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Paper Citation Record · LEDGER

Meta-Black-Box-Optimization through Offline Q-function Learning

As of 17 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2505.02010.

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

pith.paper-citation-record.v1
2505.02010 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:12:58.275096Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T17:41:40.567807Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T17:42:25.757588Z

Reference resolution

26 of 26 outbound references displayed

  • verified exact1
  • verified fuzzy9
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation b1a690d7-55b8-4e6c-8b77-f072143e1217 · outbound

This paper cites an unresolved cited work.

Meta-Black-Box-Optimization through Offline Q-function Learning Unresolved cited work

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation acd76e7e-a27c-4893-be14-38ac41b1dc15 · outbound

This paper cites States Notes Problem Property st 1 mean xi,xj∈Xt ||xi−xj||2 Average distance between any pair of individuals in current population.

Meta-Black-Box-Optimization through Offline Q-function Learning States Notes Problem Property st 1 mean xi,xj∈Xt ||xi−xj||2 Average distance between any pair of individuals in current population

Reference 4

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 4e3082a2-2c34-4876-8c17-9dbf601d6a98 · outbound

This paper cites an unresolved cited work.

Meta-Black-Box-Optimization through Offline Q-function Learning Unresolved cited work

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation f2a0fba5-afd2-4e28-9a50-820283c05fad · outbound

This paper cites Large Language Model Agent for Hyper-Parameter Optimization.

Meta-Black-Box-Optimization through Offline Q-function Learning Large Language Model Agent for Hyper-Parameter Optimization

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:12:58.175734Z digest=sha256:fb191c916f8ec9e6e7fdc59324e8d0f8894e23fe94bc752b39a950dbfb8d3a2f

Observation 684515a3-6bc9-4f1a-9c71-a9645e385074 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Meta-Black-Box-Optimization through Offline Q-function Learning Playing Atari with Deep Reinforcement Learning

Reference 9

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:12:58.187135Z digest=sha256:b27dd34af4375c80be6f658377e4c2650453d0e033545a5a30bd46a28dee3b2c

Observation d38b18a9-f383-4927-ad27-187b67f35f00 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Meta-Black-Box-Optimization through Offline Q-function Learning Proximal Policy Optimization Algorithms

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:12:58.203848Z digest=sha256:ed3a0a11147f2b62fb5a3dfbe56892c5bbb9687dc1ef2a1695d56abbe397737a

Observation 8a1896db-6b57-488b-b58d-41c688bf5472 · outbound

This paper cites Reinforced In-Context Black-Box Optimization.

Meta-Black-Box-Optimization through Offline Q-function Learning Reinforced In-Context Black-Box Optimization

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:12:58.208783Z digest=sha256:c3fb01f472202061a7d6687aa3acb2be77172fef570280557a8b58ac93d30564

Observation 117e3293-922e-4606-a655-7c9e56584705 · outbound

This paper cites and Fukunaga, A.

Meta-Black-Box-Optimization through Offline Q-function Learning and Fukunaga, A

Reference 15

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raw_fallback, observed 2026-08-16T04:12:58.959246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation fcd67b36-51a1-4e69-bf10-52a33672e243 · outbound

This paper cites DECN: Evolution Inspired Deep Convolution Network for Black-box Optimization.

Meta-Black-Box-Optimization through Offline Q-function Learning DECN: Evolution Inspired Deep Convolution Network for Black-box Optimization

Reference 17

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Observation 221773b3-ba32-451f-88a3-aff77b2bb079 · outbound

This paper cites Automated Metaheuristic Algorithm Design with Autoregressive Learning.

Meta-Black-Box-Optimization through Offline Q-function Learning Automated Metaheuristic Algorithm Design with Autoregressive Learning

Reference 18

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local_arxiv, observed 2026-08-16T04:12:58.447854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 8aca96fd-f132-4d35-b04f-4b73a7024385 · outbound

This paper cites an unresolved cited work.

Meta-Black-Box-Optimization through Offline Q-function Learning Unresolved cited work

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T04:12:58.246095Z digest=sha256:f8133a9d08facf06bcf206ad686bff013211229ab5875934505413af8fdc6c31

Observation cf501bc2-6850-4c4e-9542-233a99a19ebb · outbound

This paper cites (17) whereηc∈{ 1, 2, 3} is controllable parameter andu∈ [0, 1] is random number.

Meta-Black-Box-Optimization through Offline Q-function Learning (17) whereηc∈{ 1, 2, 3} is controllable parameter andu∈ [0, 1] is random number

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T04:12:58.261132Z digest=sha256:04a4827e04a4f2f68f07e9df9386820f7d510a0af39c476cddba1cc98906c558

Observation b3439b35-3e34-43a9-af4a-961af34c8b87 · outbound

This paper cites The Exponential crossover formulated as Eq.

Meta-Black-Box-Optimization through Offline Q-function Learning The Exponential crossover formulated as Eq

Reference 24

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raw_fallback, observed 2026-08-16T04:12:58.847260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T04:12:58.265849Z digest=sha256:7ee7888982c81a7918d942418babaf8264ab7afe85a2bd6242176f512d9c5352

Observation 3fbdb806-c8c2-42b7-bb1b-17834affdd7d · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Meta-Black-Box-Optimization through Offline Q-function Learning Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 1991

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no resolver link, observed 2026-08-16T04:12:58.154768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:12:58.154768Z digest=sha256:8d0792cb11348ef93f0abf79979f1d2a9bed99bba08a702a42e871baa7222fa4

Observation 4660b9ef-482d-4e78-a079-63283b950fd6 · outbound

This paper cites clip”, “rand.

