{"as_of":"2026-08-09T20:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:54aadeee78b8be1b95f99a10f21f90f0b9e2fd7c0e96d2dc0c151b7bb4e7df32","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T21:03:24.196439Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:58:13.613771Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T09:36:05.866441Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04923","snapshot_observed_at":"2026-08-07T13:58:13.613771Z","title":"Cached multi-lora composition for multi-concept image generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.20525","last_updated":"2025-05-26T21:05:28Z","snapshot_observed_at":"2026-08-07T13:50:25.311474Z","submitted_at":"2025-05-26T21:05:28Z","title":"MultLFG: Training-free Multi-LoRA composition using Frequency-domain Guidance","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T13:58:13.613771Z"},"links":{"cited_paper":"/paper/2502.04923","citing_paper":"/paper/2505.20525"},"observation_digest":"sha256:6f8169c00814aecfbd4b12a7c9aa65612e61c3052bbd7cae2931d93266879e99","observation_id":"88c57f29-b007-426b-8e32-74214e1b7a0f","resolution":{"observed_at":"2026-08-07T13:58:13.613771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04923","snapshot_observed_at":"2026-08-05T05:37:07.368728Z","title":"Cached multi-lora composition for multi-concept image generation.arXiv preprint arXiv:2502.04923,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-06T07:07:31.562426Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:07.368728Z"},"links":{"cited_paper":"/paper/2502.04923","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:43efb931ba8ac599f1d11610d123185a3d9ee51519f419e350c46b14296c6456","observation_id":"6aa38fa3-4503-4c9d-835c-13486607a932","resolution":{"observed_at":"2026-08-05T05:37:07.368728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"cited_work":{"arxiv_id":"2502.04923","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.04923","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cached multi-lora composition for multi- concept image generation.arXiv preprint arXiv:2502.04923","venue":null,"work_id":"ad354ec3-33ed-4303-b9fe-b6ef45593ee0","year":null},"citing_paper":{"arxiv_id":"2604.10023","last_updated":"2026-04-11T04:32:59Z","snapshot_observed_at":"2026-07-06T22:58:43.155246Z","submitted_at":"2026-04-11T04:32:59Z","title":"FREE-Switch: Frequency-based Dynamic LoRA Switch for Style Transfer","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-10T15:55:16.158145Z"},"links":{"cited_paper":"/paper/2502.04923","citing_paper":"/paper/2604.10023"},"observation_digest":"sha256:68845c4d27d20f4af47f092e8b98e7a1acb3194b27df26fcdd28f42358ec91b8","observation_id":"2a55675a-0efe-443d-946a-d6aadc07b40a","resolution":{"observed_at":"2026-05-11T09:36:05.938905Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.04923/citation-record","integrity":"/paper/2502.04923/integrity","json":"/paper/2502.04923/citation-record.json","paper":"/paper/2502.04923"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2306.07967","last_updated":"2023-10-16T15:52:20Z","snapshot_observed_at":"2026-08-09T19:05:22.405576Z","submitted_at":"2023-06-13T17:59:32Z","title":"One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.07967","snapshot_observed_at":"2026-08-08T21:03:23.581088Z","title":"One-for-all: Generalized lora for parameter-efficient fine-tuning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.581088Z"},"links":{"cited_paper":"/paper/2306.07967","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:cd62e344a15ced135f6e2e858850f78f9355732ac5234882cee58cb96d010598","observation_id":"20cd5029-2174-4318-becd-dd0497cea0c4","resolution":{"observed_at":"2026-08-08T21:03:23.581088Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.16160","last_updated":"2024-01-30T15:44:58Z","snapshot_observed_at":"2026-07-06T17:21:51.054477Z","submitted_at":"2024-01-29T13:48:36Z","title":"LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.16160","snapshot_observed_at":"2026-08-08T21:03:23.601223Z","title":"Llava-mole: Sparse mixture of lora experts for mitigating data conflicts in instruction finetuning mllms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.601223Z"},"links":{"cited_paper":"/paper/2401.16160","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:efa7f96e956c4f2b9d7fe4c1b0353701438c2c8aa5cf139e7dc72d0796ed8b53","observation_id":"fb8170dc-8fdd-433b-bf5d-18d0d3472213","resolution":{"observed_at":"2026-08-08T21:03:23.601223Z","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":"2026-08-08T21:03:23.606425Z","title":"Scaling