{"as_of":"2026-08-20T12:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b81a0a868fb6479284a306b4a44d728379b9b015902149ceaa8c977ce74207a6","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T21:17:29.545442Z","state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2608.09227/citation-record","integrity":"/paper/2608.09227/integrity","json":"/paper/2608.09227/citation-record.json","paper":"/paper/2608.09227"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:28.619609Z","title":"Aho and Jeffrey D","venue":null,"work_id":null,"year":1972},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.619609Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:0d5cc1f8fba44df172d226d747d5bb188bce9f2279d5f595b1bb56e4a184116b","observation_id":"8c19715a-07e0-4240-9dc4-960206a348a5","resolution":{"observed_at":"2026-08-11T21:17:28.619609Z","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":"2026-08-11T21:17:28.645968Z","title":null,"venue":null,"work_id":null,"year":1983},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.645968Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:e49c67320dae6d958dd9e30ff0d1a2aa899417ed993d109f891f17abac726507","observation_id":"6c5493c6-4718-448a-b60b-a5acd5fcc214","resolution":{"observed_at":"2026-08-11T21:17:28.645968Z","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":"2026-08-11T21:17:28.684840Z","title":"Chandra and Dexter C","venue":null,"work_id":null,"year":1981},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.684840Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:eb9ef805bd2dd392a9f3c658f69e7ace9c502ba0433e9f535f65a700828c79d3","observation_id":"c1d8faca-6b0e-4b99-8469-1a6568646c4e","resolution":{"observed_at":"2026-08-11T21:17:28.684840Z","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":"2026-08-11T21:17:28.734898Z","title":"Scalable training of","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.734898Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:ac6671e8b23b7c901b80eff22b435916e8818f9ac80f18a260f90af4cf491422","observation_id":"73ca573f-aeb6-4e17-92ce-a50dfc0145d8","resolution":{"observed_at":"2026-08-11T21:17:28.734898Z","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":"2026-08-11T21:17:28.767379Z","title":null,"venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.767379Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:e1d26ba8c0c7731127a7c905ebf449b6b5c643e0caf13ce86ea3773590472927","observation_id":"b4608ebc-0a03-4f97-ad23-2e6b39bb2630","resolution":{"observed_at":"2026-08-11T21:17:28.767379Z","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":"2026-08-11T21:17:28.778141Z","title":"Tetreault , title =","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.778141Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:baa08b2e61d5ec87956b5ec758d81a2774b7e829df6bf39be12dbe24fae544e5","observation_id":"77947694-abe1-40b9-8ecf-d6bce7c6ea41","resolution":{"observed_at":"2026-08-11T21:17:28.778141Z","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":"2026-08-11T21:17:28.791494Z","title":"A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , Volume =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.791494Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:7fbca8d9be7fcb5da668f0b2971c2f41b242e011b58a9ed67e1ac68f8c44d079","observation_id":"41ef15e7-7bb8-479f-82c8-710d674f6d28","resolution":{"observed_at":"2026-08-11T21:17:28.791494Z","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":"2026-08-11T21:17:28.804867Z","title":"International conference on machine learning , pages=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.804867Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:9a0706912efec70816dc25ba28bf2a4b3db840eeb0024de353ad01ab6104e880","observation_id":"1f7b7e6a-5201-4972-a9db-4ce8db9de6fa","resolution":{"observed_at":"2026-08-11T21:17:28.804867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.26584","last_updated":"2026-05-26T06:07:11Z","snapshot_observed_at":"2026-08-06T08:54:48.250441Z","submitted_at":"2026-05-26T06:07:11Z","title":"O-MARC: Omni Memory-Augmented Compression Distillation for Efficient Video Understanding","version":1},"cited_work":{"arxiv_id":"2605.26584","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.26584","snapshot_observed_at":"2026-08-11T21:17:31.051654Z","title":"O-MARC: Omni Memory-Augmented Compression Distillation for Efficient Video Understanding","venue":"cs.CV","work_id":"3c21a971-f0f2-44b8-a691-f8eeae93e2ba","year":2026},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.821446Z"},"links":{"cited_paper":"/paper/2605.26584","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:b15397275a19fab3afaf08166f8b854c5043636f439f799daee7c30ca1441407","observation_id":"3b350c71-a136-4d82-b474-1024e86eeecb","resolution":{"observed_at":"2026-08-11T21:17:31.075331Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04326","last_updated":"2026-03-01T04:35:41Z","snapshot_observed_at":"2026-08-12T16:10:35.170069Z","submitted_at":"2025-02-06T18:59:40Z","title":"WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04326","snapshot_observed_at":"2026-08-11T21:17:28.835255Z","title":"arXiv preprint arXiv:2502.04326 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.835255Z"},"links":{"cited_paper":"/paper/2502.04326","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:fc6fcfdd3b20f3e569c761b2cfa65a57bd3a2fcc226aa1c3ce1eb13609cb02c6","observation_id":"87520c25-005f-4c6c-930a-de92885dd005","resolution":{"observed_at":"2026-08-11T21:17:28.835255Z","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":"2026-08-11T21:17:28.860145Z","title":"arXiv preprint arXiv:2510.10689 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.860145Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:490555894f8522834af93bc8e4afece770562d42401258c834d55a83c60404c4","observation_id":"31a505f0-2d4b-4839-a511-60dfd92688b8","resolution":{"observed_at":"2026-08-11T21:17:28.860145Z","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":"2026-08-11T21:17:28.888304Z","title":"arXiv preprint arXiv:2505.17862 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.888304Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:ff31d518946b64c8e310b6f19618be6855d68c1718ac2d98dbac40a2f71b6e41","observation_id":"2464aa8a-a315-4d6d-b5a1-e2dc9c25d46a","resolution":{"observed_at":"2026-08-11T21:17:28.888304Z","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":"2026-08-11T21:17:31.759601Z","title":"Findings of the Association for Computational Linguistics: ACL 2025 , pages=","venue":null,"work_id":"d9111e63-3c10-414a-a6a2-0c6430fc0b01","year":2025},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.900890Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:8a02c8af7de16ee38f2eaf2878b0930a97b7e2b713b9ee9216a96ea9b9837d3b","observation_id":"36d05f9e-04ea-442c-a96c-e3b78aa0df9b","resolution":{"observed_at":"2026-08-11T21:17:31.809678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2511.14582","last_updated":"2026-04-20T05:56:36Z","snapshot_observed_at":"2026-08-17T15:19:50.584357Z","submitted_at":"2025-11-18T15:22:32Z","title":"OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2511.14582","snapshot_observed_at":"2026-08-11T21:17:28.918172Z","title":"arXiv preprint arXiv:2511.14582 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.918172Z"},"links":{"cited_paper":"/paper/2511.14582","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:8a716fff608ec03df241ed3387413ab90149459b277c078cca1b2cc3dc928b80","observation_id":"146d6b89-6fbd-403c-b4d7-d11492b2e7e8","resolution":{"observed_at":"2026-08-11T21:17:28.918172Z","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":"2026-08-11T21:17:31.563147Z","title":"IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=","venue":null,"work_id":"8449a1bb-d23e-4d67-aafb-3edde9d25058","year":2024},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.936033Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:98e8a4296e1b8280909cfbf4916b34eec4c3561e4224f4d4271bb3c86a8fd43e","observation_id":"33893307-6dbf-4bfe-90c2-e776327d3458","resolution":{"observed_at":"2026-08-11T21:17:31.658806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.09461","last_updated":"2023-03-01T19:45:11Z","snapshot_observed_at":"2026-08-13T04:00:22.647615Z","submitted_at":"2022-10-17T22:23:40Z","title":"Token Merging: Your ViT But Faster","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.09461","snapshot_observed_at":"2026-08-11T21:17:28.952753Z","title":"arXiv preprint arXiv:2210.09461 