{"as_of":"2026-08-15T05:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e8c496b1e7ee88a9aace13faa904eaf1958001e03b1136197112dddc83191140","coverage":[{"denominator":61,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":61,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T13:22:48.676225Z","state":"measured"},{"denominator":61,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":61,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2507.20749/citation-record","integrity":"/paper/2507.20749/integrity","json":"/paper/2507.20749/citation-record.json","paper":"/paper/2507.20749"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.15024","last_updated":"2024-02-09T17:59:40Z","snapshot_observed_at":"2026-08-14T10:49:22.047809Z","submitted_at":"2024-01-26T17:35:45Z","title":"SliceGPT: Compress Large Language Models by Deleting Rows and Columns","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.15024","snapshot_observed_at":"2026-08-06T13:22:44.042601Z","title":"arXiv preprint arXiv:2401.15024 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.042601Z"},"links":{"cited_paper":"/paper/2401.15024","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:dbcbdf8f040cf014344c0c7233981dcd58731bf576e4b03b59dee238bcb5a50d","observation_id":"cde1480c-a661-4101-87e6-a7b4054270e9","resolution":{"observed_at":"2026-08-06T13:22:44.042601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.15701","last_updated":"2021-07-22T13:13:45Z","snapshot_observed_at":"2026-08-14T11:34:25.805986Z","submitted_at":"2020-12-31T16:34:54Z","title":"BinaryBERT: Pushing the Limit of BERT Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.15701","snapshot_observed_at":"2026-08-06T13:22:44.098152Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.098152Z"},"links":{"cited_paper":"/paper/2012.15701","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:73512c8d4c1a32c09db72e0e9e92eb12a667031c9c74e553b3db5ab1a0bc84c9","observation_id":"71696497-427a-4cb6-b9f3-d267c42b3438","resolution":{"observed_at":"2026-08-06T13:22:44.098152Z","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":"2026-08-06T13:22:52.097550Z","title":null,"venue":null,"work_id":"201ab3fc-3029-49cd-9a79-ab7977a6d57a","year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.158846Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:795cc7525864117f8a60cd325fc9d85d285717544c27f20e547b2c255044486f","observation_id":"b1f1ec04-f4bf-424a-afd5-c1f0e236a85a","resolution":{"observed_at":"2026-08-06T13:22:52.182974Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16821","last_updated":"2024-04-29T20:24:30Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-04-25T17:59:19Z","title":"How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16821","snapshot_observed_at":"2026-08-06T13:22:44.247960Z","title":"arXiv preprint arXiv:2404.16821 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.247960Z"},"links":{"cited_paper":"/paper/2404.16821","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:1a4b2668e44a18fe42613b758a031b894fb37da9326947176538f5d88e8a0810","observation_id":"9a3a4765-f16a-48f8-9a9c-b7f8a7db46db","resolution":{"observed_at":"2026-08-06T13:22:44.247960Z","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":"2026-08-06T13:22:44.288220Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.288220Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:b91d889fc268e7e14825a729f67a469e79b47d71c0918989b0b56daa7708c59c","observation_id":"89b2e58c-ab52-4e06-b7ff-523d1e445c2c","resolution":{"observed_at":"2026-08-06T13:22:44.288220Z","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":"2026-08-06T13:22:51.935175Z","title":null,"venue":null,"work_id":"f1a3a46b-7005-4eb4-ad19-deb902fc65dc","year":2023},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.382454Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:3bfa75498f80dcc4ceb79d4b65d27f8c5c4419704800ac8b0ee4124dc2a5c72e","observation_id":"e5f32ebe-0e91-4a1b-ad90-061213ed3def","resolution":{"observed_at":"2026-08-06T13:22:52.009137Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.16886","last_updated":"2023-12-30T04:59:21Z","snapshot_observed_at":"2026-07-29T22:12:01.108911Z","submitted_at":"2023-12-28T08:21:24Z","title":"MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.16886","snapshot_observed_at":"2026-08-06T13:22:44.438590Z","title":"arXiv preprint arXiv:2312.16886 