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Paper Citation Record · LEDGER

Reward Centering

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2405.09999.

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

pith.paper-citation-record.v1
2405.09999 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:20:14.470216Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-21T00:03:51.939547Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6a234d97-1b19-4d65-bd3a-bd2b23d3b1a8 · inbound

Harnessing the Power of Reinforcement Learning for Adaptive MCMC cites this paper.

Harnessing the Power of Reinforcement Learning for Adaptive MCMC Reward Centering

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-06T21:20:14.470216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:20:14.470216Z digest=sha256:b81d400b3378e9b42063665198d34bde5aa1305277251377c81700a6b4c9394e

Observation 4535117f-df57-4af2-9837-47ca0f6d0c85 · inbound

Learning from Expert Factors: Trajectory-level Reward Shaping for Formulaic Alpha Mining cites this paper.

Learning from Expert Factors: Trajectory-level Reward Shaping for Formulaic Alpha Mining Reward Centering

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T13:48:52.362113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:48:52.362113Z digest=sha256:9bcd06b2010b7b166f36d90f20a9d71d30547486afed11a08ec8566c079959fd

Observation 47b0d747-7891-485f-8593-5fc5accb7573 · inbound

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control cites this paper.

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control Reward Centering

Reference 58

Resolution
verified exact
arxiv_id, observed 2026-05-10T22:15:49.953334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T20:04:56.512544Z digest=sha256:ebd80e7ae0df17bfb88b4930c03d1e4a0cd9824968048e3e335dfb1178c6e834

Observation fb70e78c-d09b-43d9-8fc2-73fbd9397a60 · inbound

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control cites this paper.

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control Reward Centering

Reference 58

Resolution
verified exact
arxiv_id, observed 2026-05-19T17:12:41.393233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-19T17:08:31.770889Z digest=sha256:4a9f280baff02eb22918442aeb7768c1a40a478ee4bef618a92eb1b323407680

Observation 85911fbf-9f48-4fde-95b2-9712883279ef · inbound

Graph Transformers and Stabilized Reinforcement Learning for Large-Scale Dynamic Routing Modulation and Spectrum Allocation in Elastic Optical Networks cites this paper.

Graph Transformers and Stabilized Reinforcement Learning for Large-Scale Dynamic Routing Modulation and Spectrum Allocation in Elastic Optical Networks Reward Centering

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-09T06:10:43.189533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T18:47:04.091987Z digest=sha256:7d2f4d324b7eaeb70385e53997bef153b375a40b657ab884bf19013791d26cac

Observation adc8559f-f14b-4e9e-bd51-5aeb34727d4f · inbound

Graph Transformers and Stabilized Reinforcement Learning for Large-Scale Dynamic Routing Modulation and Spectrum Allocation in Elastic Optical Networks cites this paper.

Graph Transformers and Stabilized Reinforcement Learning for Large-Scale Dynamic Routing Modulation and Spectrum Allocation in Elastic Optical Networks Reward Centering

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-21T00:03:51.942284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T00:02:01.826213Z digest=sha256:99ba7f72929dc89b81b8e2652eba2f28b3920840719f252ca2ab937818a3995a