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.952753Z"},"links":{"cited_paper":"/paper/2210.09461","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:7d6af7a1a0ab2b5f988c8e1fbb99bf68f9b05fc39ed34feacd08235bcd5301ca","observation_id":"6cd4bfe6-6074-472e-8c30-9139c5b62001","resolution":{"observed_at":"2026-08-11T21:17:28.952753Z","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":"2026-08-11T21:17:28.994766Z","title":"European Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:28.994766Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:e8b69ca4fe81f12384093f92bc40b368e9cc2ab561762b9d93a659aaf727c5ad","observation_id":"4c83f652-eb3a-4329-9288-186ad574435d","resolution":{"observed_at":"2026-08-11T21:17:28.994766Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07476","last_updated":"2024-10-30T06:49:54Z","snapshot_observed_at":"2026-08-14T16:25:22.654846Z","submitted_at":"2024-06-11T17:22:23Z","title":"VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07476","snapshot_observed_at":"2026-08-11T21:17:29.036515Z","title":"arXiv preprint arXiv:2406.07476 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.036515Z"},"links":{"cited_paper":"/paper/2406.07476","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:c7e7e824d80d8afb55b717d1a7a33b76ad202a81198aba22f955509dbf282220","observation_id":"b347e468-38d8-4dfb-9bc5-62f4bd267c4b","resolution":{"observed_at":"2026-08-11T21:17:29.036515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.17765","last_updated":"2025-09-22T13:26:24Z","snapshot_observed_at":"2026-08-11T00:14:34.061687Z","submitted_at":"2025-09-22T13:26:24Z","title":"Qwen3-Omni Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.17765","snapshot_observed_at":"2026-08-11T21:17:29.054080Z","title":"arXiv preprint arXiv:2509.17765 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.054080Z"},"links":{"cited_paper":"/paper/2509.17765","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:f1fa2f11b6c44bf587bf899b1e6dc1b732071fc3967dd18f3ae5ddea99d2e6c8","observation_id":"becc252f-2843-4ead-8b5b-76c959d2a01f","resolution":{"observed_at":"2026-08-11T21:17:29.054080Z","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":"2026-08-11T21:17:31.457082Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"14494a67-9bc2-4367-ac80-365b09914f6b","year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.074749Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:3344ec74e18b14ec5f450365dd7dfe6ed6e336ca702d96dea13a85fc94feb324","observation_id":"d32bdd68-c786-4a04-8746-b6ba816b6d54","resolution":{"observed_at":"2026-08-11T21:17:31.500141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12191","last_updated":"2024-10-03T15:54:49Z","snapshot_observed_at":"2026-08-06T05:35:29.109022Z","submitted_at":"2024-09-18T17:59:32Z","title":"Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12191","snapshot_observed_at":"2026-08-11T21:17:29.094754Z","title":"arXiv preprint arXiv:2409.12191 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.094754Z"},"links":{"cited_paper":"/paper/2409.12191","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:a307c3ef2a957899f775de5fdc009ef562d164195409237141614dba631e5bfb","observation_id":"a4d55725-5fb1-4bbe-a4f5-56ad263a9254","resolution":{"observed_at":"2026-08-11T21:17:29.094754Z","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":"2026-08-11T21:17:29.129948Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.129948Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:222fa68c3b945fb150a456f8ef8eb954088e0e7dfde2563c45c7848f28076511","observation_id":"af1dc68e-25dc-4bbc-8924-4e5e148959be","resolution":{"observed_at":"2026-08-11T21:17:29.129948Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.03172","last_updated":"2023-11-20T23:09:34Z","snapshot_observed_at":"2026-07-06T15:51:12.179086Z","submitted_at":"2023-07-06T17:54:11Z","title":"Lost in the Middle: How Language Models Use Long Contexts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.03172","snapshot_observed_at":"2026-08-11T21:17:29.142244Z","title":"arXiv preprint arXiv:2307.03172 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.142244Z"},"links":{"cited_paper":"/paper/2307.03172","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:b96a7e9651d6ca9a7b085288d6ed8d78aa79521a878941e6e14ec6985d646249","observation_id":"0afd9a1a-908a-4af6-86e9-0a55658c897b","resolution":{"observed_at":"2026-08-11T21:17:29.142244Z","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":"2026-08-11T21:17:29.184756Z","title":"Proceedings