{"as_of":"2026-08-21T21:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0d81d77f3128776802a69e07adc82c8a4506f73a386c71f43547752d3d3f74f0","coverage":[{"denominator":49,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":49,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:53:40.901530Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2505.13515/citation-record","integrity":"/paper/2505.13515/integrity","json":"/paper/2505.13515/citation-record.json","paper":"/paper/2505.13515"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:53:41.714305Z","title":"Lora: Low-rank adaptation of large language models,","venue":null,"work_id":"6f6512be-ff37-4bf0-9564-e4d1d35dc1c8","year":2022},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.013005Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:3d20986b9681ac4a7511c340c3ab8cf9eaef10d17d0f8ba8539a853f95b49057","observation_id":"bd4ad752-6391-4fad-bf9e-313b2f1791bc","resolution":{"observed_at":"2026-08-15T20:53:41.718652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:53:41.701911Z","title":"Gemini nano with the google ai edge sdk,","venue":null,"work_id":"5a1b493a-3b77-4fc7-88bd-975f2231caa4","year":2025},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.042637Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:8b6acdd95f6d3c0bad79b2037a70390aceeb4ba0de2386ec49094463b163b4f0","observation_id":"cedfbf7f-4c86-45fe-b0c1-3a546a3f87d6","resolution":{"observed_at":"2026-08-15T20:53:41.706365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:53:41.690581Z","title":"Autodroid: Llm-powered task automation in android,","venue":null,"work_id":"368748ba-da41-4cee-a01c-718c812c3781","year":2024},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.046951Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:89a088fbb8b519a66bcab3678a4267e92e0f5afe35c3aeba7e0416ef9f499e1d","observation_id":"0f24a756-a7db-4faa-b410-cf2ad3d57906","resolution":{"observed_at":"2026-08-15T20:53:41.694492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-15T20:53:40.050666Z","title":"Llama 2: Open foundation and fine-tuned chat models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.050666Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:f194c0c1d7bb3781f358185cb39504ec1082689812b988c9a97e13020f9e7f92","observation_id":"1c4093a5-1570-46f4-90d6-8d1fa35d6df1","resolution":{"observed_at":"2026-08-15T20:53:40.050666Z","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":"2026-08-15T20:53:41.652123Z","title":"The llama 4 herd: The beginning of a new era of natively multimodal ai innovation,","venue":null,"work_id":"a5adfe1f-acc1-48a6-9986-e7b6f738c81b","year":null},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.054581Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:2acedee3c8a6fc7dcf0634e882790baadfb28d90a9975b4d1993e463b3d5a4e6","observation_id":"5808cff3-bc9b-4d4a-914c-3f7ee3b4c828","resolution":{"observed_at":"2026-08-15T20:53:41.682404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10671","last_updated":"2024-09-10T13:25:53Z","snapshot_observed_at":"2026-08-17T11:08:48.802438Z","submitted_at":"2024-07-15T12:35:42Z","title":"Qwen2 Technical Report","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10671","snapshot_observed_at":"2026-08-15T20:53:40.062683Z","title":"Qwen2 technical report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.062683Z"},"links":{"cited_paper":"/paper/2407.10671","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:420e422cd393285077d4dfb0ef976fb105932131f6394804d1f036c5a1e87a28","observation_id":"9af0a624-2e65-4ea0-8aad-621611f56598","resolution":{"observed_at":"2026-08-15T20:53:40.062683Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-08-17T18:50:07.059564Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-15T20:53:40.066937Z","title":"Qwen2.5 technical report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.066937Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:40aa649487a61c1d7b1b00612bbd0497936462a3ffc35ea099f8c53620478b5b","observation_id":"ec238f76-7385-4460-a57f-abd4e68ecd3b","resolution":{"observed_at":"2026-08-15T20:53:40.066937Z","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":"2026-08-15T20:53:41.598601Z","title":"Fwdllm: Efficient