.765785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97552221-9bfa-4bb2-aece-d0d53118d6b5 · outbound

This paper cites Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T05:06:17.769893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be65d024-5bbe-40a7-8513-44b8efcb0216 · outbound

This paper cites Reward poisoning attacks on offline multi-agent reinforcement learning.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Reward poisoning attacks on offline multi-agent reinforcement learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:17.917456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d8c94cc8-9b3d-49eb-b627-4ca583954b65 · outbound

This paper cites Spiking pitch black: Poisoning an unknown environment to attack unknown reinforcement learners.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Spiking pitch black: Poisoning an unknown environment to attack unknown reinforcement learners

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:17.905837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cb5ed119-5c80-4f61-a445-b92930e51152 · outbound

This paper cites Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T05:06:17.781396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54323036-961a-496b-8822-2da88b6a6027 · outbound

This paper cites Adaptive reward-poisoning attacks against reinforcement learning.

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments Adaptive reward-poisoning attacks against reinforcement learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:06:17.893439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.