of the European conference on computer vision (ECCV) , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.184756Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:3e2114a22f8d24a620fc7617bb749649a867fa90fcf9940e6f5e92be9e039ed3","observation_id":"0019f5ea-a61e-47f6-95f5-fbb2c5e92bf9","resolution":{"observed_at":"2026-08-11T21:17:29.184756Z","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":"2026-08-11T21:17:31.353786Z","title":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=","venue":null,"work_id":"4a8bb867-a176-4258-9516-df56a9ef81f7","year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.233583Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:66cacdbb93c52b5042aa4daf068aedf18f128417d239c42c1da315e348d79ea4","observation_id":"a3b3678d-3a67-4bcb-b9a6-d85ca2dffb1e","resolution":{"observed_at":"2026-08-11T21:17:31.378363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:17:29.254896Z","title":", author=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.254896Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:a2c305d529a3fc9afe56b1e5e5f6f9c6f2c3c398fa33609348314cf88d84c818","observation_id":"3372bef2-30f6-4785-901a-2de8f1696440","resolution":{"observed_at":"2026-08-11T21:17:29.254896Z","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":"2026-08-11T21:17:29.287354Z","title":"International conference on learning representations , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.287354Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:20c601ae3a0a6c127857d8594bd0bd7fcf62d46703b000fe199b24c52dd35d73","observation_id":"39a78bd7-2502-4296-be39-5cda62c839f7","resolution":{"observed_at":"2026-08-11T21:17:29.287354Z","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":"2026-08-11T21:17:29.314137Z","title":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.314137Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:24986a3cd437800f3fa1f8dba859514e451efe39b7f4601fcd5ffcbf72a5dd72","observation_id":"a2740c45-4966-49a2-914d-62a534ecdea6","resolution":{"observed_at":"2026-08-11T21:17:29.314137Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-11T21:17:29.344780Z","title":"arXiv preprint arXiv:2402.03300 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.344780Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:51c7ac20732feb404b8c5c81bf1b01222d0a7037ceb282fb025608b198457ea6","observation_id":"47bc38a4-2a65-457d-aaee-3543d74768ff","resolution":{"observed_at":"2026-08-11T21:17:29.344780Z","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":"2026-08-11T21:17:29.368426Z","title":"International conference on machine learning , pages=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.368426Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:ead921841bbe8a3d62343441753992d682697450ac62b3c19e0ac68bcb9bca31","observation_id":"43d494a3-de97-4647-b7dc-a385d36c7cd0","resolution":{"observed_at":"2026-08-11T21:17:29.368426Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-20T07:04:06.309989Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-11T21:17:29.376457Z","title":"arXiv preprint arXiv:1707.06347 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.376457Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:71481aef3209e4dbec23813ac05d1bc47285565b5285e5a6efd9dc3a462466cb","observation_id":"2d9bec3c-2528-45e1-99c6-2e3acd7b0e05","resolution":{"observed_at":"2026-08-11T21:17:29.376457Z","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":"2026-08-11T21:17:29.396328Z","title":"arXiv preprint arXiv:2602.15902 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.396328Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:0a7ee0700e5b101f9cbe33d7ba3d1ed6563d8b4d7de30a0841412fd070457b9b","observation_id":"959ee15d-1d34-479f-8cf5-21dbad58897c","resolution":{"observed_at":"2026-08-11T21:17:29.396328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.28889","last_updated":"2026-05-27T07:29:40Z","snapshot_observed_at":"2026-07-06T23:38:23.512460Z","submitted_at":"2026-05-27T07:29:40Z","title":"Context Distillation as Latent Memory Management","version":1},"cited_work":{"arxiv_id":"2605.28889","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.28889","snapshot_observed_at":"2026-08-11T21:17:29.773655Z","title":"Context