Paper Citation Record · LEDGER

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2506.06891.

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

pith.paper-citation-record.v1
2506.06891 v3

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:52:47.229805Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

36 of 36 outbound references displayed

verified exact5
verified fuzzy10
unresolved18
parse uncertain0
malformed identifier2
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 27025b2e-4e77-40d8-91c3-85fba343b45f · outbound

This paper cites an unresolved cited work.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Unresolved cited work

Reference 3

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T05:52:47.735305Z

Source-reported events for the cited work

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Observation df4ff8b2-7fdb-4e63-aeb8-5e4697426ef1 · outbound

This paper cites Filtering Learning Histories Enhances In-Context Reinforcement Learning.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Filtering Learning Histories Enhances In-Context Reinforcement Learning

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:52:47.718666Z

Source-reported events for the cited work

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Observation 99f4c225-b51a-4a8e-ad62-0a2e1285aa00 · outbound

This paper cites We chose the α-trimmed variant, which performs best empirically.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks We chose the α-trimmed variant, which performs best empirically

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:47.750820Z

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Observation 1f6ff6e3-61c7-44a2-8c64-88ae69ffeb6d · outbound

This paper cites Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

Reference 9

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Source-reported events for the cited work

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Observation 12a8d48f-d0b8-4feb-ab1a-2cba963e91e1 · outbound

This paper cites Data Poisoning for In-context Learning.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Data Poisoning for In-context Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.165231Z

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Observation 5d2781a7-4306-49a4-af79-d29143ccc42e · outbound

This paper cites Decision Transformer under Random Frame Dropping.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Decision Transformer under Random Frame Dropping

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:52:47.668693Z

Source-reported events for the cited work

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Observation ca54a501-c1f5-4e85-8943-89d798fbda97 · outbound

This paper cites Adversarial Attacks on Neural Network Policies.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Adversarial Attacks on Neural Network Policies

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.170551Z

Source-reported events for the cited work

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Observation 65ddb73e-84f5-45ce-93fe-72119da406e3 · outbound

This paper cites Thodoris Lykouris, Vahab Mirrokni, and Renato Paes Leme.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Thodoris Lykouris, Vahab Mirrokni, and Renato Paes Leme

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:47.791720Z

Source-reported events for the cited work

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Observation 1746c1c3-08e4-4fab-af78-76e71a11315e · outbound

This paper cites A Survey of In-Context Reinforcement Learning.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks A Survey of In-Context Reinforcement Learning

Reference 20

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Source-reported events for the cited work

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Observation 3997a93b-b66b-40db-8f6b-7a90da0c71ba · outbound

This paper cites Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:52:47.456709Z

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.

Observation 1a6ce69d-45fd-4d9f-9e92-77ff00cc3797 · outbound

This paper cites Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.193639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1883a44-6921-4c55-96f1-c3ca9f319d51 · outbound

This paper cites Practical black-box attacks against machine learning.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Practical black-box attacks against machine learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:47.775957Z

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.

Observation 51b86397-76fc-4f7f-ac2f-48a0ea11e66f · outbound

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

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.198902Z

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Observation 7e0fc685-3e21-458d-a5f1-e300e5ef0ef7 · outbound

This paper cites URLhttps://ojs.aaai.org/index.php/AAAI/article/view/6047.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks URLhttps://ojs.aaai.org/index.php/AAAI/article/view/6047

Reference 25

Resolution: verified exact; doi, observed 2026-08-07T05:52:47.268078Z

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.

Observation f136a351-c851-4d85-97ec-01aa741e0cc0 · outbound

This paper cites Christopher JCH Watkins and Peter Dayan.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Christopher JCH Watkins and Peter Dayan

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:47.766759Z

Source-reported events for the cited work

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Observation e49cd421-c9de-4350-85c8-d7651e67ca60 · outbound

This paper cites URL https://ojs.aaai.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks URL https://ojs.aaai

Reference 29

Resolution: verified exact; doi, observed 2026-08-07T05:52:47.258152Z

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.

Observation 28009646-614a-4c3d-b7a0-c37f2050d375 · outbound

This paper cites Robust Thompson Sampling Algorithms Against Reward Poisoning Attacks.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Robust Thompson Sampling Algorithms Against Reward Poisoning Attacks

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:52:47.296109Z

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.

