{"as_of":"2026-08-09T18:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:97d91a6d0d29d38213abbdada2d3f15ed026f508d8ab82fc123a8eb247f4c048","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-13T14:09:20.967908Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2604.01886/citation-record","integrity":"/paper/2604.01886/integrity","json":"/paper/2604.01886/citation-record.json","paper":"/paper/2604.01886"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.13923","last_updated":"2025-02-19T18:00:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-19T18:00:14Z","title":"Qwen2.5-VL Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13923","snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"cited_paper":"/paper/2502.13923","citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:87bc6941e4c1944d43542ec885843a62eb9c7469db8e21ea1b865b3c4748050e","observation_id":"b1ee78f3-2f93-4c01-af8f-520b3852501f","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Ai auditing: The bro- ken bus on the road to ai accountability","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:5978c0c27884c158aeca419c9700f7e320e5965dc590ad95c2f227e4e61aa703","observation_id":"610d5b37-6427-4dee-b5d3-b9796940a1b8","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00523","last_updated":"2025-06-24T18:55:29Z","snapshot_observed_at":"2026-07-06T18:55:32.910650Z","submitted_at":"2024-08-01T12:54:46Z","title":"Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00523","snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Jailbreaking text-to-image models with llm- based agents.arXiv preprint arXiv:2408.00523, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"cited_paper":"/paper/2408.00523","citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:540460c2718faa47ef85879ec10841a5c0df6bbd6acde627be13da19859ba562","observation_id":"55934f19-db2a-4737-b06f-b8d99d7ca4a1","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Hotflip: White-box adversarial examples for text classifica- tion","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:afce45c6580e394311def604827a57a8d55d27b1dbc4e021176a784f9041e261","observation_id":"443d3552-f13f-4f8d-9899-3ec520ff6704","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Colornet: Investigat- ing the importance of color spaces for image classification","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:b46b03c4c7e0e460685566615c999d0c0ae2353933f27876b496c8341714ecfd","observation_id":"ee44ca50-99bb-4200-8f93-e300eca06434","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Stegcolnet: Steganal- ysis based on an ensemble colorspace approach","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:c6cc1cf87b6728ff66abe1be7ee8cd6fac0be8397d17fa6db6b5b8868687140e","observation_id":"72f2bcb5-9489-422e-a620-d2a92ecfb35b","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:1105925eac876e044b7f9a303c736cef6bdc943e903d10460575ebb2125d70de","observation_id":"3d323de6-a41f-4be7-a190-85a6e368298c","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Evilpromptfuzzer: generating inappropriate con- tent based on text-to-image models.Cybersecurity, 7(1):70,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:d32e863113b4fd6b1e46903dbe8587eb82bbdd59ed774b2fb22abeb6a243d5e3","observation_id":"0472e4fb-d83b-4097-8cc4-cc688068c47b","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:db84e0b7b31803b3a568a71fb924c72b2cb426278fa9788da778b257470cc53d","observation_id":"e376acf3-8387-40ee-9391-52899d25f8c7","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Perception-guided jailbreak against text-to-image models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:ffed8384cd635c5891bf3febe3f7c800fe176edc22a0a1e471084a53f93a1407","observation_id":"7cce26cc-8625-4058-b6dc-5e6e3611a431","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"A watermark for large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:fb6e3ec8c2d42c7662bdba5f1664f53e80469780e9c2c260d3d37d26da2b8948","observation_id":"6e33c0bf-a264-4f35-aecb-e730d4ea163a","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.07403","last_updated":"2024-07-12T03:58:05Z","snapshot_observed_at":"2026-08-07T09:29:22.417640Z","submitted_at":"2024-07-10T06:57:58Z","title":"A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.07403","snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"A survey of attacks on large vision- language models: Resources, advances, and future trends","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"cited_paper":"/paper/2407.07403","citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:d14e6d5accecec3e8abf8bfc42af5f46d6e4c64d9316c2dec989d4f1396e5253","observation_id":"a16e3c19-3995-4325-a328-36701531f6ed","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Treant: Red-teaming text-to-image models with tree-based semantic transformations","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:1961459cf35a9d3c7dfcf73c52d17ae4efe748a86253c0dd913066cdc07ea59f","observation_id":"bff7d87f-6f0d-4e09-86b4-ecd2dd48882b","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17177","last_updated":"2024-04-17T18:41:39Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-27T03:30:58Z","title":"Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17177","snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Sora: A review on background, technology, limitations, and opportunities of large vision models.arXiv preprint