{"as_of":"2026-08-14T18:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8fc81e508b70dfeabac8bfb486cac4bed4395143488997f3a3809875b5f3f852","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T05:37:07.368728Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T05:33:58.924093Z","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":"2026-05-10T05:36:01.654182Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"cited_work":{"arxiv_id":"2509.10535","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.10535","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Semantic-guided LoRA Parameters Generation","venue":null,"work_id":"d32b8570-20a1-4fda-88a4-69195b48dcaa","year":2025},"citing_paper":{"arxiv_id":"2604.17822","last_updated":"2026-04-20T05:20:39Z","snapshot_observed_at":"2026-08-13T00:27:28.017289Z","submitted_at":"2026-04-20T05:20:39Z","title":"GR4CIL: Gap-compensated Routing for CLIP-based Class Incremental Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T05:33:58.924093Z"},"links":{"cited_paper":"/paper/2509.10535","citing_paper":"/paper/2604.17822"},"observation_digest":"sha256:83972da35984a05e062b9a5b7a37353abe52de1fe651ea874b1b3d7abf99d5ea","observation_id":"55166bd0-4fa7-4c91-8ed9-9c05ca8d27b3","resolution":{"observed_at":"2026-05-10T05:36:01.655600Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.10535/citation-record","integrity":"/paper/2509.10535/integrity","json":"/paper/2509.10535/citation-record.json","paper":"/paper/2509.10535"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-05T05:37:06.155369Z","title":"Auto-encoding variational bayes.arXiv preprint arXiv:1312.6114,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:06.155369Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:078f4ac18af6b0c0b56b60b3115bcdd8e0ee51fad34a66bba2e9023a996a9c5c","observation_id":"4adfb6a1-d1d5-4bde-87d0-1f93ee84d71f","resolution":{"observed_at":"2026-08-05T05:37:06.155369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.15698","last_updated":"2023-09-27T14:40:12Z","snapshot_observed_at":"2026-08-14T00:02:12.911882Z","submitted_at":"2023-09-27T14:40:12Z","title":"Deep Model Fusion: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.15698","snapshot_observed_at":"2026-08-05T05:37:06.252859Z","title":"Deep model fusion: A survey","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:06.252859Z"},"links":{"cited_paper":"/paper/2309.15698","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:9f62e58b4db189513beae0a811f020716d60ff1de76463828ff6d2ceb30449e7","observation_id":"3afed03b-54ec-4048-a723-2a0e02fc70e6","resolution":{"observed_at":"2026-08-05T05:37:06.252859Z","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":"2026-08-05T05:37:06.328118Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:06.328118Z"},"links":{"citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:0aea236249dde70881a42e119411f1e3a8caecae8227c4ae6e55be2de993f965","observation_id":"c33b0fe9-81b8-4156-8d14-15fbfd97f78e","resolution":{"observed_at":"2026-08-05T05:37:06.328118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.08942","last_updated":"2023-01-31T12:41:41Z","snapshot_observed_at":"2026-08-13T14:03:47.781326Z","submitted_at":"2022-10-17T11:09:35Z","title":"Meta-Learning via Classifier(-free) Diffusion Guidance","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.08942","snapshot_observed_at":"2026-08-05T05:37:06.463131Z","title":"Meta- learning via classifier (-free) diffusion guidance.arXiv preprint arXiv:2210.08942,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:06.463131Z"},"links":{"cited_paper":"/paper/2210.08942","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:76a1fd44353b2db59b00e91e965aca6e499653328965e9c4b1f59222bfdcd713","observation_id":"b3fd6efb-acd6-4217-b4cb-4f3a4193c18e","resolution":{"observed_at":"2026-08-05T05:37:06.463131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.17635","last_updated":"2025-07-05T18:59:06Z","snapshot_observed_at":"2026-08-10T04:31:58.491960Z","submitted_at":"2025-01-29T13:12:01Z","title":"In-Context Meta LoRA Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.17635","snapshot_observed_at":"2026-08-05T05:37:06.665333Z","title":"In-context