Paper Citation Record · LEDGER

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments

As of 13 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2507.00030.

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

pith.paper-citation-record.v1
2507.00030 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:12:49.665075Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

11 of 11 outbound references displayed

verified exact0
verified fuzzy9
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4e1a3798-0d25-4f56-b343-8670aff436f4 · outbound

This paper cites Bellemare and others.

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments Bellemare and others

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:12:51.530869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3567d400-933f-469b-b9bb-2ca1b7f304a1 · outbound

This paper cites Frame skip is a powerful parameter for learning to play Atari.

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments Frame skip is a powerful parameter for learning to play Atari

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:12:51.398444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 335db7ef-2e9f-4867-8c0d-da5f348a8d89 · outbound

This paper cites Gilbert and Timothy D.

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments Gilbert and Timothy D

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:12:51.272890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a555fbe8-65b9-41fd-8310-f78551845d19 · outbound

This paper cites Lakshminarayanan and others.

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments Lakshminarayanan and others

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:12:51.099339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 491d555f-1113-4195-b209-78488a0a87e3 · outbound

This paper cites A contextual-bandit approach to personalized news article recommendation.

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments A contextual-bandit approach to personalized news article recommendation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:12:50.919715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6bf59c52-fba4-4fd7-a67d-3803fe9168ce · outbound

This paper cites Human-level control through deep reinforcement learning.

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments Human-level control through deep reinforcement learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:12:50.740473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b9ff5d85-7e5b-45f0-82f3-613ccedff340 · outbound

This paper cites Asynchronous methods for deep reinforcement learning.

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments Asynchronous methods for deep reinforcement learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:12:50.590134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0e8db6ed-34d1-402c-9815-b9aabcb59659 · outbound

This paper cites Mastering the game of Go with deep neural networks and tree search.

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments Mastering the game of Go with deep neural networks and tree search

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:12:50.419941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 26e464ee-730c-4a27-95b4-e2089cef3724 · outbound

This paper cites Sutton and others.

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments Sutton and others

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:12:50.250126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4aabe205-466b-4c2b-a5c5-00452fb8a38b · outbound

This paper cites Effect of scalar leptoquarks on the rare decays of B_s meson.

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments Effect of scalar leptoquarks on the rare decays of B_s meson

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T00:12:49.836013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9daf9023-c8d9-4a86-93e8-5808381c8ac9 · outbound

This paper cites A gradient estimate for nonlocal minimal graphs.

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments A gradient estimate for nonlocal minimal graphs

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T00:12:50.085655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.