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.438590Z"},"links":{"cited_paper":"/paper/2312.16886","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:3677a02ecadd075c70e17cdf4f09800918317f5484856b3cc3e5e579a1189bcc","observation_id":"8544ec58-e517-4bfc-97f5-7b8cb41c1d5c","resolution":{"observed_at":"2026-08-06T13:22:44.438590Z","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":"2026-08-06T13:22:44.503133Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.503133Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:e17be4bd248fafe352df94608e9d9202a804690d6d72eb2043e93bdf9b589329","observation_id":"504bbf55-2121-414f-a9e0-ee8cb76bbafd","resolution":{"observed_at":"2026-08-06T13:22:44.503133Z","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":"2026-08-06T13:22:51.745634Z","title":"int8 (): 8-bit matrix multiplicationfortransformersatscale.AdvancesinNeuralInformationProcessing Systems 35, 30318–30332 (2022)","venue":null,"work_id":"63d3fe6e-c0b2-4047-bb39-d6eefc7426f8","year":2022},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.570859Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:76649aa154cbc6ab5baa1fb249e75988dd9fd9602ae36088b2eaad8dc93642de","observation_id":"d5878f7d-8e4d-4ae7-8ef7-abbf0b611e3d","resolution":{"observed_at":"2026-08-06T13:22:51.820101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:22:51.632485Z","title":null,"venue":null,"work_id":"481971a5-a7ba-4bb1-89b7-0d1edebfa272","year":2019},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.621053Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:0530f65bbc20c1335086a2fc249036d30e4409aadefb12feca65e53dda0a252a","observation_id":"acf926ff-1937-477c-8b0c-130bef12dffc","resolution":{"observed_at":"2026-08-06T13:22:51.695570Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.07565","last_updated":"2017-11-09T23:50:55Z","snapshot_observed_at":"2026-08-14T20:59:11.517509Z","submitted_at":"2017-05-22T05:54:37Z","title":"Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.07565","snapshot_observed_at":"2026-08-06T13:22:44.661615Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.661615Z"},"links":{"cited_paper":"/paper/1705.07565","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:b0790d930718978b7e60da2b8e5a7c04b23b0c213b3e0f9a3244d410b3e471f6","observation_id":"4290d994-172a-4b45-b8f7-189b17ba2e12","resolution":{"observed_at":"2026-08-06T13:22:44.661615Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.11556","last_updated":"2019-09-25T15:35:03Z","snapshot_observed_at":"2026-08-13T06:47:52.053569Z","submitted_at":"2019-09-25T15:35:03Z","title":"Reducing Transformer Depth on Demand with Structured Dropout","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.11556","snapshot_observed_at":"2026-08-06T13:22:44.726762Z","title":"arXiv preprint arXiv:1909.11556 (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.726762Z"},"links":{"cited_paper":"/paper/1909.11556","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:b546736795e555c58459ecb337de3957eb030b63318a6dfc2e4540645a717c1f","observation_id":"3776073e-b025-4559-a80c-f2d61610c801","resolution":{"observed_at":"2026-08-06T13:22:44.726762Z","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":"2026-08-06T13:22:44.801416Z","title":"In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.801416Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:fa619d704962e86b6992a11d804cd6a63fd1225adf4fa19781b6e588e591f884","observation_id":"b314f67b-cb0a-4bf8-856c-e1dc8b7a240f","resolution":{"observed_at":"2026-08-06T13:22:44.801416Z","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":"2026-08-06T13:22:51.408813Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":"517fdae2-e96f-4106-85c2-b61b5bc3325b","year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.881891Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:36ada3cc2107dcb76df4ff8c81c3c9fb6cdd7452058a5da2123d3a5476ba7167","observation_id":"16654f6e-d277-4a49-b6ac-3de1ccce50d8","resolution":{"observed_at":"2026-08-06T13:22:51.495830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.03635","last_updated":"2019-03-04T15:51:11Z","snapshot_observed_at":"2026-08-14T19:37:43.556604Z","submitted_at":"2018-03-09T18:51:28Z","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.03635","snapshot_observed_at":"2026-08-06T13:22:44.947753Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.947753Z"},"links":{"cited_paper":"/paper/1803.03635","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:b126a4f972b701e7586130080b5b6101123a45efb9c328705a15b5a373aef35d","observation_id":"1cc44439-724d-4e5c-843c-84e2f4d2c5cf","resolution":{"observed_at":"2026-08-06T13:22:44.947753Z","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":"2026-08-06T13:22:44.982668Z","title":"International Journal of Computer Vision 129(6), 1789–1819 (Mar Pruning and Recovery Techniques for Compressing MLLMs 15 