Paper Citation Record · LEDGER

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning

As of 22 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2505.15011.

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

pith.paper-citation-record.v1
2505.15011 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:29:46.948522Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

34 of 34 outbound references displayed

verified exact7
verified fuzzy5
unresolved21
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f2332345-46e5-4552-bc79-0810f28a4cfc · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:50.068590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 92fa9ca6-4ed9-4ec6-96f3-23a674c10e81 · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 2

Resolution: verified exact; doi, observed 2026-08-07T15:29:47.879679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e9adfc80-a04d-4a0a-b6eb-05e92ec58a40 · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:49.965780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b63afe19-0577-4c95-b520-50dbdd65545d · outbound

This paper cites 2011.Machine Ethics.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning 2011.Machine Ethics

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:43.877217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27251b20-f90f-4d83-99ab-cf5909d59cde · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 5

Resolution: verified exact; doi, observed 2026-08-07T15:29:47.757610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5d31ef8c-32c7-4f81-bebc-032c5f01fb53 · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:49.885469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d1ed2666-4d6f-4b88-a99b-c05226295133 · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:49.819983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7c782612-e9c2-4935-8399-85d73ef4f1c9 · outbound

This paper cites Noisy Networks for Exploration.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Noisy Networks for Exploration

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:44.411449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 548e27e1-1903-42a6-8b79-686afb662f1d · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:44.541245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b5c38e4-c5c3-4d5f-8325-3b2da96af338 · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 11

Resolution: verified exact; doi, observed 2026-08-07T15:29:47.598569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6bf8f9d4-7852-479b-aa0d-da8c51ca34b9 · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:49.746484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation eb319be1-0dd1-4b26-a26b-d5a732be27aa · outbound

This paper cites Hauptabt.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Hauptabt

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:49.670291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7de1c634-0e51-4e0c-926b-1730fe63f9fd · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:49.600286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f8d70b68-4efb-4b33-90cb-1371c0060446 · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:49.502077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 550a2488-0789-4df3-87ee-9969e6210caa · outbound

This paper cites Rusu, Joel Veness, Marc G.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Rusu, Joel Veness, Marc G

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:45.357991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3dbad0a-4ed1-45e7-97ba-f38945127bb1 · outbound

This paper cites Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:29:48.390968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation af378706-719e-4f27-b0e4-215f3a9c79b6 · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:45.536818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31e0eabc-f2b7-47cc-a689-0f23b5163ca9 · outbound

This paper cites Neufeld, Ezio Bartocci, and Agata Ciabattoni.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Neufeld, Ezio Bartocci, and Agata Ciabattoni

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:49.401537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 28df5bc8-b13f-4eb6-a5f9-1b6c73d43956 · outbound

This paper cites Neufeld, Ezio Bartocci, Agata Ciabattoni, and Guido Governatori.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Neufeld, Ezio Bartocci, Agata Ciabattoni, and Guido Governatori

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:49.215661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3f6dd90d-8610-495a-bfe2-0a004950ee3f · outbound

This paper cites Varshney, Murray Campbell, Moninder Singh, and Francesca Rossi.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Varshney, Murray Campbell, Moninder Singh, and Francesca Rossi

Reference 21

Resolution: verified exact; doi, observed 2026-08-07T15:29:47.389279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ea220ad8-f90a-4872-a2df-eff8584e2783 · outbound

This paper cites Osoba, Benjamin Boudreaux, and Douglas Yeung.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Osoba, Benjamin Boudreaux, and Douglas Yeung

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:46.133292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef64e642-b235-4f0e-ba81-6653850dc5e2 · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:46.189575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4854d889-93a8-40a6-bedc-e52c39514889 · outbound

This paper cites Oliehoek, and Luciano C.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Oliehoek, and Luciano C

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:49.069779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8e0d87da-b920-4c2c-be6b-fe776b1211e7 · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:48.798664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f2e25747-3b2a-4679-bd64-903669f2310d · outbound

This paper cites Shaw, Andreas Stöckel, Ryan W.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Shaw, Andreas Stöckel, Ryan W

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:46.489529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dee9b111-49f4-4194-8121-6ad689b612e3 · outbound

This paper cites InProceedings of the 21st International Conf.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning InProceedings of the 21st International Conf

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:48.925273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 164746cd-c772-4674-847e-d9d152fddccd · outbound

This paper cites Deep Reinforcement Learning with Double Q-learning.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Deep Reinforcement Learning with Double Q-learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:46.611548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5cc2ca7-15bb-4bdf-ae16-5cb09a9a0ce0 · outbound

This paper cites Czarnecki, Michaël Mathieu, An- drew Dudzik, Junyoung Chung, David H.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Czarnecki, Michaël Mathieu, An- drew Dudzik, Junyoung Chung, David H

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:46.745452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4cb5ffb-dfe9-4aa1-a9a3-1a7754de3f93 · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:48.726223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1d695a29-cbb7-42db-84d1-e8ec1032ec9c · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 31

Resolution: verified exact; raw_fallback, observed 2026-08-07T15:29:48.085845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 948d17fb-3f1e-4d04-b5fe-c84cdc96c77d · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:29:48.634613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fc70aa8e-b68e-466b-8639-77ca9f51adef · outbound

This paper cites on Artificial Intelligence33, 01 (Jul.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning on Artificial Intelligence33, 01 (Jul

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:44.163712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 505f516f-aac2-40d8-99ee-83f6560d8370 · outbound

This paper cites https://doi.org/10.1007/s10676- 022-09665-8.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning https://doi.org/10.1007/s10676- 022-09665-8

Reference 2022

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fb8835b-2bb1-4ef5-8d8d-ea135d9da299 · outbound

This paper cites an unresolved cited work.

HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning Unresolved cited work

Reference 9789

Resolution: verified exact; doi, observed 2026-08-07T15:29:47.165029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.