arXiv:2402.17177, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"cited_paper":"/paper/2402.17177","citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:cb35d3354bfc8595ca89d3ed3f1846a460d574619b28528f5ce30938649d09de","observation_id":"e9cd354b-53e3-44fc-aa3e-99c352b7e540","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Pla: Prompt learning attack against text-to-image generative models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:080f668aba5d465417e0ddad8a63a34df34d5f88bb5e3e21a5ab6afc9127bbf9","observation_id":"df45e618-0950-4ba0-811d-03a68e19626d","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02928","last_updated":"2025-05-26T11:46:09Z","snapshot_observed_at":"2026-07-06T17:55:16.774337Z","submitted_at":"2024-04-02T09:49:35Z","title":"Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.02928","snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Jailbreaking prompt attack: A controllable adversarial attack against diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"cited_paper":"/paper/2404.02928","citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:cc2186e84d18e20caae931b5d713820f9226076f381209428ac101ca7fb71562","observation_id":"68c7a32a-32fd-4f8f-80d5-b0c77811c770","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Coljailbreak: Collaborative generation and editing for jailbreaking text-to-image deep generation.Advances in Neural Information Processing Systems, 37:60335–60358,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:52258c916f0229009c5c670a68e4386c37572e566bb85c858c59852ed558e6ff","observation_id":"168279c8-d792-4549-a00f-8dccecede5f8","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Midjourney v5.2, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:eb0e0ff2b0eeee7077abde5e18b34fb8604a3b358ccd8c6f2c7322043a2156d7","observation_id":"26ca5818-d1bf-4876-bafe-2779c562b944","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"An overview of image steganography","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:a139755ef79b9c4e8c8c3ab84101171ae70095630af331d7843687130dfd3920","observation_id":"1d9d457f-e17c-4123-87b9-6b3afd730567","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.21820","last_updated":"2025-07-29T13:55:23Z","snapshot_observed_at":"2026-08-06T12:23:03.395397Z","submitted_at":"2025-07-29T13:55:23Z","title":"Anyone Can Jailbreak: Prompt-Based Attacks on LLMs and T2Is","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.21820","snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Anyone can jailbreak: Prompt-based attacks on llms and t2is.arXiv preprint arXiv:2507.21820, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"cited_paper":"/paper/2507.21820","citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:29028d9f9d2be730b8ec7470a6e30656fd7b58ddb2f2c9624915d6099bc3ac34","observation_id":"f865a987-0da5-4165-9fce-a550505055e0","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Say it differently: Linguistic styles as jailbreak vectors.arXiv preprint arXiv:2511.10519,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:3cc41dc123152137c0dba1cd57c9b9d8b22bb8d82734872a90686e21956da76d","observation_id":"90743e00-0bb3-4664-a53d-6f8c98841710","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Red teaming language models with language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:22d993cb2cf2e98d19ec8c85f9ba13c0ce09bc24e7b5c7bbb25f47764bf1b525","observation_id":"8aa5bf61-119c-4686-89b0-d94b18dcd23c","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Unsafe diffusion: On the generation of unsafe images and hateful memes from text- to-image models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:25de660946679b1cfb965d0d75f7842014af654676cbdda8711fdef62309ca08","observation_id":"a35bf6fa-eda1-418f-b228-87232162f907","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06125","last_updated":"2022-04-13T01:10:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-13T01:10:33Z","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06125","snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Hierarchical text-conditional image gener- ation with clip latents.arXiv preprint arXiv:2204.06125, 1 (2):3, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:8dfe899003b7836403a18168fdecab660ff9436c49786dfcf35970b18ae6d9db","observation_id":"9c606a4c-3bbc-4adf-b3da-ec16fa864411","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:90fa52b7ee2bdb6f0ebb1e5744944ca42cc6363cb640b159de7b8ca1dcf9121b","observation_id":"3c145c22-fe24-4de8-9f09-5aad0f7f436b","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T14:09:20.967908Z","title":"Photorealistic text-to-image diffusion models with deep","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-13T14:09:20.967908Z"},"links":{"citing_paper":"/paper/2604.01886"},"observation_digest":"sha256:25cae8d996b899af912ad3c6aff30cae9162393cf573f72572431a07f4fa7c7a","observation_id":"6556e028-5792-4ebf-9113-64bcb13c1572","resolution":{"observed_at":"2026-07-13T14:09:20.967908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2604.01886","last_updated":"2026-06-30T08:04:58Z","latest_version":2,"primary_category":"math.OC","snapshot_observed_at":"2026-07-13T14:09:20.726738Z","submitted_at":"2026-04-02T10:47:01Z","title":"When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":26,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":26},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2604.01886."}