Meta-Black-Box-Optimization through Offline Q-function Learning clip”, “rand

Reference 1992

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:12:58.897117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T04:12:58.250847Z digest=sha256:a902870a9ffc19d74af67f84846917139faca70451ffc67408f99c86c6efa34d

Observation bd249aec-9649-46f7-9340-95515454d86c · outbound

This paper cites C., Pearson, D.

Meta-Black-Box-Optimization through Offline Q-function Learning C., Pearson, D

Reference 1995

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raw_fallback, observed 2026-08-16T04:12:58.989060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T04:12:58.150426Z digest=sha256:15320306bc42f1f0451b07c9ae661c321ff33a4f707cb757b1e278c692ed77fd

Observation 0c3855ec-5c74-4dc8-92a9-e2a47c905382 · outbound

This paper cites Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning.

Meta-Black-Box-Optimization through Offline Q-function Learning Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

Reference 1997

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source=pdf_text observed=2026-08-16T04:12:58.213620Z digest=sha256:84676bb209ff30c8fe5d25c90580af4e74b12ba618dedd58a70cb3c48bfb482f

Observation a1f5ef90-a9b9-4150-b4a9-0ba5382610a6 · outbound

This paper cites The Polynomial mutation is as follow: x′′ i = ( x′ i + ((2u) 1 1+ηm− 1)(x′ i−lb), ifu≤ 0.5; x′ i + (1− (2− 2u) 1 1+ηm )(ub−x′ i), ifu> 0.5.

Meta-Black-Box-Optimization through Offline Q-function Learning The Polynomial mutation is as follow: x′′ i = ( x′ i + ((2u) 1 1+ηm− 1)(x′ i−lb), ifu≤ 0.5; x′ i + (1− (2− 2u) 1 1+ηm )(ub−x′ i), ifu> 0.5

Reference 1999

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T04:12:58.255932Z digest=sha256:804d91132920f572e24b88dd7bb9fbcfdc63b486513b3afb3ab6eaab957e3ab6

Observation bed09463-35f8-4fcd-8647-5f564af4c412 · outbound

This paper cites W., Hadi, A.

Meta-Black-Box-Optimization through Offline Q-function Learning W., Hadi, A

Reference 2013

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raw_fallback, observed 2026-08-16T04:12:58.974349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T04:12:58.192562Z digest=sha256:d617bb6a99857f80fed12c83fb27beaa46f1e41f21508c7b8d4b13112c6764a2

Observation 38307373-0d0f-437f-b1d3-b5d394fd10d7 · outbound

This paper cites Mujoco: A physics engine for model-based control.

Meta-Black-Box-Optimization through Offline Q-function Learning Mujoco: A physics engine for model-based control

Reference 2014

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:12:58.944007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T04:12:58.223188Z digest=sha256:3a7814a1330835504cd4e995e0f201ee926ec907bcec96bcd72ba2981e0ef5a0

Observation 353b6159-e330-47b4-9ca8-7ba8c2d2a0e6 · outbound

This paper cites Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?.

Meta-Black-Box-Optimization through Offline Q-function Learning Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

Reference 2017

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source=pdf_text observed=2026-08-16T04:12:58.144502Z digest=sha256:595b63890f0c1d516685909179c2f28af6a1793b3d8b0507c970290a5b777cc0

Observation 7bbf7f18-751a-4a8f-abab-1060df898b1d · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Meta-Black-Box-Optimization through Offline Q-function Learning Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 2020

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source=pdf_text observed=2026-08-16T04:12:58.170426Z digest=sha256:17e3c79efef2174ebeb2a1564ab54adbe3165107ff36d01b581671bde1ab59b4

Observation 33689923-964c-41b3-8bdc-ef707cde410f · outbound

This paper cites Decision Mamba: Reinforcement Learning via Sequence Modeling with Selective State Spaces.

Meta-Black-Box-Optimization through Offline Q-function Learning Decision Mamba: Reinforcement Learning via Sequence Modeling with Selective State Spaces

Reference 2021

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:12:58.198043Z digest=sha256:4b261d14a81394c02530af09ef8608246bd0e219eb9f48c06f296ca84804dd2e

Observation 6b2ee1c3-3670-40a7-bbf0-a6d419bbf6a1 · outbound

This paper cites ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning.

Meta-Black-Box-Optimization through Offline Q-function Learning ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning

Reference 2022

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source=pdf_text observed=2026-08-16T04:12:58.159226Z digest=sha256:64f6bd282f33caf91c1718343dcc1935050275561e0772c9ee46339d8cc14f39

Observation 1b224c58-9e7f-4f1a-87db-2306e2a425e2 · outbound

This paper cites Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization.

Meta-Black-Box-Optimization through Offline Q-function Learning Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization

Reference 2023

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:12:58.180780Z digest=sha256:28f50395d7232eeaa2e7cc4ca7fdd30a31923dae6aebe0a03cf862be7f21bbfb

Observation 53ed532d-424c-4a5f-892b-86071331d325 · outbound

This paper cites Q-value Regularized Transformer for Offline Reinforcement Learning.

Meta-Black-Box-Optimization through Offline Q-function Learning Q-value Regularized Transformer for Offline Reinforcement Learning

Reference 2024

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:12:58.164914Z digest=sha256:01faa3daea84148aca5b299794ebdf470bf6652093b79bb07e6813f008414bf4

Pith citing papers

Observation d7bab352-3317-48ac-93bc-f6e1e4870e71 · inbound

Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA cites this paper.

Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA Meta-Black-Box-Optimization through Offline Q-function Learning

Reference 31

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arxiv_id, observed 2026-06-28T17:42:25.759635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-28T17:41:40.567807Z digest=sha256:113035dbba3f0b798159809aac535c3b715f1d45c4410d4d69a26a5da3e6bcd3