Paper Citation Record · LEDGER

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL

As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2507.08848.

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

pith.paper-citation-record.v1
2507.08848 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:18:38.348459Z

measured 21 of 21 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

21 of 21 outbound references displayed

verified exact0
verified fuzzy16
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 36f9be12-ed20-44e7-8e1e-4adc0cf278a1 · outbound

This paper cites an unresolved cited work.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:18:42.500731Z

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 190250b7-ddc7-4715-95b1-664520f0ef41 · outbound

This paper cites A deep reinforcement learning approach based energy management strategy for home energy system considering the time-of-use price and real-time control of energy storage system,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL A deep reinforcement learning approach based energy management strategy for home energy system considering the time-of-use price and real-time control of energy storage system,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:42.238307Z

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 d13f76d6-f26c-46c7-90ba-93f2d002fa78 · outbound

This paper cites Deep reinforcement learning for autonomous driving: A survey,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Deep reinforcement learning for autonomous driving: A survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:42.036960Z

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 8a765da5-ffbd-425b-8991-8121fa31ca4d · outbound

This paper cites Deep reinforcement learning: An overview,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Deep reinforcement learning: An overview,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:41.742138Z

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 2915ee2b-bd11-4402-a0cc-9d3407f0062f · outbound

This paper cites Challenges of real-world reinforcement learn- ing: definitions, benchmarks and analysis,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Challenges of real-world reinforcement learn- ing: definitions, benchmarks and analysis,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:41.442076Z

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 f66abcdb-635b-4982-8713-a1455ddfcfb8 · outbound

This paper cites Explainable Reinforcement Learning: A Survey.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Explainable Reinforcement Learning: A Survey

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:36.678381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8495aa36-a567-4d61-a72f-d239f7f969a9 · outbound

This paper cites Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS).

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:36.830624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb7eb515-7f6f-40cd-a661-602f9be8f847 · outbound

This paper cites Uther, Markov Decision Processes , pp.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Uther, Markov Decision Processes , pp

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:41.142272Z

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 3d62bb90-85c1-4e99-8bbf-339534dd7855 · outbound

This paper cites A review of safe reinforcement learning: Methods, theories, and applica- tions,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL A review of safe reinforcement learning: Methods, theories, and applica- tions,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:40.795249Z

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 a4c2c0df-b2e7-4e3f-bc41-da22f627841d · outbound

This paper cites Safe reinforcement learning for autonomous vehicles through parallel constrained policy optimization,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Safe reinforcement learning for autonomous vehicles through parallel constrained policy optimization,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:40.501697Z

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 5ba90573-ecf2-40bc-81b1-541198e3ba2f · outbound

This paper cites Reinforcement learning in healthcare: A survey,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Reinforcement learning in healthcare: A survey,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:40.193469Z

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 0033cd90-6859-4b91-801d-e1804650e343 · outbound

This paper cites Safe learning in robotics: From learning-based control to safe reinforcement learning,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Safe learning in robotics: From learning-based control to safe reinforcement learning,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:37.393022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f6a493c-c5af-4da7-ada5-26054e2b597d · outbound

This paper cites An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:37.534979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99743d1e-91ae-45dc-b373-66e517d67c89 · outbound

This paper cites Making the case for safety of machine learning in highly automated driving,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Making the case for safety of machine learning in highly automated driving,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:39.961864Z

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 1c973057-a639-485c-98f3-6f22354564ce · outbound

This paper cites Can you trust your agent? the effect of out-of-distribution detection on the safety of reinforcement learning systems,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Can you trust your agent? the effect of out-of-distribution detection on the safety of reinforcement learning systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:39.782644Z

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 0329f449-25d7-4ee9-b555-124c2aa3d8c5 · outbound

This paper cites Safety-driven design of machine learning for sepsis treatment,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Safety-driven design of machine learning for sepsis treatment,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:39.535667Z

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 106197c6-6da6-41a0-8d20-2fd1edc7bfe3 · outbound

This paper cites Assuring the machine learning lifecycle: Desiderata, methods, and challenges,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Assuring the machine learning lifecycle: Desiderata, methods, and challenges,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:39.342046Z

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 e61962a7-1a52-42d0-9a83-bc2f4e37e5c4 · outbound

This paper cites Defining and characterizing reward gaming,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Defining and characterizing reward gaming,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:39.176815Z

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 1c9d9960-a66b-4eb2-8f61-14fa78a83723 · outbound

This paper cites What is acceptably safe for reinforcement learning?,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL What is acceptably safe for reinforcement learning?,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:38.946213Z

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 db362e88-7b18-486b-ac0a-ffd67a570873 · outbound

This paper cites Design of the safety case of the reinforcement learning-enabled component of a quanser autonomous vehicle,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Design of the safety case of the reinforcement learning-enabled component of a quanser autonomous vehicle,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:38.784777Z

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 c7448eb4-419c-46e2-b7a4-ab71f13b65d5 · outbound

This paper cites Reliable safety decision-making for autonomous vehicles: a safety assurance reinforce- ment learning,.

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL Reliable safety decision-making for autonomous vehicles: a safety assurance reinforce- ment learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:38.566258Z

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.

Pith citing papers

No inbound Pith citation observations are available.