2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:44.982668Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:2332e895f3225a7c32c621a28519d92fb9b578090f21dd8ff14d5268d1cffe89","observation_id":"d07fa3d9-2024-433d-9135-a580bfdec58b","resolution":{"observed_at":"2026-08-06T13:22:44.982668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.08543","last_updated":"2026-01-31T09:16:35Z","snapshot_observed_at":"2026-08-13T16:04:46.474244Z","submitted_at":"2023-06-14T14:44:03Z","title":"MiniLLM: On-Policy Distillation of Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.08543","snapshot_observed_at":"2026-08-06T13:22:45.128888Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.128888Z"},"links":{"cited_paper":"/paper/2306.08543","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:38b7c57e61753640b9f60f68b9d555dcacc28b9ed2c9562ea6f8fb4a8e4a0398","observation_id":"0e0e02db-332f-41d8-8a0e-2cc44286cb70","resolution":{"observed_at":"2026-08-06T13:22:45.128888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11530","last_updated":"2024-07-22T09:54:40Z","snapshot_observed_at":"2026-08-14T10:26:56.366247Z","submitted_at":"2024-02-18T10:09:10Z","title":"Efficient Multimodal Learning from Data-centric Perspective","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11530","snapshot_observed_at":"2026-08-06T13:22:45.165347Z","title":"arXiv preprint arXiv:2402.11530 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.165347Z"},"links":{"cited_paper":"/paper/2402.11530","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:20f4a34f4329248635518b5d814e0f95c663f442f74fc16defc8745904521bc3","observation_id":"e6e16ed1-ea5d-45d0-a58b-b8b29de3acce","resolution":{"observed_at":"2026-08-06T13:22:45.165347Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-08-14T22:57:08.956233Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-06T13:22:45.229918Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.229918Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:fbe7df70896c137201f84904564cc0582f0c37fe6fb925b7ff9618f2c8ec3b35","observation_id":"ef1e9ca0-ff73-46ea-91bc-b8c8248b5a39","resolution":{"observed_at":"2026-08-06T13:22:45.229918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.09751","last_updated":"2020-02-14T21:56:30Z","snapshot_observed_at":"2026-07-06T07:47:32.745963Z","submitted_at":"2019-04-22T07:17:18Z","title":"The Curious Case of Neural Text Degeneration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.09751","snapshot_observed_at":"2026-08-06T13:22:45.286552Z","title":"arXiv preprint arXiv:1904.09751 (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.286552Z"},"links":{"cited_paper":"/paper/1904.09751","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:bd5bfcb57079b138149fd1cabbae4c023abdd21802ce89318a0735e5a19ed8e9","observation_id":"7d23b2a2-47e7-413f-bf93-54a9a47512fe","resolution":{"observed_at":"2026-08-06T13:22:45.286552Z","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":"2026-08-06T13:22:51.229015Z","title":null,"venue":null,"work_id":"a46c5d0d-f057-47d6-92a8-fce5ad558fe6","year":2022},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.323392Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:6bfdb300f720147d52c5c7d61b565918c8bff54844a177b8c560d889cad3b821","observation_id":"a63f0888-6f7e-4bd3-9843-6e4532d24289","resolution":{"observed_at":"2026-08-06T13:22:51.294215Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-11T08:20:29.798517Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-06T13:22:45.474761Z","title":"arXiv preprint arXiv:2106.09685 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.474761Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:d357ef080c4af1164f1167e5540cb7c96bc69a221328c94d1e0e762408e681ba","observation_id":"aa152e63-2d7a-48d5-94bd-6ee8740a0077","resolution":{"observed_at":"2026-08-06T13:22:45.474761Z","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":"2026-08-06T13:22:45.511186Z","title":"In: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.511186Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:978e307b12b565f25937efde63d9611c89dbed25f22805bb04982fb2c1326bd9","observation_id":"4b02ecb8-5706-4f78-9280-1de82ff192ef","resolution":{"observed_at":"2026-08-06T13:22:45.511186Z","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":"2026-08-06T13:22:51.006514Z","title":"Microsoft Research Blog (2023)","venue":null,"work_id":"36135c2b-9372-410d-bcc0-a283894ba067","year":2023},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.560083Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:4eacfbcda7cdcd2c04ec75c840b982c793ead563364c841ff4e0b48f725d0775","observation_id":"7a41c6bc-6b1f-46ea-abd1-4542610f4d0a","resolution":{"observed_at":"2026-08-06T13:22:51.106896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:22:50.826395Z","title":"Master’s