Observation fe2de193-e9e5-4310-9462-b571e2eb62ef · outbound

This paper cites Towards Robust Offline Reinforcement Learning under Diverse Data Corruption.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Towards Robust Offline Reinforcement Learning under Diverse Data Corruption

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.216750Z

Source-reported events for the cited work

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Observation e5d1c826-323a-4a57-b213-bcdb645d0471 · outbound

This paper cites Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.219188Z

Source-reported events for the cited work

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Observation 892269cb-bfc4-41fc-8ca9-6c840a8957cf · outbound

This paper cites 17 A.2 crUCB.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks 17 A.2 crUCB

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:47.758795Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2d271eeb-7d20-4bda-8555-70f2b8f207c6 · outbound

This paper cites A.3 CRLINUCB We source the CRLinUCB algorithm from Ding et al.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks A.3 CRLINUCB We source the CRLinUCB algorithm from Ding et al

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:47.743153Z

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.

Observation 2687ddba-a36d-4d36-ac32-b92a22a65b81 · outbound

This paper cites URL http://www.jstor.org/stable/2332286.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks URL http://www.jstor.org/stable/2332286

Reference 1933

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.206980Z

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Observation fa42f0e6-444b-44d2-80b2-e2ce2d03bcbe · outbound

This paper cites URL https://doi.org/10.1214/ aoms/1177703732.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks URL https://doi.org/10.1214/ aoms/1177703732

Reference 1964

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.173278Z

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Observation a0692b87-26f3-4328-a687-d52ad9c61d5a · outbound

This paper cites In-context Reinforcement Learning with Algorithm Distillation.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks In-context Reinforcement Learning with Algorithm Distillation

Reference 2001

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.175619Z

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Observation 60c842f6-b19c-4219-8770-a2d2879e6b58 · outbound

This paper cites A Tutorial on Meta-Reinforcement Learning.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks A Tutorial on Meta-Reinforcement Learning

Reference 2002

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Observation 0df5aec3-4b63-4980-bee9-bf6e870e2ec2 · outbound

This paper cites ISBN 9781605587998.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks ISBN 9781605587998

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.178059Z

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Observation 54ab52f4-e511-484c-b98f-671b9e56d912 · outbound

This paper cites BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.161755Z

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Observation b52de5ab-0527-471e-9505-869efaeb9556 · outbound

This paper cites Improved corruption robust algorithms for episodic reinforcement learning.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Improved corruption robust algorithms for episodic reinforcement learning

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:47.807523Z

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.

Observation 786c1c23-1392-4f09-99dd-e3e8f2b540e6 · outbound

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Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks ISBN 9781450355599

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.183209Z

Source-reported events for the cited work

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Observation 41d83310-d62f-46ec-8bfd-c2e59c2c330e · outbound

This paper cites Shike Mei and Xiaojin Zhu.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Shike Mei and Xiaojin Zhu

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:47.783983Z

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.

Observation 19a3987a-b618-4194-8dd8-91f026d85e9c · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.142043Z

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Observation 66563669-82bd-45bf-8186-546ab5f6f483 · outbound

This paper cites Intriguing properties of neural networks.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Intriguing properties of neural networks

Reference 2021

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Observation f273fc84-7c3c-4e86-8f73-f360b450647c · outbound

This paper cites Juncheng Dong, Moyang Guo, Ethan X Fang, Zhuoran Yang, and Vahid Tarokh.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Juncheng Dong, Moyang Guo, Ethan X Fang, Zhuoran Yang, and Vahid Tarokh

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:47.799877Z

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.

Observation b66940f3-31ae-45c0-951c-119617dbbb3e · outbound

This paper cites Evasion attacks against machine learning at test time.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Evasion attacks against machine learning at test time

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:52:47.820843Z

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.

Observation ec03edf5-12fe-4e40-bbcc-cbc5698483f0 · outbound

This paper cites RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.156119Z

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Observation 30bce87b-27c9-463b-937a-9e95f4799bbd · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:47.147789Z

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Pith citing papers

No inbound Pith citation observations are available.