rectified flow transformers for high-resolution image synthesis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.606425Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:ee238cc989ed530ca8924995531d8de71c73ac2244db9fae7172acdb7a49ab73","observation_id":"456fa52c-3363-4adc-86ea-cd73bcd2ce3c","resolution":{"observed_at":"2026-08-08T21:03:23.606425Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:25.287395Z","title":"Leveraging frequency analysis for deep fake image recognition","venue":null,"work_id":"7f8a1e25-8835-406a-8b9e-3a71b3fbb956","year":2020},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.611027Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:4bb16fb4a10678494101b54f2281f5939ad405f850c60384090823bd87b7adde","observation_id":"93a9882c-3d39-4094-ad58-28e6c5a3fd0e","resolution":{"observed_at":"2026-08-08T21:03:25.292932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.13131","last_updated":"2022-03-24T15:44:50Z","snapshot_observed_at":"2026-08-06T19:08:21.390272Z","submitted_at":"2022-03-24T15:44:50Z","title":"Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.13131","snapshot_observed_at":"2026-08-08T21:03:23.615737Z","title":"Make-a-scene: Scene-based text-to-image generation with human priors, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.615737Z"},"links":{"cited_paper":"/paper/2203.13131","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:3112585c9c68202a1fa19960db5b1db8cf54203d3723f78a53f6726f3501fba6","observation_id":"64cde2e2-49af-4cb3-9f2a-7d0a9de71ac5","resolution":{"observed_at":"2026-08-08T21:03:23.615737Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:25.273129Z","title":"Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models","venue":null,"work_id":"63e405f1-98b2-4aa6-a857-812194678631","year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.620641Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:39bb1bc714456792567fdbddf8516ea431d436bf90a34c169e47c94592190e40","observation_id":"a75b4dde-aaf2-434a-81b2-8a99d290746a","resolution":{"observed_at":"2026-08-08T21:03:25.277691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08718","last_updated":"2022-03-23T19:47:21Z","snapshot_observed_at":"2026-07-06T11:01:02.207193Z","submitted_at":"2021-04-18T05:00:29Z","title":"CLIPScore: A Reference-free Evaluation Metric for Image Captioning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08718","snapshot_observed_at":"2026-08-08T21:03:23.625311Z","title":"Clipscore: A reference-free evaluation metric for image captioning, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.625311Z"},"links":{"cited_paper":"/paper/2104.08718","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:f5ea8ffab714da1e886e7e1b83b72c3993a5a99b2bef8931e4e94b82f11f9af2","observation_id":"cc1cf290-a9f7-489a-988b-cec3226d64d8","resolution":{"observed_at":"2026-08-08T21:03:23.625311Z","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":"2026-08-08T21:03:23.642774Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.642774Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:6b55df8a36bd6458475c3ecaf8bab93292cf6673ea9b07781aac822aec002073","observation_id":"dc0e6de0-a2ed-415b-a84d-f4f304e97767","resolution":{"observed_at":"2026-08-08T21:03:23.642774Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-07T07:43:16.294957Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-08T21:03:23.667834Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.667834Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:57963985962b7bfe07f803d0c878887a2b19221ae9150bf056272fd0d41f9988","observation_id":"73c763b9-7910-49df-951e-5684a4b2e4df","resolution":{"observed_at":"2026-08-08T21:03:23.667834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.13269","last_updated":"2024-08-19T03:31:19Z","snapshot_observed_at":"2026-07-06T15:58:08.777051Z","submitted_at":"2023-07-25T05:39:21Z","title":"LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.13269","snapshot_observed_at":"2026-08-08T21:03:23.697544Z","title":"Lorahub: Efficient cross-task generalization via dynamic lora