federated finetuning of large language models with perturbed inferences,","venue":null,"work_id":"35306d80-374f-478b-b49b-c9f0e0cb1141","year":2024},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.071023Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:53d42361f124632804a0bf0f603973e4fde5d7068985279fdc1dba0bc3a911e0","observation_id":"4d6812f9-af14-4749-85a2-6bc07d87b5f9","resolution":{"observed_at":"2026-08-15T20:53:41.602515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04693","last_updated":"2024-08-08T16:26:07Z","snapshot_observed_at":"2026-08-17T12:52:16.307088Z","submitted_at":"2024-08-08T16:26:07Z","title":"Understanding the Performance and Estimating the Cost of LLM Fine-Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.04693","snapshot_observed_at":"2026-08-15T20:53:40.127210Z","title":"Understanding the performance and estimating the cost of llm fine-tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.127210Z"},"links":{"cited_paper":"/paper/2408.04693","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:7d02148b5aa979ac24b2c77a26b82dd3993037f57b2847f917c78f02d71eb6a6","observation_id":"402c309d-c82c-4202-a9fd-fbb0c5b9ba01","resolution":{"observed_at":"2026-08-15T20:53:40.127210Z","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":"2026-08-15T20:53:41.586261Z","title":"Energy and policy considerations for modern deep learning research,","venue":null,"work_id":"4c079d28-36ba-4a8b-9fe4-e2c06e14b9ec","year":2020},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.228561Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:92caa48bc27ca00ebae0a7d742ed0409cddc43d8f7e012e76f99af9456809b02","observation_id":"fcdaf1f0-c098-4dd0-8535-145ffb50383b","resolution":{"observed_at":"2026-08-15T20:53:41.591008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.01070","last_updated":"2018-07-08T21:42:31Z","snapshot_observed_at":"2026-08-14T19:19:26.878984Z","submitted_at":"2018-05-03T00:46:56Z","title":"What you can cram into a single vector: Probing sentence embeddings for linguistic properties","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.01070","snapshot_observed_at":"2026-08-15T20:53:40.296746Z","title":"What you can cram into a single vector: Probing sentence embeddings for linguistic properties,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.296746Z"},"links":{"cited_paper":"/paper/1805.01070","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:c1f4058c269add76c5d906eb9d3b3ea71f3bc290df66da8563b6c39e3df59c60","observation_id":"6fbcb595-7940-4915-a299-f73b761a6840","resolution":{"observed_at":"2026-08-15T20:53:40.296746Z","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":"2026-08-15T20:53:40.300591Z","title":"Towards automated circuit discovery for mechanistic interpretability,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.300591Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:6250a8acc4c90f7735cf90ca8d2dd347aaa8a947ba0bcf441c71cc0f9acf3a84","observation_id":"5acd7c73-e324-408f-880b-4189569c5a85","resolution":{"observed_at":"2026-08-15T20:53:40.300591Z","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":"2026-08-15T20:53:40.304266Z","title":"Locating and editing factual associations in gpt,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.304266Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:41f743c542dc0d44e1ee20079019fc389cfb1c7faa0f6b0e2dfac4808f84eb9e","observation_id":"2abbdd45-f22f-4e35-a253-9d745e4e3e43","resolution":{"observed_at":"2026-08-15T20:53:40.304266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.02535","last_updated":"2023-12-24T23:11:19Z","snapshot_observed_at":"2026-08-18T04:56:33.336356Z","submitted_at":"2022-09-06T14:36:57Z","title":"Analyzing Transformers in Embedding Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.02535","snapshot_observed_at":"2026-08-15T20:53:40.308235Z","title":"Analyzing transformers in embedding space,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.308235Z"},"links":{"cited_paper":"/paper/2209.02535","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:a25a154a522b989acd36d43b39ba41cf33d21f0a243b843214f9264769c50889","observation_id":"17ec31c2-1b85-456e-9680-7724f52eb97d","resolution":{"observed_at":"2026-08-15T20:53:40.308235Z","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":"2026-08-15T20:53:40.463467Z","title":"A