meta lora generation.arXiv preprint arXiv:2501.17635,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:06.665333Z"},"links":{"cited_paper":"/paper/2501.17635","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:9565d5fe50f025e24e9bd69c11e8cabb67690997a9b302a2031a031e12bca6d5","observation_id":"3d8da03d-4285-4e4c-90c9-3055c7c4a931","resolution":{"observed_at":"2026-08-05T05:37:06.665333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-05T05:37:07.020896Z","title":"Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:07.020896Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:99e1eccb1dac4bbda7628c460ced07919032c6b9ab0b13f0c61854953711d567","observation_id":"d293f28e-97ee-4d0d-9155-5615160c3787","resolution":{"observed_at":"2026-08-05T05:37:07.020896Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12191","last_updated":"2024-10-03T15:54:49Z","snapshot_observed_at":"2026-08-06T05:35:29.109022Z","submitted_at":"2024-09-18T17:59:32Z","title":"Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12191","snapshot_observed_at":"2026-08-05T05:37:07.195660Z","title":"Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution.arXiv preprint arXiv:2409.12191,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:07.195660Z"},"links":{"cited_paper":"/paper/2409.12191","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:6262a35e50cb4ac2367395ff55a363f2b9cea743499ed0184df6f797b1d0c930","observation_id":"602d831f-4634-4850-a98f-fcca70b34668","resolution":{"observed_at":"2026-08-05T05:37:07.195660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.10512","last_updated":"2023-12-20T20:56:14Z","snapshot_observed_at":"2026-08-12T23:46:47.414754Z","submitted_at":"2023-03-18T22:36:25Z","title":"AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.10512","snapshot_observed_at":"2026-08-05T05:37:07.244148Z","title":"Composing parameter-efficient modules with arithmetic operation.Advances in Neural Information Processing Systems, 36:12589–12610, 2023a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:07.244148Z"},"links":{"cited_paper":"/paper/2303.10512","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:4889e241744d484e6c902cd8cb1c6636540379ff3567453109e2eef6dc5cdb1e","observation_id":"ddd40eaa-a6ff-4374-add7-244d80d2f90d","resolution":{"observed_at":"2026-08-05T05:37:07.244148Z","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":"2026-08-10T06:10:52.406971Z","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":"2026-08-14T14:12:21.901879Z","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:e6add054b55ec16e0d64f3ab5d8c8028a5b357f82e15a9b8d15bafdd7b3ce579","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":"2209.12892","last_updated":"2022-09-26T17:59:58Z","snapshot_observed_at":"2026-08-13T14:19:00.693552Z","submitted_at":"2022-09-26T17:59:58Z","title":"Learning to Learn with Generative Models of Neural Network Checkpoints","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.12892","snapshot_observed_at":"2026-08-05T05:37:06.534636Z","title":"Learning to learn with generative models of neural network checkpoints.arXiv preprint arXiv:2209.12892,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:06.534636Z"},"links":{"cited_paper":"/paper/2209.12892","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:de91326ede816eeab9f3e51eb7a584b30e9c4be2a72eb4a04da1a530380f9edd","observation_id":"9c9e960f-6924-4a9a-8523-7b02f82cc3b3","resolution":{"observed_at":"2026-08-05T05:37:06.534636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18153","last_updated":"2024-10-25T08:47:06Z","snapshot_observed_at":"2026-08-13T04:08:27.390151Z","submitted_at":"2024-02-28T08:34:23Z","title":"Diffusion-Based Neural Network Weights Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18153","snapshot_observed_at":"2026-08-05T05:37:06.908891Z","title":"Diffusion-based neural network weights generation.arXiv preprint