{"as_of":"2026-08-15T06:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1c2f87b84f43045361142cf9bb665fed1e4f7df1af1b4665176136fa2526f17e","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":32,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T21:56:03.362250Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":3,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-12T21:56:03.362250Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.08212","last_updated":"2024-11-12T22:03:37Z","snapshot_observed_at":"2026-08-13T13:37:32.404749Z","submitted_at":"2024-11-12T22:03:37Z","title":"PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-12T21:56:03.362250Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2411.08212"},"observation_digest":"sha256:31467fb091ee9846577c4f392085eead8619e6f039a4a5609b6c83fe0197334b","observation_id":"e20d1821-01ce-4544-b94c-b90e35b55728","resolution":{"observed_at":"2026-08-12T21:56:03.362250Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-11T17:48:13.378236Z","title":"MoELoRA: Contrastive learning guided mixture of experts on parameter-efficient fi ne-tuning for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08587","last_updated":"2025-05-11T04:59:46Z","snapshot_observed_at":"2026-08-12T04:25:59.820438Z","submitted_at":"2024-12-11T18:06:44Z","title":"Advancing Single and Multi-task Text Classification through Large Language Model Fine-tuning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T17:48:13.378236Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2412.08587"},"observation_digest":"sha256:c430371cc3347d7f439a20941f3887ee06fd62a2ea784eddc4fae00dfb99ba73","observation_id":"c2b04420-2a69-49e7-a597-e6dd332c1f32","resolution":{"observed_at":"2026-08-11T17:48:13.378236Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-11T17:26:28.392368Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08946","last_updated":"2024-12-12T05:22:49Z","snapshot_observed_at":"2026-08-14T15:24:18.305131Z","submitted_at":"2024-12-12T05:22:49Z","title":"MoSLD: An Extremely Parameter-Efficient Mixture-of-Shared LoRAs for Multi-Task Learning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T17:26:28.392368Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2412.08946"},"observation_digest":"sha256:24eccd7b7f047a09a74b80e3c283c16d97019cd48e5f5968421f2a2f96d386dd","observation_id":"110c7021-f7e5-45e4-a431-a5bc6f4a2b00","resolution":{"observed_at":"2026-08-11T17:26:28.392368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-11T12:44:35.788673Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14219","last_updated":"2025-01-22T03:33:25Z","snapshot_observed_at":"2026-08-13T23:36:29.207493Z","submitted_at":"2024-12-18T14:11:15Z","title":"A Survey on Inference Optimization Techniques for Mixture of Experts Models","version":2},"reference_index":113,"source":"pdf_text","source_observed_at":"2026-08-11T12:44:35.788673Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2412.14219"},"observation_digest":"sha256:32fec09ed394ced1f0cea0ba68e15873ed3c620eb3a4a18eb1458a86499e6b2d","observation_id":"b7f9ec8b-1fdc-4c99-a4ce-6b5287d6d840","resolution":{"observed_at":"2026-08-11T12:44:35.788673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-10T23:42:24.398657Z","title":"Moelora: Con- trastive learning guided mixture of experts on parameter-efficient fine- tuning for large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.20064","last_updated":"2024-12-28T07:38:23Z","snapshot_observed_at":"2026-08-13T19:43:41.004957Z","submitted_at":"2024-12-28T07:38:23Z","title":"VELoRA: A Low-Rank Adaptation Approach for Efficient RGB-Event based Recognition","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T23:42:24.398657Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2412.20064"},"observation_digest":"sha256:cce6555bcc43368b0bcb9e502144e847bb7c75486e2517940001eedee90846df","observation_id":"fd108fa9-eb7d-48b7-bd50-7edf165b1940","resolution":{"observed_at":"2026-08-10T23:42:24.398657Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-09T20:29:52.595718Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00089","last_updated":"2025-01-31T18:07:21Z","snapshot_observed_at":"2026-08-09T20:21:06.208133Z","submitted_at":"2025-01-31T18:07:21Z","title":"Ensembles of Low-Rank Expert