practical review of mechanistic inter- pretability for transformer-based language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.463467Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:923ba3ca567a4982f10e08d34a7e3605588e698aa3eb0cf2512c5db5e216ebd0","observation_id":"cbd9c696-4bb5-4003-8ef4-1d1e160bc00d","resolution":{"observed_at":"2026-08-15T20:53:40.463467Z","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":"2026-08-15T20:53:41.529681Z","title":"Parameter-efficient transfer learning for nlp,","venue":null,"work_id":"e8e6af53-1b80-405a-a424-803b074963db","year":2019},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.466629Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:5678585847b3be7b42970539d96117deab36887b614c2a5b7e03c3ea63a26299","observation_id":"ab68f86a-cd23-42bc-8dbc-409da302d13e","resolution":{"observed_at":"2026-08-15T20:53:41.568184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08691","last_updated":"2021-09-02T17:34:41Z","snapshot_observed_at":"2026-08-16T20:01:36.160048Z","submitted_at":"2021-04-18T03:19:26Z","title":"The Power of Scale for Parameter-Efficient Prompt Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08691","snapshot_observed_at":"2026-08-15T20:53:40.470797Z","title":"The power of scale for parameter-efficient prompt tuning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.470797Z"},"links":{"cited_paper":"/paper/2104.08691","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:c95d3021e649eb46d3eeb9b4d43d3003fa6afdc53ae6fb1a8620ddf42fca49e7","observation_id":"52ac10f5-79fd-431a-9417-9a103be0f409","resolution":{"observed_at":"2026-08-15T20:53:40.470797Z","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":"2026-08-15T20:53:41.490214Z","title":"Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,","venue":null,"work_id":"9aa3211c-d61b-4ad4-bf65-8c3fe4a395b7","year":1950},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.474278Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:eaf3ecb55c034811f0b67a66fd7d97d8728d118b9412d4efaf368c59d19f5129","observation_id":"4d4b508a-7a4d-4402-9e8d-2561efdd5534","resolution":{"observed_at":"2026-08-15T20:53:41.493900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:53:40.477857Z","title":"Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.477857Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:8170805b4dfd2507b0d6979edeadf6b348c491cae99d75eb4ad714040772af5c","observation_id":"4f3cc393-edd7-4ab1-8fad-7f6ead94b381","resolution":{"observed_at":"2026-08-15T20:53:40.477857Z","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":"2026-08-15T20:53:40.480574Z","title":"Training neural networks with fixed sparse masks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.480574Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:ab786e3a77a8a1bee28dd9306c39c1a6aa333fb0ff8d528ba72db328bb71772e","observation_id":"29c1693d-6621-4153-9ed9-7044956c3c23","resolution":{"observed_at":"2026-08-15T20:53:40.480574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.03131","last_updated":"2022-03-07T05:04:32Z","snapshot_observed_at":"2026-08-16T17:16:34.472415Z","submitted_at":"2022-03-07T05:04:32Z","title":"Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.03131","snapshot_observed_at":"2026-08-15T20:53:40.483900Z","title":"Input-tuning: Adapting unfamiliar inputs to frozen pretrained models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.483900Z"},"links":{"cited_paper":"/paper/2203.03131","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:a40626b451a6ea92ee5b2148a6dc227c8070d07b1d3c6e740c29c309436199c0","observation_id":"4869b0a9-088b-419e-a9e2-ea51bee306ff","resolution":{"observed_at":"2026-08-15T20:53:40.483900Z","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":"2026-08-15T20:53:41.474562Z","title":"Adaptive budget allocation for parameter-efficient fine-tuning,","venue":null,"work_id":"a682875b-7ddf-4dd9-a803-0cbf42327653","year":2023},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.487914Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:5685a6a059b9f91e48adffd54e31233ee3bc92ab4120313835f293800a3a7e01","observation_id":"b19c0a4e-1247-419d-a443-e8b7360acebf","resolution":{"observed_at":"2026-08-15T20:53:41.477998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:53:41.465339Z","title":"Losparse: Structured compres- sion of large language models based on low-rank and sparse approximation,","venue":null,"work_id":"ac4c5290-a619-4883-a7a6-b00f3ba64ac1","year":2023},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.552318Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:429ae30ad47dd3108b9fc87a985e2d8f844af9077396dc6544e7c99792555209","observation_id":"882ef4f4-83fe-4a2f-91c6-cf2db27a4aec","resolution":{"observed_at":"2026-08-15T20:53:41.468645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09353","last_updated":"2024-07-09T05:59:16Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-14T17:59:34Z","title":"DoRA: Weight-Decomposed Low-Rank Adaptation","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09353","snapshot_observed_at":"2026-08-15T20:53:40.625442Z","title":"Dora: Weight-decomposed low-rank adaptation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.625442Z"},"links":{"cited_paper":"/paper/2402.09353","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:e5a01658189929e971b6490272f7b01805716470365163903844b5fa77658d1f","observation_id":"0aafc8e1-00d0-481b-8873-7d13589ae3e7","resolution":{"observed_at":"2026-08-15T20:53:40.625442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02948","last_updated":"2025-04-09T06:54:20Z","snapshot_observed_at":"2026-08-18T15:17:25.814187Z","submitted_at":"2024-04-03T15:06:43Z","title":"PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.02948","snapshot_observed_at":"2026-08-15T20:53:40.629590Z","title":"Pissa: Principal singular values and singular vectors adaptation of large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.629590Z"},"links":{"cited_paper":"/paper/2404.02948","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:e65ef460add586689a74fa4f9787edf87c26eae0225db6a08a4196b2f68b74e3","observation_id":"2500a492-31a7-4a9e-9280-ed8b9c9133f7","resolution":{"observed_at":"2026-08-15T20:53:40.629590Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12354","last_updated":"2024-07-04T18:33:00Z","snapshot_observed_at":"2026-08-18T19:20:07.218781Z","submitted_at":"2024-02-19T18:33:49Z","title":"LoRA+: Efficient Low Rank Adaptation of Large Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12354","snapshot_observed_at":"2026-08-15T20:53:40.633738Z","title":"Lora+: Efficient low rank adaptation of large models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.633738Z"},"links":{"cited_paper":"/paper/2402.12354","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:808546f3195c5147ca12c7a81679fa11087fb23b5b057e34a9f98d7ac59bdf92","observation_id":"688a283c-dfc8-400d-86ce-fac20e372f71","resolution":{"observed_at":"2026-08-15T20:53:40.633738Z","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":"2026-08-15T20:53:40.638318Z","title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.638318Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:af9851c23d45adf16bade5229b78fd715c210a552d7bce1e3a95b699841a5bbc","observation_id":"dd089d45-b55d-4be5-b6f5-7cbe70b7e641","resolution":{"observed_at":"2026-08-15T20:53:40.638318Z","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":"2026-08-15T20:53:41.449614Z","title":"Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth,","venue":null,"work_id":"a9d2f072-5741-4d24-ae9a-f3f785f13356","year":2021},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.641562Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:da81ac7bb5a25e88bb598b5d2b3f0ff22c34c17129f760b1e577b354c99dc411","observation_id":"f5d8d0f1-527d-408c-8240-7212c0b432d2","resolution":{"observed_at":"2026-08-15T20:53:41.453775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:53:41.439091Z","title":"Similarity of neural network representations revisited,","venue":null,"work_id":"fa9737bc-ae3f-4b45-a291-543eeeb5c540","year":2019},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.645471Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:80ad91e8ae495562de12ec9a819782f9dcba4672ccd02e5a2a1ac7a3694ae122","observation_id":"d616ca39-6378-42b1-a059-b4654876c6bf","resolution":{"observed_at":"2026-08-15T20:53:41.442475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:53:41.400467Z","title":"Feature