Distillation as Latent Memory Management","venue":"cs.LG","work_id":"6ebf583a-275c-42c2-b1d6-1a3d84d8eb1d","year":2026},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.430246Z"},"links":{"cited_paper":"/paper/2605.28889","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:adb9f51b2f42b32270a75078edd72c96bde4a4aa41756eedddf470cc2c2442c1","observation_id":"10f4f9dc-90eb-493b-b22c-06fae209c297","resolution":{"observed_at":"2026-08-11T21:17:29.807770Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.30260","last_updated":"2026-05-28T17:22:24Z","snapshot_observed_at":"2026-08-02T13:02:03.917305Z","submitted_at":"2026-05-28T17:22:24Z","title":"How LoRA Remembers? A Parametric Memory Law for LLM Finetuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.30260","snapshot_observed_at":"2026-08-11T21:17:29.454868Z","title":"arXiv preprint arXiv:2605.30260 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.454868Z"},"links":{"cited_paper":"/paper/2605.30260","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:a963550db385173fae23bdef8ab442263ed9e694d502339545eff0824c90411f","observation_id":"8bc6e4d5-7963-4d2f-a323-6a041a09d136","resolution":{"observed_at":"2026-08-11T21:17:29.454868Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2603.01097","last_updated":"2026-07-29T13:53:39Z","snapshot_observed_at":"2026-08-13T15:12:56.676372Z","submitted_at":"2026-03-01T13:28:57Z","title":"Understanding LoRA as Knowledge Memory: An Empirical Analysis","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.01097","snapshot_observed_at":"2026-08-11T21:17:29.504744Z","title":"arXiv preprint arXiv:2603.01097 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.504744Z"},"links":{"cited_paper":"/paper/2603.01097","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:254c3e68279c0b481b68e801c63e500ae2e72bb5c5171fd04b2cd99d448581c8","observation_id":"a40c33dd-1c66-4d0e-a2c9-4597372eee18","resolution":{"observed_at":"2026-08-11T21:17:29.504744Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.04351","last_updated":"2026-06-10T09:50:06Z","snapshot_observed_at":"2026-08-09T18:22:27.938557Z","submitted_at":"2026-06-03T02:07:35Z","title":"Frames2LoRA: Parametric Video Internalization for Vision-Language Models","version":2},"cited_work":{"arxiv_id":"2606.04351","doi":null,"metadata_source":"pith","pith_arxiv_id":"2606.04351","snapshot_observed_at":"2026-08-11T21:17:29.621963Z","title":"Frames2LoRA: Parametric Video Internalization for Vision-Language Models","venue":"cs.CV","work_id":"850d33da-3878-4cab-9b2a-dbc80814eb63","year":2026},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.521363Z"},"links":{"cited_paper":"/paper/2606.04351","citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:8c9b75139b3c2def9a5961722fc222fe5f153934502b89dfc80a53aeb48a4cf8","observation_id":"a411251c-ae7c-42a5-9bc1-420c61e88c76","resolution":{"observed_at":"2026-08-11T21:17:29.636342Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T21:17:29.532407Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.532407Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:254f74300141fa46fd7b82f41b0cea8ebfeb2c08dd0b1064439c4e3b8cb8f127","observation_id":"af3886ab-abfc-491d-8e22-6a0d8187bc64","resolution":{"observed_at":"2026-08-11T21:17:29.532407Z","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":"2026-08-11T21:17:31.199645Z","title":"Proceedings of the Computer Vision and Pattern Recognition Conference , pages=","venue":null,"work_id":"e207b555-878d-4910-bef3-73f0540dce36","year":null},"citing_paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-11T21:17:29.545442Z"},"links":{"citing_paper":"/paper/2608.09227"},"observation_digest":"sha256:21fb46450e085baa43c52d02b96de8f1833f674296dccef565bcccb4c7294c0a","observation_id":"b23693f6-f55a-4375-812b-2245f6a06aa6","resolution":{"observed_at":"2026-08-11T21:17:31.208027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.09227","last_updated":"2026-08-10T07:49:10Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-20T06:23:23.156412Z","submitted_at":"2026-08-10T07:49:10Z","title":"Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":30,"verified_exact":0,"verified_fuzzy":5},"total_outbound_references":38},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2608.09227."}