Adapters","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-09T20:29:52.595718Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2502.00089"},"observation_digest":"sha256:ae15a68167c295574cf2e97dc2908412b0634df07fe729752e56cc827e24be1f","observation_id":"155a85db-4f02-43dc-b155-c36fa0ef3c6b","resolution":{"observed_at":"2026-08-09T20:29:52.595718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-07T20:21:30.486841Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09838","last_updated":"2025-02-21T17:39:29Z","snapshot_observed_at":"2026-08-14T04:57:56.034309Z","submitted_at":"2025-02-14T00:42:36Z","title":"HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T20:21:30.486841Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2502.09838"},"observation_digest":"sha256:ca0c371d6eb98f8761b72f7ff40ac869826085567fb031ffd13945b3feac62a4","observation_id":"0fd19150-0baf-43aa-b405-72400cd1f41b","resolution":{"observed_at":"2026-08-07T20:21:30.486841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-07T14:27:25.227275Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18867","last_updated":"2025-05-24T21:01:35Z","snapshot_observed_at":"2026-08-10T03:13:57.879071Z","submitted_at":"2025-05-24T21:01:35Z","title":"Sci-LoRA: Mixture of Scientific LoRAs for Cross-Domain Lay Paraphrasing","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T14:27:25.227275Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2505.18867"},"observation_digest":"sha256:d8969e610155fb500e740b1de5be6dcf62ff358f16fe17bd63b6b7ee990f9f7f","observation_id":"a5353e0a-26d9-4fd2-9791-3b30c58681e8","resolution":{"observed_at":"2026-08-07T14:27:25.227275Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-07T13:44:34.475689Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models.arXiv preprint arXiv:2402.12851, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21226","last_updated":"2025-06-03T14:43:50Z","snapshot_observed_at":"2026-08-15T04:06:41.446744Z","submitted_at":"2025-05-27T14:10:46Z","title":"Why Do More Experts Fail? A Theoretical Analysis of Model Merging","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T13:44:34.475689Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2505.21226"},"observation_digest":"sha256:81a8fa3a2bf16cf8eb29bb753eafb6227c0435da748663fd6dda9efc8eac4c65","observation_id":"7a8a99c8-b520-4f7e-bd11-4cfa72266b36","resolution":{"observed_at":"2026-08-07T13:44:34.475689Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-07T05:34:41.479024Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07563","last_updated":"2025-06-11T07:55:32Z","snapshot_observed_at":"2026-08-07T05:28:15.323605Z","submitted_at":"2025-06-09T09:03:05Z","title":"MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:34:41.479024Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2506.07563"},"observation_digest":"sha256:a6d45b3f483f1ffd32312f6960670ae18c043fa1efdea5528aa56dd7be5418f6","observation_id":"cb1cd97b-98d6-4954-a417-2ef15981663c","resolution":{"observed_at":"2026-08-07T05:34:41.479024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2507.00029","last_updated":"2026-05-13T05:28:39Z","snapshot_observed_at":"2026-08-11T06:15:13.326412Z","submitted_at":"2025-06-17T14:58:54Z","title":"LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-19T09:05:25.236355Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2507.00029"},"observation_digest":"sha256:31146ebeb990a172e561978e5533894dc1b13e7a6945f6013ebe72de62369fd2","observation_id":"6370a6b2-7a14-40a8-b979-769d5329f32e","resolution":{"observed_at":"2026-05-19T09:07:14.634948Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T22:33:57.010285Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models.arXiv preprint arXiv:2402.12851,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.06830","last_updated":"2025-08-09T05:00:49Z","snapshot_observed_at":"2026-08-13T21:39:05.534841Z","submitted_at":"2025-08-09T05:00:49Z","title":"Onsager Principle-Based Domain Embedding for Thermodynamically Consistent Cahn-Hilliard Model in Arbitrary Domain","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T22:33:57.010285Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2508.06830"},"observation_digest":"sha256:8c8f0bb483d23a7d5ede89d5632a83e9b0ff23fbdc74d10ea12cda2109e62958","observation_id":"3292ce8d-0562-4f43-855a-2531e9ee8d82","resolution":{"observed_at":"2026-08-05T22:33:57.010285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2509.25414","last_updated":"2026-04-24T20:59:39Z","snapshot_observed_at":"2026-08-14T10:23:03.892400Z","submitted_at":"2025-09-29T19:16:14Z","title":"Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-18T11:58:39.003926Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2509.25414"},"observation_digest":"sha256:088e5a97c63265fcc0ff2bc07eaf339d375f7bd9e9c82fd924ee4a81c5672d0d","observation_id":"6c75303b-ed94-44a0-a3eb-73df5018b2ba","resolution":{"observed_at":"2026-05-18T12:01:21.205507Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2510.19316","last_updated":"2026-05-07T10:32:29Z","snapshot_observed_at":"2026-08-11T10:49:22.091928Z","submitted_at":"2025-10-22T07:26:55Z","title":"KORE: Enhancing Knowledge Injection for Large Multimodal Models via Knowledge-Oriented Controls","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-18T05:33:37.865654Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2510.19316"},"observation_digest":"sha256:317495f0bf0d8ce60eced7398196420b4259e6eaa510b47b554e90d95686b4e2","observation_id":"38e7098f-7e00-40af-a7d8-1e32a6970ee9","resolution":{"observed_at":"2026-05-18T05:35:56.230814Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-03T05:56:57.125605Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.00994","last_updated":"2026-05-28T05:45:59Z","snapshot_observed_at":"2026-08-15T01:22:58.389543Z","submitted_at":"2026-02-01T03:19:22Z","title":"Reasoning and Tool-use Compete in Agentic RL:From Quantifying Interference to Disentangled Tuning","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-03T05:56:57.125605Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2602.00994"},"observation_digest":"sha256:f05c9a45b938e88e445fd07c1dc3abff9c6e37048ec573444efca4f13d58546d","observation_id":"eff3f6da-0e6d-4d2c-bcbc-d24ea6174ce1","resolution":{"observed_at":"2026-08-03T05:56:57.125605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-02T19:55:06.807238Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.00573","last_updated":"2026-06-04T02:39:00Z","snapshot_observed_at":"2026-08-04T08:08:12.263429Z","submitted_at":"2026-02-28T09:40:11Z","title":"CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-02T19:55:06.807238Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2603.00573"},"observation_digest":"sha256:d227206db33cead3edca044ec312e4e705b97b36b560e2ce794ca2b872cc0fd4","observation_id":"d2e80be4-b240-443c-8fe1-3251e5c44b3f","resolution":{"observed_at":"2026-08-02T19:55:06.807238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-07-13T14:28:05.261852Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models.arXiv preprint arXiv:2402.12851, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.01313","last_updated":"2026-07-13T21:14:32Z","snapshot_observed_at":"2026-08-05T01:00:36.896864Z","submitted_at":"2026-04-01T18:36:47Z","title":"ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-13T14:28:05.261852Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2604.01313"},"observation_digest":"sha256:500c7a237f91909e4d284740460028dfb7543bcc4519d7259d054c4a447d2b79","observation_id":"3fb4caf9-4736-41ac-91a2-6c9cc5bf58bc","resolution":{"observed_at":"2026-07-13T14:28:05.261852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-07-15T11:44:19.622453Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models.arXiv preprint arXiv:2402.12851, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.01313","last_updated":"2026-07-13T21:14:32Z","snapshot_observed_at":"2026-08-05T01:00:36.896864Z","submitted_at":"2026-04-01T18:36:47Z","title":"ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-15T11:44:19.622453Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2604.01313"},"observation_digest":"sha256:ef8a6f072906b658faac914882a0a7a659b8e496a63b67ed435732ba1bc9b3d5","observation_id":"92bd9db7-b5a8-4931-b3e2-8148a54bea6a","resolution":{"observed_at":"2026-07-15T11:44:19.622453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2604.06291","last_updated":"2026-04-07T14:57:44Z","snapshot_observed_at":"2026-08-14T01:47:31.800378Z","submitted_at":"2026-04-07T14:57:44Z","title":"TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-10T20:17:28.805236Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2604.06291"},"observation_digest":"sha256:4bfe21fbb9f130aeda8f4484e62464886795a71b7ff4d52ed215401918bf2688","observation_id":"7ed6b305-42df-4e68-9e5f-110eb77fed99","resolution":{"observed_at":"2026-05-10T20:20:48.209979Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2604.17375","last_updated":"2026-04-19T10:58:40Z","snapshot_observed_at":"2026-08-11T04:14:27.111708Z","submitted_at":"2026-04-19T10:58:40Z","title":"When