arXiv:2402.18153,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:06.908891Z"},"links":{"cited_paper":"/paper/2402.18153","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:8eed9e847a9187f5f7f6dbf4d73b3d6d4595eb0a4333c3f19c8623a3719491f2","observation_id":"b4c33e8e-015d-41dd-859b-5ea1c3320a80","resolution":{"observed_at":"2026-08-05T05:37:06.908891Z","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":"2026-08-14T14:11:39.416219Z","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":"2026-08-05T05:37:05.950405Z","title":"Lorahub: Efficient cross-task generalization via dynamic lora composition.arXiv preprint arXiv:2307.13269,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:05.950405Z"},"links":{"cited_paper":"/paper/2307.13269","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:14eb80667a6a43780750c5995cbcb2148fc25fbeea70527faf3ca097c2f5269b","observation_id":"b00fdbd6-519d-42ad-9c4c-d066329759de","resolution":{"observed_at":"2026-08-05T05:37:05.950405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.07027","last_updated":"2023-03-28T13:37:54Z","snapshot_observed_at":"2026-08-13T12:44:46.344750Z","submitted_at":"2023-02-14T13:09:23Z","title":"AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.07027","snapshot_observed_at":"2026-08-05T05:37:05.785956Z","title":"Adaptersoup: Weight averaging to improve generalization of pretrained language models.arXiv preprint arXiv:2302.07027,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:05.785956Z"},"links":{"cited_paper":"/paper/2302.07027","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:e34c33566772f682672b80382bf7bf2ba50c55e582c029789338cd823faaf1d5","observation_id":"3607e9dd-72d6-4334-98d1-783525177c5a","resolution":{"observed_at":"2026-08-05T05:37:05.785956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.09106","last_updated":"2016-12-01T10:08:15Z","snapshot_observed_at":"2026-08-14T14:43:47.411225Z","submitted_at":"2016-09-27T05:57:00Z","title":"HyperNetworks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.09106","snapshot_observed_at":"2026-08-05T05:37:05.834174Z","title":"Hypernetworks.arXiv preprint arXiv:1609.09106,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:05.834174Z"},"links":{"cited_paper":"/paper/1609.09106","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:2966a5b877b88e680048f10546e04dec2b2a069eb357227ef6a36db846ea761c","observation_id":"9d6355b6-8acb-497e-a523-1db14c02eaa9","resolution":{"observed_at":"2026-08-05T05:37:05.834174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.01415","last_updated":"2024-08-02T17:43:34Z","snapshot_observed_at":"2026-08-12T23:09:28.146558Z","submitted_at":"2024-08-02T17:43:34Z","title":"Conditional LoRA Parameter Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.01415","snapshot_observed_at":"2026-08-05T05:37:06.024629Z","title":"Conditional lora parameter generation.arXiv preprint arXiv:2408.01415,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:06.024629Z"},"links":{"cited_paper":"/paper/2408.01415","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:9b79048ddbf46bcd0902e2a50b681ce152696c2d949905e699c0322dda0aa51e","observation_id":"5b4201a8-c429-4df8-b51f-3094580d8ad5","resolution":{"observed_at":"2026-08-05T05:37:06.024629Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05148","last_updated":"2025-08-10T17:09:36Z","snapshot_observed_at":"2026-08-14T05:48:55.563611Z","submitted_at":"2024-12-06T16:04:56Z","title":"LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.05148","snapshot_observed_at":"2026-08-05T05:37:06.770013Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-05T05:37:06.770013Z"},"links":{"cited_paper":"/paper/2412.05148","citing_paper":"/paper/2509.10535"},"observation_digest":"sha256:358cdbe30791d30d6c6de8dde4cb2688cfe13994258d6bd0882b733287329065","observation_id":"9267d36a-6741-431e-a856-d31b66ce8d6d","resolution":{"observed_at":"2026-08-05T05:37:06.770013Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.10535","last_updated":"2025-09-05T14:43:41Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T14:12:21.901879Z","submitted_at":"2025-09-05T14:43:41Z","title":"Semantic-guided LoRA Parameters Generation"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":16},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 1 inbound Pith citation observation for arXiv:2509.10535."}