selection via dependence maximization,","venue":null,"work_id":"04c06c21-340b-40da-b525-8fc44eb03a59","year":2012},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.648645Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:0eb8243f59592d55ef7737cd590521942b0b6e59be5fa356919c03b4229d394c","observation_id":"9206f83c-c6de-4476-a6f3-dc7752cc2084","resolution":{"observed_at":"2026-08-15T20:53:41.430666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:53:41.329509Z","title":"Post selection inference with kernels,","venue":null,"work_id":"9c7d5bc0-e76a-43c7-9a95-7719d558a380","year":2018},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.651931Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:41def87e5866b5b4b14b6617b93fb6d9f20055fa018705887c889f71d867e6eb","observation_id":"b03d06ae-85dd-4285-9708-00dd3aa11171","resolution":{"observed_at":"2026-08-15T20:53:41.366897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.03265","last_updated":"2021-10-26T02:48:30Z","snapshot_observed_at":"2026-08-19T14:20:48.530093Z","submitted_at":"2019-08-08T20:51:17Z","title":"On the Variance of the Adaptive Learning Rate and Beyond","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.03265","snapshot_observed_at":"2026-08-15T20:53:40.655389Z","title":"On the variance of the adaptive learning rate and beyond,","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.655389Z"},"links":{"cited_paper":"/paper/1908.03265","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:7eed476e2bf72e4481073c36c913b8d29addc3fb6a800b6f2765346e6d7fa02a","observation_id":"79aa1f88-a06f-44a5-9600-d996fb18e693","resolution":{"observed_at":"2026-08-15T20:53:40.655389Z","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":"2026-08-15T20:53:41.296820Z","title":"Algorithms for the assignment and transportation problems,","venue":null,"work_id":"118903ed-15fc-456b-a487-8945352837f8","year":1957},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.678994Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:afffa297100bd390e81779bc72d6ce99b50b54b152d7a7ca1db99165d681257e","observation_id":"ad798297-1856-4131-8fa4-5fa141b08f0b","resolution":{"observed_at":"2026-08-15T20:53:41.300859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10044","last_updated":"2019-05-24T05:48:49Z","snapshot_observed_at":"2026-08-17T14:44:42.060038Z","submitted_at":"2019-05-24T05:48:49Z","title":"BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10044","snapshot_observed_at":"2026-08-15T20:53:40.739626Z","title":"Boolq: Exploring the surprising difficulty of natural yes/no questions,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.739626Z"},"links":{"cited_paper":"/paper/1905.10044","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:d950d1c149a19ad98814d5d07c54efdbbd47abb2a21af56f909db21dbaf7840d","observation_id":"f9e864fc-b82a-4415-bc6a-a031e6770e76","resolution":{"observed_at":"2026-08-15T20:53:40.739626Z","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":"2026-08-15T20:53:41.284947Z","title":"Piqa: Reasoning about physical commonsense in natural language,","venue":null,"work_id":"7ac73c08-413f-49c7-b56e-c745973cba1a","year":2020},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.742811Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:ed323d56599562df0b128a3b2f31d8f3ad5c2fe060a81d23a1d219cb947d5f7b","observation_id":"9e2a1d31-315e-4fb1-8f25-2ac4532671c7","resolution":{"observed_at":"2026-08-15T20:53:41.289375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.09728","last_updated":"2019-09-09T17:29:55Z","snapshot_observed_at":"2026-08-12T21:45:41.485479Z","submitted_at":"2019-04-22T05:36:37Z","title":"SocialIQA: Commonsense Reasoning about Social Interactions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.09728","snapshot_observed_at":"2026-08-15T20:53:40.746002Z","title":"Socialiqa: Commonsense reasoning about social interactions,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.746002Z"},"links":{"cited_paper":"/paper/1904.09728","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:39ebe2bd56810960a0f044c5f46b54b889bbac4cd91ae919be9237a5dc260b92","observation_id":"ac72b228-8601-421c-991f-2ab2e8bf4c03","resolution":{"observed_at":"2026-08-15T20:53:40.746002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.07830","last_updated":"2019-05-19T23:57:23Z","snapshot_observed_at":"2026-08-15T09:37:44.321271Z","submitted_at":"2019-05-19T23:57:23Z","title":"HellaSwag: Can a Machine Really Finish Your Sentence?