composition","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.697544Z"},"links":{"cited_paper":"/paper/2307.13269","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:d0e5490e2d13fce8569fd559ce195fa5cbf902a9d28d88944f851dc2972181c3","observation_id":"9b270e29-5965-4e10-b6ae-5931e57cdfa2","resolution":{"observed_at":"2026-08-08T21:03:23.697544Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:25.248686Z","title":"Merging loras with diffusers","venue":null,"work_id":"4a1035d6-4e26-4d0c-853b-5eacb5636bf1","year":2023},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.730537Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:cb734128a0f8355bb02f209f77fbbfb5fc8fec8b0d994b5c413c7b8daeb3908f","observation_id":"38b38110-62b5-4fb8-8f26-4b7383c2cba1","resolution":{"observed_at":"2026-08-08T21:03:25.253064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05268","last_updated":"2024-11-30T11:55:19Z","snapshot_observed_at":"2026-07-06T17:57:02.141961Z","submitted_at":"2024-04-08T07:59:04Z","title":"MC$^2$: Multi-concept Guidance for Customized Multi-concept Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05268","snapshot_observed_at":"2026-08-08T21:03:23.766572Z","title":"Mc2: Multi-concept guidance for customized multi-concept generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.766572Z"},"links":{"cited_paper":"/paper/2404.05268","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:0ed36fc9c905b6098cf1a516d63aa24ffc8b1d5c3ebafe6016e96dcc31659b5e","observation_id":"99abc064-fa17-4c27-a0ff-2c4f9dfad076","resolution":{"observed_at":"2026-08-08T21:03:23.766572Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17741","last_updated":"2024-05-28T01:53:26Z","snapshot_observed_at":"2026-07-06T18:21:01.419111Z","submitted_at":"2024-05-28T01:53:26Z","title":"LoRA-Switch: Boosting the Efficiency of Dynamic LLM Adapters via System-Algorithm Co-design","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17741","snapshot_observed_at":"2026-08-08T21:03:23.796687Z","title":"Lora-switch: Boosting the efficiency of dynamic llm adapters via system-algorithm co-design","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.796687Z"},"links":{"cited_paper":"/paper/2405.17741","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:57b4a0b040d785f536411ef4bd8ec45773a8b0c4f08bffdbcf7ad9f88ada709b","observation_id":"b83b5a0b-6d8c-4313-8546-e65b43bac856","resolution":{"observed_at":"2026-08-08T21:03:23.796687Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:25.233823Z","title":"Multi-concept customization of text-to-image diffusion","venue":null,"work_id":"ca55cfc7-fcb4-4793-82a7-19fecacb43ec","year":1931},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.823632Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:4c3dcb0f026013c89ce7359c3e711f7cb65b216aa369c91fadbe97a08206a3ba","observation_id":"15dbb24b-8a3c-44a7-b9f1-e4eea37100ee","resolution":{"observed_at":"2026-08-08T21:03:25.238508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:25.203085Z","title":"Concept weaver: Enabling multi-concept fusion in text-to-image models","venue":null,"work_id":"f631a457-829a-49bd-86bd-27d697c45bae","year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.843862Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:8589e66f43218cadf69e8a55fbff559bdfd167898e2ee8b4d9f41d609d3dcd4c","observation_id":"c181f30b-28b1-45ee-ba1d-f57710af6082","resolution":{"observed_at":"2026-08-08T21:03:25.218684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:25.171237Z","title":"Instruction tuning large language models for multimodal relation extraction using lora","venue":null,"work_id":"7dbbfc8c-6197-48cc-95b1-3912f3fd6a6f","year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.853947Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:effae504ee60c31d8ad897318d714fa68303e06412c04b93155d671ae5944bdc","observation_id":"e26a68f2-e192-4b6a-b8dd-aca71aedfff6","resolution":{"observed_at":"2026-08-08T21:03:25.186331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.06914","last_updated":"2024-05-11T05:01:53Z","snapshot_observed_at":"2026-07-06T18:12:55.888082Z","submitted_at":"2024-05-11T05:01:53Z","title":"Non-confusing Generation of Customized Concepts in Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.06914","snapshot_observed_at":"2026-08-08T21:03:23.863202Z","title":"Non-confusing generation of customized concepts in diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.863202Z"},"links":{"cited_paper":"/paper/2405.06914","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:726346200e71b43d3c2796b8040ac68fdfee2f3a43a5bc6dec4c563bf054f65c","observation_id":"79a5681e-d375-4166-89f3-83d23e1f88b5","resolution":{"observed_at":"2026-08-08T21:03:23.863202Z","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":"2026-08-08T21:03:23.883999Z","title":"Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.883999Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:d85e4d76e7073dd4376da698cc4279188a3eeb3a6192d4e9224bbdbbaa987fa3","observation_id":"60b2b9f9-a8a9-4a9f-b1b9-489b1bac72b5","resolution":{"observed_at":"2026-08-08T21:03:23.883999Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:25.140236Z","title":"Deepcache: Accelerating diffusion models for free","venue":null,"work_id":"b4c73b76-7846-4e21-99d7-d20709c29cd8","year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.900948Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:de8add97481129ecaaaaed1853e7a5b00c7055701877bf03138da333afad9a3e","observation_id":"339dc27b-f8f2-452d-9b2f-34fe9621e66d","resolution":{"observed_at":"2026-08-08T21:03:25.152527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-08T21:03:23.911225Z","title":"Gpt-4 technical report, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.911225Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:c3a28a6c1b9cc77e2cfd2e5e7664ce1422a3da19014ab7101d37a3419d8706ac","observation_id":"e45b55a9-b597-46cf-9ff2-c9fe2cfc5e61","resolution":{"observed_at":"2026-08-08T21:03:23.911225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01703","last_updated":"2019-12-03T22:06:05Z","snapshot_observed_at":"2026-07-06T08:41:49.632205Z","submitted_at":"2019-12-03T22:06:05Z","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01703","snapshot_observed_at":"2026-08-08T21:03:23.924244Z","title":"Pytorch: An imperative style, high-performance deep learning library, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.924244Z"},"links":{"cited_paper":"/paper/1912.01703","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:4758406194d3beebdd9a9c50d3739d4caaa2286f63e841440e743e53f2ba9441","observation_id":"b696954e-915f-430a-af4a-7d60aac81475","resolution":{"observed_at":"2026-08-08T21:03:23.924244Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01952","last_updated":"2023-07-04T23:04:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-04T23:04:57Z","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01952","snapshot_observed_at":"2026-08-08T21:03:23.945462Z","title":"Sdxl: Improving latent diffusion models for high-resolution image synthesis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.945462Z"},"links":{"cited_paper":"/paper/2307.01952","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:31100595c4634059b94397e92d858108d87574a913438c8110a71859604625f9","observation_id":"e142fa03-8b51-4233-a715-5176945775d0","resolution":{"observed_at":"2026-08-08T21:03:23.945462Z","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":"2026-08-08T21:03:23.963758Z","title":"Zero-shot text-to-image generation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.963758Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:8844bfbe899c38ebd30090e261e080fa3cb7f3f833d5b714307f926e8519de77","observation_id":"54b2e3a5-48dc-48d3-af55-72f71a82961b","resolution":{"observed_at":"2026-08-08T21:03:23.963758Z","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":"2026-08-08T21:03:23.967606Z","title":"Hierarchical text-conditional image generation with clip latents","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.967606Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:bdcf3120fa9fb7408cd8416d6b29ceead9a2acff00cbf1d2c2afca410f1123f7","observation_id":"ed9afb59-5d42-4562-a6e0-3b4500d83028","resolution":{"observed_at":"2026-08-08T21:03:23.967606Z","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":"2026-08-08T21:03:23.971726Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.971726Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:67f42f1a029fba70c0d1c799a9e0991a1fd11bd9bae571c834bafb3125ea9541","observation_id":"778bdc7a-c579-4919-979e-fc94425e59d6","resolution":{"observed_at":"2026-08-08T21:03:23.971726Z","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":"2026-08-08T21:03:23.975693Z","title":"Photorealistic text-to-image diffusion models with deep language