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Paper Citation Record · LEDGER

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling

As of 10 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2604.01886.

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

pith.paper-citation-record.v1
2604.01886 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T14:09:20.967908Z

measured 26 of 26 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

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Outbound references

Observation b1ee78f3-2f93-4c01-af8f-520b3852501f · outbound

This paper cites Qwen2.5-VL Technical Report.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Qwen2.5-VL Technical Report

Reference 1

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Observation 610d5b37-6427-4dee-b5d3-b9796940a1b8 · outbound

This paper cites Ai auditing: The bro- ken bus on the road to ai accountability.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Ai auditing: The bro- ken bus on the road to ai accountability

Reference 2

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Observation 55934f19-db2a-4737-b06f-b8d99d7ca4a1 · outbound

This paper cites Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models

Reference 3

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Observation 443d3552-f13f-4f8d-9899-3ec520ff6704 · outbound

This paper cites Hotflip: White-box adversarial examples for text classifica- tion.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Hotflip: White-box adversarial examples for text classifica- tion

Reference 4

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Observation ee44ca50-99bb-4200-8f93-e300eca06434 · outbound

This paper cites Colornet: Investigat- ing the importance of color spaces for image classification.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Colornet: Investigat- ing the importance of color spaces for image classification

Reference 5

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Observation 72f2bcb5-9489-422e-a620-d2a92ecfb35b · outbound

This paper cites Stegcolnet: Steganal- ysis based on an ensemble colorspace approach.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Stegcolnet: Steganal- ysis based on an ensemble colorspace approach

Reference 6

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Observation 3d323de6-a41f-4be7-a190-85a6e368298c · outbound

This paper cites an unresolved cited work.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Unresolved cited work

Reference 7

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Observation 0472e4fb-d83b-4097-8cc4-cc688068c47b · outbound

This paper cites Evilpromptfuzzer: generating inappropriate con- tent based on text-to-image models.Cybersecurity, 7(1):70,.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Evilpromptfuzzer: generating inappropriate con- tent based on text-to-image models.Cybersecurity, 7(1):70,

Reference 8

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Observation e376acf3-8387-40ee-9391-52899d25f8c7 · outbound

This paper cites Deep residual learning for image recognition.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Deep residual learning for image recognition

Reference 9

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Observation 7cce26cc-8625-4058-b6dc-5e6e3611a431 · outbound

This paper cites Perception-guided jailbreak against text-to-image models.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Perception-guided jailbreak against text-to-image models

Reference 10

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Observation 6e33c0bf-a264-4f35-aecb-e730d4ea163a · outbound

This paper cites A watermark for large language models.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling A watermark for large language models

Reference 11

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Observation a16e3c19-3995-4325-a328-36701531f6ed · outbound

This paper cites A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends

Reference 12

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Observation bff7d87f-6f0d-4e09-86b4-ecd2dd48882b · outbound

This paper cites Treant: Red-teaming text-to-image models with tree-based semantic transformations.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Treant: Red-teaming text-to-image models with tree-based semantic transformations

Reference 13

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source=pdf_text observed=2026-07-13T14:09:20.967908Z digest=sha256:20200d35639115c43c31f033a0a479e1f5b1b6a630872b32557ee7d51c262da2

Observation e9cd354b-53e3-44fc-aa3e-99c352b7e540 · outbound

This paper cites Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Reference 14

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Observation df45e618-0950-4ba0-811d-03a68e19626d · outbound

This paper cites Pla: Prompt learning attack against text-to-image generative models.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Pla: Prompt learning attack against text-to-image generative models

Reference 15

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source=pdf_text observed=2026-07-13T14:09:20.967908Z digest=sha256:22402f0932ce80a7da5a83101e9593030fd04dde38fefa0f73eaac4c07c2a594

Observation 68c7a32a-32fd-4f8f-80d5-b0c77811c770 · outbound

This paper cites Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models

Reference 16

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Observation 168279c8-d792-4549-a00f-8dccecede5f8 · outbound

This paper cites Coljailbreak: Collaborative generation and editing for jailbreaking text-to-image deep generation.Advances in Neural Information Processing Systems, 37:60335–60358,.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Coljailbreak: Collaborative generation and editing for jailbreaking text-to-image deep generation.Advances in Neural Information Processing Systems, 37:60335–60358,

Reference 17

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Observation 26ca5818-d1bf-4876-bafe-2779c562b944 · outbound

This paper cites Midjourney v5.2, 2023.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Midjourney v5.2, 2023

Reference 18

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Observation 1d9d457f-e17c-4123-87b9-6b3afd730567 · outbound

This paper cites An overview of image steganography.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling An overview of image steganography

Reference 19

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Observation f865a987-0da5-4165-9fce-a550505055e0 · outbound

This paper cites Anyone Can Jailbreak: Prompt-Based Attacks on LLMs and T2Is.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Anyone Can Jailbreak: Prompt-Based Attacks on LLMs and T2Is

Reference 20

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Observation 90743e00-0bb3-4664-a53d-6f8c98841710 · outbound

This paper cites Say it differently: Linguistic styles as jailbreak vectors.arXiv preprint arXiv:2511.10519,.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Say it differently: Linguistic styles as jailbreak vectors.arXiv preprint arXiv:2511.10519,

Reference 21

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Observation 8aa5bf61-119c-4686-89b0-d94b18dcd23c · outbound

This paper cites Red teaming language models with language models.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Red teaming language models with language models

Reference 22

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Observation a35bf6fa-eda1-418f-b228-87232162f907 · outbound

This paper cites Unsafe diffusion: On the generation of unsafe images and hateful memes from text- to-image models.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Unsafe diffusion: On the generation of unsafe images and hateful memes from text- to-image models

Reference 23

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Observation 9c606a4c-3bbc-4adf-b3da-ec16fa864411 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 24

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Observation 3c145c22-fe24-4de8-9f09-5aad0f7f436b · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling High-resolution image synthesis with latent diffusion models

Reference 25

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Observation 6556e028-5792-4ebf-9113-64bcb13c1572 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep.

When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling Photorealistic text-to-image diffusion models with deep

Reference 26

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Pith citing papers

No inbound Pith citation observations are available.