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.07830","snapshot_observed_at":"2026-08-15T20:53:40.749613Z","title":"Hellaswag: Can a machine really finish your sentence?","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.749613Z"},"links":{"cited_paper":"/paper/1905.07830","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:987f99170ea229f8d84a92536b91bf79a6e6b70220226599c6042f4eb35321a8","observation_id":"296bdada-cc06-4ebc-84fe-fce73f8799ad","resolution":{"observed_at":"2026-08-15T20:53:40.749613Z","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":"2026-08-15T20:53:40.752501Z","title":"Winogrande: An adversarial winograd schema challenge at scale,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.752501Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:f05b05df7c85b1ddb06ff67955edb767752be00674cc676ead2ac94d65e54f8c","observation_id":"d7c19b76-0091-4840-9c0b-9664660c202b","resolution":{"observed_at":"2026-08-15T20:53:40.752501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.05457","last_updated":"2018-03-14T18:04:21Z","snapshot_observed_at":"2026-08-14T19:36:07.505691Z","submitted_at":"2018-03-14T18:04:21Z","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.05457","snapshot_observed_at":"2026-08-15T20:53:40.756750Z","title":"Think you have solved question answering? try arc, the ai2 reasoning challenge,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.756750Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:158a29943c3cb74788bfb0213b71740b42c436cb74e9778d755c217418129f92","observation_id":"5dd7fe16-6b04-4254-b159-e0b370339803","resolution":{"observed_at":"2026-08-15T20:53:40.756750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.02789","last_updated":"2018-09-08T11:47:16Z","snapshot_observed_at":"2026-08-14T07:00:02.529732Z","submitted_at":"2018-09-08T11:47:16Z","title":"Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.02789","snapshot_observed_at":"2026-08-15T20:53:40.760507Z","title":"Can a suit of armor conduct electricity? a new dataset for open book question answering,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.760507Z"},"links":{"cited_paper":"/paper/1809.02789","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:27a74ac5ec53e593a863692621d7ce0833675ab0dcdd830b3ee11f65e087fb6e","observation_id":"703b33df-441b-48d6-a22f-9c1c25bf8df8","resolution":{"observed_at":"2026-08-15T20:53:40.760507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.01933","last_updated":"2023-10-09T15:38:46Z","snapshot_observed_at":"2026-08-18T09:43:13.512582Z","submitted_at":"2023-04-04T16:31:37Z","title":"LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.01933","snapshot_observed_at":"2026-08-15T20:53:40.763827Z","title":"Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.763827Z"},"links":{"cited_paper":"/paper/2304.01933","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:52606e47e4223e3c15789411e21c9e83b152acb927c7b446728bb0661b6ed704","observation_id":"7372b5ac-a46e-4e88-9f14-d710beb1c2ce","resolution":{"observed_at":"2026-08-15T20:53:40.763827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.04146","last_updated":"2017-10-23T16:45:03Z","snapshot_observed_at":"2026-08-14T21:01:22.490456Z","submitted_at":"2017-05-11T13:04:47Z","title":"Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.04146","snapshot_observed_at":"2026-08-15T20:53:40.767352Z","title":"Program induction by rationale generation: Learning to solve and explain algebraic word problems,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.767352Z"},"links":{"cited_paper":"/paper/1705.04146","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:cac45247473747783defbd21663ed9cddb6d6dbca459a005eab22272dbbf820f","observation_id":"3de47173-8e2a-446d-b479-98c10dfcfcb4","resolution":{"observed_at":"2026-08-15T20:53:40.767352Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-14T02:43:01.480086Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-15T20:53:40.827411Z","title":"Training