understanding","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.975693Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:c001596682121687311cedc111e5910c3579b5704e20e537e8d8d4bbe5539026","observation_id":"f96cfb9e-c904-4feb-a852-c1a868f31d66","resolution":{"observed_at":"2026-08-08T21:03:23.975693Z","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":"2026-08-08T21:03:23.980028Z","title":"Ziplora: Any subject in any style by effectively merging loras","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.980028Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:1269df92230dcad78c124be4bbf66025c4c390c2c84fb293e2c1204605daa5e9","observation_id":"a1edc82e-f1f3-4d04-8302-9d153c90a849","resolution":{"observed_at":"2026-08-08T21:03:23.980028Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:25.084671Z","title":"Freeu: Free lunch in diffusion u-net","venue":null,"work_id":"3857e9c6-ccf1-416c-bef2-96227dcf21c5","year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.987578Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:4ebfbb8ed2c5ca0a62db8d968a11a499e83733adc5d780d207600e05eac45ce9","observation_id":"7523337a-0aa3-4458-88e3-28636a1033c5","resolution":{"observed_at":"2026-08-08T21:03:25.098608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-07-06T10:01:50.133383Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-08T21:03:23.999238Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:23.999238Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:2effe668ce1c53b7dee5e1b47148f00c26bb90689a21010d275a4f6eb1750684","observation_id":"ab2f0e8a-e434-43d4-8ea4-96f93afeae94","resolution":{"observed_at":"2026-08-08T21:03:23.999238Z","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":"2026-08-08T21:03:24.007702Z","title":"Diffusers: State-of-the-art diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:24.007702Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:155694fa829293576f08d8fa0685f32097112cbaafadb5d20ac9943ccf7cb5ac","observation_id":"f07233a0-5f05-4db4-a0fe-44d933da13c5","resolution":{"observed_at":"2026-08-08T21:03:24.007702Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:25.024191Z","title":"Fastcomposer: Tuning-free multi-subject image generation with localized attention","venue":null,"work_id":"a3204f66-69c2-41a2-b30a-f457dfd87e28","year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:24.011701Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:21b5683f46a6ac57c10c43165a91063baea5120ee260c0b0a33d313f2e49c40f","observation_id":"4f5820ec-d4aa-4948-9b2a-36256ea2eb20","resolution":{"observed_at":"2026-08-08T21:03:25.039738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.05977","last_updated":"2023-12-28T14:13:35Z","snapshot_observed_at":"2026-08-08T18:12:30.379068Z","submitted_at":"2023-04-12T16:58:13Z","title":"ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.05977","snapshot_observed_at":"2026-08-08T21:03:24.015825Z","title":"Imagereward: Learning and evaluating human preferences for text-to-image generation, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:24.015825Z"},"links":{"cited_paper":"/paper/2304.05977","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:5eac66deba3fef19efca9e2b2198ef371f7874d687c175d05b4f6a6b58c6678e","observation_id":"5e533c71-cb8b-44ea-a8a9-5be80b27b81b","resolution":{"observed_at":"2026-08-08T21:03:24.015825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.11627","last_updated":"2024-07-11T02:30:02Z","snapshot_observed_at":"2026-07-06T17:46:11.859512Z","submitted_at":"2024-03-18T09:58:52Z","title":"LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.11627","snapshot_observed_at":"2026-08-08T21:03:24.020627Z","title":"Lora-composer: Leveraging low-rank adaptation for multi-concept customization in training-free diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:24.020627Z"},"links":{"cited_paper":"/paper/2403.11627","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:7083bec398cdd6c4016752a0738b0e2c31fe8779fe985672eb391db20d5b7554","observation_id":"33a0f868-e524-476d-b2e8-3253ec950b0e","resolution":{"observed_at":"2026-08-08T21:03:24.020627Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.17421","last_updated":"2023-10-11T05:07:37Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-29T17:34:51Z","title":"The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.17421","snapshot_observed_at":"2026-08-08T21:03:24.025085Z","title":"The dawn of lmms: Preliminary explorations with gpt-4v (ision)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:24.025085Z"},"links":{"cited_paper":"/paper/2309.17421","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:90751d43aa3cea56474744ca0c5bce67577af7f4498a65348b4b5799504485da","observation_id":"38ceff0b-3cfd-44d6-aeea-3705bf2faee9","resolution":{"observed_at":"2026-08-08T21:03:24.025085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.01800","last_updated":"2024-08-03T15:02:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-03T15:02:21Z","title":"MiniCPM-V: A GPT-4V Level MLLM on Your Phone","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.01800","snapshot_observed_at":"2026-08-08T21:03:24.029731Z","title":"Minicpm-v: A gpt-4v level mllm on your phone","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:24.029731Z"},"links":{"cited_paper":"/paper/2408.01800","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:ef06fba5e12bc002f926bac7bef921742e7774c57d103d209509d51bc69f99df","observation_id":"05bd489a-113c-4044-a4e5-e7baa155387c","resolution":{"observed_at":"2026-08-08T21:03:24.029731Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:24.967221Z","title":"Composing parameter-efficient modules with arithmetic operation","venue":null,"work_id":"c4eb2272-7b74-4434-9237-a95a54816303","year":2023},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:24.043118Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:ee57bbbb550f8a5a778829a713db50cc622b94a550848b7079c24658edddea35","observation_id":"a4690506-201f-47fb-8339-b1a4c1fc91fa","resolution":{"observed_at":"2026-08-08T21:03:24.991949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16843","last_updated":"2024-11-19T02:52:45Z","snapshot_observed_at":"2026-08-06T19:03:44.030685Z","submitted_at":"2024-02-26T18:59:18Z","title":"Multi-LoRA Composition for Image Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16843","snapshot_observed_at":"2026-08-08T21:03:24.064992Z","title":"Multi-lora composition for image generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:24.064992Z"},"links":{"cited_paper":"/paper/2402.16843","citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:19dab8753f7d116523885791da5d450a3c621e025569b87eec4b1637e04e5a97","observation_id":"612959a0-704f-423c-9923-f9bc51e5fe71","resolution":{"observed_at":"2026-08-08T21:03:24.064992Z","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":"2026-08-08T21:03:24.096490Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:24.096490Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:7f72657b0abf146dc451f08949dd368c62500e941a70b988f9073b748c579517","observation_id":"02b90d3f-83e3-43b0-a195-4e60b3364814","resolution":{"observed_at":"2026-08-08T21:03:24.096490Z","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":"2026-08-08T21:03:24.136638Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:24.136638Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:9ddf30460cc4e235ab64a91662eac34d0f352967985ca2c7d1b8dceb34dedc4d","observation_id":"fe1dcd2f-b809-4ed6-bf72-6fa639dc1654","resolution":{"observed_at":"2026-08-08T21:03:24.136638Z","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":"2026-08-08T21:03:24.162329Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:24.162329Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:51ba0e1697837470589cc3fe7883278eacd2ef14eb4d44e699f5335a3289789f","observation_id":"105d53ec-539c-49a4-9a1d-1b1d2f664425","resolution":{"observed_at":"2026-08-08T21:03:24.162329Z","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":"rel/0000775","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:24.421112Z","title":null,"venue":null,"work_id":"eb1596c9-04df-4adf-be85-929414f57313","year":1946},"citing_paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-08T21:03:24.196439Z"},"links":{"citing_paper":"/paper/2502.04923"},"observation_digest":"sha256:3299ba5bfedcd92b61eb0ec127408b10487d6d9d478146e31b647fb9b2f62083","observation_id":"56f4118d-c94f-4a7c-a5b1-7428a7967bd8","resolution":{"observed_at":"2026-08-08T21:03:24.451224Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.04923","last_updated":"2025-02-07T13:41:51Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T20:55:55.679816Z","submitted_at":"2025-02-07T13:41:51Z","title":"Cached Multi-Lora Composition for Multi-Concept Image Generation"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":1,"verified_fuzzy":10},"total_outbound_references":41},"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 41 of 41 outbound references and 3 inbound Pith citation observations for arXiv:2502.04923."}