Text Hijacks Vision: Benchmarking and Mitigating Text Overlay-Induced Hallucination in Vision Language Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-10T06:41:59.641410Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2604.17375"},"observation_digest":"sha256:94b6062c6d5a45aea39de0a9d650fed10bbf35430be7f51ff001bb6a6c890ba2","observation_id":"36709a0a-f8f8-4ec6-acde-28f3e08f5340","resolution":{"observed_at":"2026-05-10T06:46:37.493773Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2604.21335","last_updated":"2026-05-06T05:14:51Z","snapshot_observed_at":"2026-08-10T21:41:05.176614Z","submitted_at":"2026-04-23T06:47:33Z","title":"Sub-Token Routing in LoRA for Adaptation and Query-Aware KV Compression","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-09T22:49:19.948502Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2604.21335"},"observation_digest":"sha256:f761b07f921ea37e8fa6f8529be448484e317a338b0c15ca59d76057d85ea515","observation_id":"a6de8c67-9cab-4dcc-8fb3-91fe88eb043c","resolution":{"observed_at":"2026-05-09T22:54:16.679633Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2604.26340","last_updated":"2026-04-29T06:45:44Z","snapshot_observed_at":"2026-08-12T20:27:51.568788Z","submitted_at":"2026-04-29T06:45:44Z","title":"Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-07T13:22:13.763578Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2604.26340"},"observation_digest":"sha256:4aa53e1c0018ba6cad81d661e8ef7e31cb932f801329e1ab7a077539759c3b90","observation_id":"ed53c71b-dbbd-4307-8ce5-2a6c9b7b85fb","resolution":{"observed_at":"2026-05-12T09:01:24.509761Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2605.08423","last_updated":"2026-05-08T19:32:43Z","snapshot_observed_at":"2026-07-06T23:20:38.385189Z","submitted_at":"2026-05-08T19:32:43Z","title":"Queryable LoRA: Instruction-Regularized Routing Over Shared Low-Rank Update Atoms","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-12T00:59:21.421307Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2605.08423"},"observation_digest":"sha256:e08b775d6a806358623879662b91d7b6e7effe08ec2eef865af85654c00c2aac","observation_id":"e94654ca-9f91-4c98-a4b6-ddc56cef190f","resolution":{"observed_at":"2026-05-12T08:31:26.625871Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2606.07542","last_updated":"2026-05-01T17:28:59Z","snapshot_observed_at":"2026-08-12T17:30:15.230676Z","submitted_at":"2026-05-01T17:28:59Z","title":"DIYHealth Suite: Dataset, Model, and Benchmark for Health Management at Home","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-07-01T07:40:03.204845Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2606.07542"},"observation_digest":"sha256:7ecd348a1abb6ddb922489eec89202f7b02b7e143ca949214531fc4cf9299aa2","observation_id":"9e12803c-fb53-4961-b593-93872b13aa3d","resolution":{"observed_at":"2026-07-01T08:05:31.562244Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2606.21645","last_updated":"2026-06-19T17:56:44Z","snapshot_observed_at":"2026-08-05T10:02:01.473600Z","submitted_at":"2026-06-19T17:56:44Z","title":"Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-06-26T14:18:11.215278Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2606.21645"},"observation_digest":"sha256:d22141385cfdf89dc66d116be3829fd45ed114c7a97ef9a1351cab9070d6f700","observation_id":"adc964da-a2d8-4af4-ab60-d6f09e6cb0c2","resolution":{"observed_at":"2026-07-04T06:39:37.811624Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2606.29375","last_updated":"2026-06-28T12:52:07Z","snapshot_observed_at":"2026-08-07T13:59:13.744367Z","submitted_at":"2026-06-28T12:52:07Z","title":"TriageRA-CCF: Source-Side Clinical Confidence and Coverage Signals for Adaptive Rank Budgeting in Medical LLMs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-30T07:40:21.132616Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2606.29375"},"observation_digest":"sha256:db86ec217d0ffbe06de1ad1dcf4ae4ab8a797bc8c72044492682b7b4173a12e8","observation_id":"4843ed96-04cb-4f98-b2bf-77ac7b33fcbc","resolution":{"observed_at":"2026-06-30T07:44:21.893772Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2606.31432","last_updated":"2026-07-02T08:43:43Z","snapshot_observed_at":"2026-07-07T00:05:07.912609Z","submitted_at":"2026-06-30T09:59:05Z","title":"Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-01T05:53:55.141657Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2606.31432