verifiers to solve math word problems,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.827411Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:52eaef884deb03aedc39077cb3c4caf616793ac91997530a1d482a2166d5ee44","observation_id":"41ecdc2a-5262-473d-8dc4-6ab5e93ea9e2","resolution":{"observed_at":"2026-08-15T20:53:40.827411Z","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":"2026-08-15T20:53:41.268964Z","title":"Mawps: A math word problem repository,","venue":null,"work_id":"f0f4e3a0-355e-4cc9-bc02-479d965699e2","year":2016},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.886946Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:681dfbb8fed5fd1aa3632137d3405dea36732bfc6310880fdb784416dffa578f","observation_id":"a06b2f0c-c767-43d7-95aa-6ba368ed7019","resolution":{"observed_at":"2026-08-15T20:53:41.272427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.07191","last_updated":"2021-04-15T06:11:12Z","snapshot_observed_at":"2026-08-07T13:11:17.300265Z","submitted_at":"2021-03-12T10:23:47Z","title":"Are NLP Models really able to Solve Simple Math Word Problems?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.07191","snapshot_observed_at":"2026-08-15T20:53:40.890593Z","title":"Are nlp models really able to solve simple math word problems?","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.890593Z"},"links":{"cited_paper":"/paper/2103.07191","citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:5025dbdec520df17b36aa2e8cfae99c8006fb8c6399c3119683300718b0f81d8","observation_id":"7c2a6ff2-d18d-4d18-a2bc-017cc5ea72a6","resolution":{"observed_at":"2026-08-15T20:53:40.890593Z","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":"2026-08-15T20:53:40.894217Z","title":"Chain-of- thought prompting elicits reasoning in large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.894217Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:00c2fa6d5f82d4f563ec84ad16bf08ba3de94e6b394d37fc0fffac7c81b52779","observation_id":"31c1d88e-1433-4490-8ade-6e9bc0fdfe37","resolution":{"observed_at":"2026-08-15T20:53:40.894217Z","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":"2026-08-15T20:53:41.250720Z","title":"Transformer layers as painters,","venue":null,"work_id":"1b2bb88f-f760-4670-8ef1-11365ede509f","year":2025},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.898185Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:bb330df9a5801f50ec1d58471c99dfeb0e5ba2504781bb77e51bede37753f643","observation_id":"77fa7f03-bce1-48bc-9c76-5e7028e0027c","resolution":{"observed_at":"2026-08-15T20:53:41.254615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:53:41.234676Z","title":"Insights on representational similarity in neural networks with canonical correlation,","venue":null,"work_id":"e514d108-95a2-4888-9bc3-88f8ece1cd43","year":2018},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.901530Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:6a87d95359e903b9ce649ad6ba4c9d3f2f3db7b7a3ed1cdba9b8ef55fbd3ee1c","observation_id":"3d11f053-48fa-4359-a656-83af5bd25c4f","resolution":{"observed_at":"2026-08-15T20:53:41.243321Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T20:53:41.608792Z","title":"Available: https://ai.meta.com/blog/llama-4-multimodal-intelligence/","venue":null,"work_id":"20dfb327-4413-45e9-a7e8-fb79641d6958","year":null},"citing_paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-15T20:53:40.058477Z"},"links":{"citing_paper":"/paper/2505.13515"},"observation_digest":"sha256:d72a5d7cdcb61a087010c3de5cee7e5584e5e851df7bbe278908b31006a96aad","observation_id":"63334ae4-867e-4b03-a34d-963a5c971462","resolution":{"observed_at":"2026-08-15T20:53:41.612408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.13515","last_updated":"2025-05-17T04:11:17Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T09:40:40.071176Z","submitted_at":"2025-05-17T04:11:17Z","title":"LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades"},"reference_resolution":{"displayed":49,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":29,"verified_exact":0,"verified_fuzzy":19},"total_outbound_references":49},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2505.13515."}