Paper Citation Record · LEDGER

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning

As of 11 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2506.11172.

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

pith.paper-citation-record.v1
2506.11172 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:33:53.640555Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

25 of 25 outbound references displayed

verified exact1
verified fuzzy18
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f58df497-37b2-4c7f-a948-88252a80b5c5 · outbound

This paper cites López, and Vladlen Koltun.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning López, and Vladlen Koltun

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:59.521851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d675eb21-6ec0-41d0-b05e-ccfdf91e7a0c · outbound

This paper cites AWAC: Accelerating Online Reinforcement Learning with Offline Datasets.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T04:33:51.271578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73993638-8a13-47e6-a2a7-c5d5c92ed92e · outbound

This paper cites Tactics of adversarial attack on deep reinforcement learning agents.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Tactics of adversarial attack on deep reinforcement learning agents

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:58.292204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f2365652-123b-46f8-a50c-565305277aa9 · outbound

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

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Policy teaching via environment poisoning: Training-time adversarial attacks against re- inforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:57.983111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 910c8e31-de8b-4ad1-aaf1-c3020a4049a8 · outbound

This paper cites Understanding the limits of poisoning attacks in episodic reinforcement learning.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Understanding the limits of poisoning attacks in episodic reinforcement learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:57.700610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a5aa4151-e9f6-4447-818f-0558a868e3c4 · outbound

This paper cites SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:33:53.979737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c77d68a0-8a7e-4556-974d-c53e1db78987 · outbound

This paper cites Stealthy and effi- cient adversarial attacks against deep reinforcement learn- ing.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Stealthy and effi- cient adversarial attacks against deep reinforcement learn- ing

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:57.060431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8068bb60-2e8f-4706-951a-bc15927fc7c9 · outbound

This paper cites Vulnerability- aware poisoning mechanism for online RL with unknown dynamics.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Vulnerability- aware poisoning mechanism for online RL with unknown dynamics

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:56.757052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7c5958e1-33eb-4343-8b5e-e1319324bf10 · outbound

This paper cites Who is the strongest enemy? towards optimal and efficient evasion attacks in deep RL.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Who is the strongest enemy? towards optimal and efficient evasion attacks in deep RL

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:56.482952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 91dfd0b0-7ade-4dec-ad12-648b2c8709a9 · outbound

This paper cites Behavior Regularized Offline Reinforcement Learning.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Behavior Regularized Offline Reinforcement Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T04:33:52.816694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95c9a2f4-25c3-4f94-a704-8c471f1447b2 · outbound

This paper cites Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:55.558915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 92d01ebd-d99f-4d4e-b4d6-7d08a6ec9c91 · outbound

This paper cites Robust Reinforcement Learning on State Observations with Learned Optimal Adversary.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Robust Reinforcement Learning on State Observations with Learned Optimal Adversary

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:33:53.084260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c600682-afe6-4dc4-bf3d-0422803035e9 · outbound

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

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Adaptive reward-poisoning attacks against reinforcement learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:55.221667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ad6cdf0c-538e-4b90-83a6-fe1b13fc8ad3 · outbound

This paper cites We use the official open-source code of these algorithms and follow the settings by D4RL [Fu et al., 2020].

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning We use the official open-source code of these algorithms and follow the settings by D4RL [Fu et al., 2020]

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:54.581343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4bb23235-9f51-4126-82ff-fcf70a40938f · outbound

This paper cites an unresolved cited work.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:33:54.274142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7d3bb7f3-2eaa-4acd-a487-210fe25d24db · outbound

This paper cites Morel: Model-based offline rein- forcement learning.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Morel: Model-based offline rein- forcement learning

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:58.613023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8310298f-6573-40c7-8a0a-0f42180a46cc · outbound

This paper cites Hunt, and Mingyuan Zhou.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Hunt, and Mingyuan Zhou

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:55.880927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 53bada1c-cf38-49be-933b-c503b7026613 · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T04:33:50.727595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0c529f5-abf3-4f92-a26a-33bd37cbde61 · outbound

This paper cites Mujoco: A physics engine for model-based control.2012 IEEE/RSJ International Conference on Intelligent Robots and Sys- tems, pages 5026–5033,.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Mujoco: A physics engine for model-based control.2012 IEEE/RSJ International Conference on Intelligent Robots and Sys- tems, pages 5026–5033,

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:56.215240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 202fe4ea-8544-48ba-951e-73dc33df8417 · outbound

This paper cites Decision S4: efficient sequence-based RL via state spaces layers.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Decision S4: efficient sequence-based RL via state spaces layers

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:00.100542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 47c7acd4-3f06-4a80-8e12-570475adba9d · outbound

This paper cites A minimalist ap- proach to offline reinforcement learning.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning A minimalist ap- proach to offline reinforcement learning

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:59.282913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 34f1a10d-7849-468c-bf85-d789b519e125 · outbound

This paper cites Off- policy deep reinforcement learning without exploration.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Off- policy deep reinforcement learning without exploration

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:58.917369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e03b7de2-deea-4a87-9a0e-7be9f458d069 · outbound

This paper cites Reinforcement learning with sim- ple sequence priors.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Reinforcement learning with sim- ple sequence priors

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:57.350096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 349fd631-693d-42ab-b360-af2bde90d62c · outbound

This paper cites Julian, Chelsea Finn, and Sergey Levine.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Julian, Chelsea Finn, and Sergey Levine

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:59.808138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1697ad35-3954-4e2e-a61b-e678f76d06a0 · outbound

This paper cites an unresolved cited work.

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Unresolved cited work

Reference 3090

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:33:54.948969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.