"},"observation_digest":"sha256:2c3e21bff982ba58a78ab4a08de49017c159f19d6df92567fceb3b9f4b010079","observation_id":"3bd441e5-8ea3-4bfe-8e80-d0d59bea79f2","resolution":{"observed_at":"2026-07-01T10:05:41.011930Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2606.31432","last_updated":"2026-07-02T08:43:43Z","snapshot_observed_at":"2026-07-07T00:05:07.912609Z","submitted_at":"2026-06-30T09:59:05Z","title":"Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-02T19:53:25.252752Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2606.31432"},"observation_digest":"sha256:813686f8c1646d0f847ad9a09ee3d60f8a37e7df73a94719e40ba1195f4bb5a9","observation_id":"1436aa42-de8c-41d3-a1f5-57a2ed196488","resolution":{"observed_at":"2026-07-02T19:57:19.020599Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.12851","doi":"10.48550/arxiv.2402.12851","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Moelora: Contrastive learning guided mixture of experts on parameter-efficient fine-tuning for large language models","venue":"arXiv (Cornell University)","work_id":"4c3623d4-ea20-4210-b7ca-0374e0b7e099","year":2024},"citing_paper":{"arxiv_id":"2606.31432","last_updated":"2026-07-02T08:43:43Z","snapshot_observed_at":"2026-07-07T00:05:07.912609Z","submitted_at":"2026-06-30T09:59:05Z","title":"Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-03T22:08:53.025783Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2606.31432"},"observation_digest":"sha256:068b9ee0103d14001a553e2fba1d7c242d275dfaf649e6cad52ebf1dcb9d1587","observation_id":"a5064f58-f2c8-44e5-83e2-5ae994383bdb","resolution":{"observed_at":"2026-07-03T22:08:58.520247Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:17.7861+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-01T06:18:16.121296Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21978","last_updated":"2026-07-24T04:50:39Z","snapshot_observed_at":"2026-08-07T20:59:48.143638Z","submitted_at":"2026-07-24T04:50:39Z","title":"MoE$^2$-LoRA: When MoE Models Meet MoE-style Low-Rank Adaptation","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-01T06:18:16.121296Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2607.21978"},"observation_digest":"sha256:e4327d37d4fb7928483f76da4ae3f07cb99b4c957e59ae1ddc4dd7e3576a7c24","observation_id":"e0ab70ed-bfab-407c-b5fd-d4c2a0a8a7da","resolution":{"observed_at":"2026-08-01T06:18:16.121296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-02T11:50:19.409136Z","title":"arXiv preprint arXiv:2402.12851 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.24771","last_updated":"2026-06-10T13:33:10Z","snapshot_observed_at":"2026-08-11T00:51:04.779183Z","submitted_at":"2026-06-10T13:33:10Z","title":"RoCo-ACE: Rollout-Conditioned Online Distillation for Retention-Aware Knowledge Injection","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-02T11:50:19.409136Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2607.24771"},"observation_digest":"sha256:0214a7293528e2469f77e09540fe1d9cada30b4e1c073003bf465eb5ae1caa1d","observation_id":"4b5eff80-64fd-46e8-8d47-f5368d22b59c","resolution":{"observed_at":"2026-08-02T11:50:19.409136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12851","snapshot_observed_at":"2026-08-07T11:35:39.705741Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.06246","last_updated":"2026-08-06T16:32:26Z","snapshot_observed_at":"2026-08-13T15:38:24.525176Z","submitted_at":"2026-08-06T16:32:26Z","title":"A Six-Dimensional Taxonomy of Post-Training Adaptation Techniques with Applications in AI Governance","version":1},"reference_index":132,"source":"pdf_text","source_observed_at":"2026-08-07T11:35:39.705741Z"},"links":{"cited_paper":"/paper/2402.12851","citing_paper":"/paper/2608.06246"},"observation_digest":"sha256:2e73b778b49463f0fe2e845c54017738ca4d3d76667be40680614795fbbdc223","observation_id":"ba0582a4-7415-4f29-b6f9-a701bcfa67e8","resolution":{"observed_at":"2026-08-07T11:35:39.705741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.12851/citation-record","integrity":"/paper/2402.12851/integrity","json":"/paper/2402.12851/citation-record.json","paper":"/paper/2402.12851"},"outbound":[],"paper":{"arxiv_id":"2402.12851","last_updated":"2024-02-20T09:30:48Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-13T04:14:41.180426Z","submitted_at":"2024-02-20T09:30:48Z","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 32 inbound Pith citation observations for arXiv:2402.12851."}