thesis, University of Washington (2024)","venue":null,"work_id":"824ac4ca-6be8-43cd-b8e4-33841be46dfb","year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.621599Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:3bcde5880cee6ba869ec2e8ede50fb5834057eb4f027a4f0f408d65589cd38cb","observation_id":"7945b797-6fe1-48bc-a8cf-aaa36466f759","resolution":{"observed_at":"2026-08-06T13:22:50.902477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.10351","last_updated":"2020-10-16T02:12:46Z","snapshot_observed_at":"2026-08-10T14:27:02.964369Z","submitted_at":"2019-09-23T13:05:35Z","title":"TinyBERT: Distilling BERT for Natural Language Understanding","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.10351","snapshot_observed_at":"2026-08-06T13:22:45.689017Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.689017Z"},"links":{"cited_paper":"/paper/1909.10351","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:e2c984d657bffd2dabb07f27eeb1c336cadd79487430bed7c7244e9048020876","observation_id":"1830bc6a-bdb2-4108-8f5a-0332b13c47ad","resolution":{"observed_at":"2026-08-06T13:22:45.689017Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07865","last_updated":"2024-05-30T13:08:48Z","snapshot_observed_at":"2026-08-13T04:20:22.694273Z","submitted_at":"2024-02-12T18:21:14Z","title":"Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.07865","snapshot_observed_at":"2026-08-06T13:22:45.746187Z","title":"arXiv preprint arXiv:2402.07865 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.746187Z"},"links":{"cited_paper":"/paper/2402.07865","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:3073a9b80a9c50e8d8727f3347541cc6b5476b94a3b954ecdf1a8e937be7a351","observation_id":"97d2059f-e96f-406e-9738-42bc79a9939b","resolution":{"observed_at":"2026-08-06T13:22:45.746187Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.11942","last_updated":"2020-02-09T03:00:18Z","snapshot_observed_at":"2026-07-06T08:24:44.631342Z","submitted_at":"2019-09-26T07:06:13Z","title":"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.11942","snapshot_observed_at":"2026-08-06T13:22:45.812030Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.812030Z"},"links":{"cited_paper":"/paper/1909.11942","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:91651aa3818ee8757c23746821cec4ea3e775a66eced8f8230abc26cef8b8127","observation_id":"9062c940-8f27-491d-b23c-0817595dff84","resolution":{"observed_at":"2026-08-06T13:22:45.812030Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.06307","last_updated":"2020-02-16T18:23:41Z","snapshot_observed_at":"2026-08-14T16:15:27.178209Z","submitted_at":"2019-06-14T17:26:29Z","title":"A Signal Propagation Perspective for Pruning Neural Networks at Initialization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.06307","snapshot_observed_at":"2026-08-06T13:22:45.868130Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.868130Z"},"links":{"cited_paper":"/paper/1906.06307","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:282c0d6ce675bbf25f295f00d6bc0b4b1c1c9ddbdb8c9b910ec5580885863559","observation_id":"adc4c753-1de6-4343-a4ba-4d565c7aadda","resolution":{"observed_at":"2026-08-06T13:22:45.868130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1608.08710","last_updated":"2017-03-10T17:57:56Z","snapshot_observed_at":"2026-08-14T21:42:03.106939Z","submitted_at":"2016-08-31T02:29:59Z","title":"Pruning Filters for Efficient ConvNets","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.08710","snapshot_observed_at":"2026-08-06T13:22:45.937930Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:45.937930Z"},"links":{"cited_paper":"/paper/1608.08710","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:19cfe23c931be6baa7210664902c22bd85cf3192149fbe52acbc49b94730fc38","observation_id":"7b9e497b-40bc-4f95-bab5-1fcd9783b7ea","resolution":{"observed_at":"2026-08-06T13:22:45.937930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10355","last_updated":"2023-10-26T02:52:40Z","snapshot_observed_at":"2026-08-12T18:48:30.326248Z","submitted_at":"2023-05-17T16:34:01Z","title":"Evaluating Object Hallucination in Large Vision-Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10355","snapshot_observed_at":"2026-08-06T13:22:46.010798Z","title":"arXiv preprint arXiv:2305.10355 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.010798Z"},"links":{"cited_paper":"/paper/2305.10355","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:5833dac30994a18492856a8d89a01061bd71086f88a190f48b6bed96f07494a2","observation_id":"bc4072fa-b7b5-49dd-ad2e-37d41680e44c","resolution":{"observed_at":"2026-08-06T13:22:46.010798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.00593","last_updated":"2021-03-17T06:38:05Z","snapshot_observed_at":"2026-07-06T10:10:39.816933Z","submitted_at":"2020-11-01T18:47:51Z","title":"MixKD: Towards Efficient Distillation of Large-scale Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.00593","snapshot_observed_at":"2026-08-06T13:22:46.069815Z","title":"org/abs/2011.00593","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.069815Z"},"links":{"cited_paper":"/paper/2011.00593","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:2bf11afd17cd9574844f7ef23a4c28a825a90894df16f027901c653fbe962879","observation_id":"813551f2-7d42-4ace-995a-2b6eefcd90d3","resolution":{"observed_at":"2026-08-06T13:22:46.069815Z","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":"2026-08-06T13:22:50.657676Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":"95ca5b23-a3d2-41c9-b7bd-1d4e907553f6","year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.130382Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:e37f697262aa4784b8ec4ff2ca3f625c1577d53929d30378f5653ccf99a6110f","observation_id":"b7b0fe10-9d47-40ce-a3c2-e0339c882a9c","resolution":{"observed_at":"2026-08-06T13:22:50.741243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08485","last_updated":"2023-12-11T17:46:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-04-17T17:59:25Z","title":"Visual Instruction Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08485","snapshot_observed_at":"2026-08-06T13:22:46.228942Z","title":"org/abs/2304.08485","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.228942Z"},"links":{"cited_paper":"/paper/2304.08485","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:79544b45b49c1d5463672fc3b84484d6cf282ab4a56017d6536b97130425cad5","observation_id":"aff4ce76-5fdc-43ab-ab30-14bb3b3b6d08","resolution":{"observed_at":"2026-08-06T13:22:46.228942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.00708","last_updated":"2021-08-02T08:21:44Z","snapshot_observed_at":"2026-08-14T19:23:48.495687Z","submitted_at":"2021-08-02T08:21:44Z","title":"Group Fisher Pruning for Practical Network Compression","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.00708","snapshot_observed_at":"2026-08-06T13:22:46.284667Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.284667Z"},"links":{"cited_paper":"/paper/2108.00708","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:e84bfe8df442c950a272c9d8f13d8078e24eea401ccae0f3995105874b121986","observation_id":"e404fb81-d970-468a-afcb-4cac69586276","resolution":{"observed_at":"2026-08-06T13:22:46.284667Z","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":"2026-08-06T13:22:46.381619Z","title":"Advances in Neural Information Processing Systems 35, 2507–2521 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.381619Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:22932f0827ecf2228dfa62b5eaaba31046b02f6f3cfad1d0877d1ac6f2e29363","observation_id":"41f59a31-6279-4d4c-a070-0af503067cef","resolution":{"observed_at":"2026-08-06T13:22:46.381619Z","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":"2026-08-06T13:22:46.487160Z","title":"Advances in neural information processing systems36, 21702–21720 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.487160Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:4a39a80285b9af3f2e13f1c3dd7c427d8f916ab39e8145c6e94021b88a10ee02","observation_id":"0ae59497-55cb-4f04-b6fe-ccc62c01cbca","resolution":{"observed_at":"2026-08-06T13:22:46.487160Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.06360","last_updated":"2021-04-11T18:53:43Z","snapshot_observed_at":"2026-08-13T18:42:55.654852Z","submitted_at":"2019-10-14T18:12:30Z","title":"Structured Pruning of a BERT-based Question Answering Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.06360","snapshot_observed_at":"2026-08-06T13:22:46.547975Z","title":"arXiv preprint arXiv:1910.06360 (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.547975Z"},"links":{"cited_paper":"/paper/1910.06360","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:212b386f10eb5f21fe4ee8ae3462c31a2e72bf2a3831aeeb0b108162c12dff94","observation_id":"49e3e980-9831-4937-9fd4-aa83b45533c7","resolution":{"observed_at":"2026-08-06T13:22:46.547975Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03853","last_updated":"2024-10-11T09:43:32Z","snapshot_observed_at":"2026-08-14T01:18:55.206280Z","submitted_at":"2024-03-06T17:04:18Z","title":"ShortGPT: Layers in Large Language Models are More Redundant Than You Expect","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03853","snapshot_observed_at":"2026-08-06T13:22:46.639736Z","title":"arXiv preprint arXiv:2403.03853 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.639736Z"},"links":{"cited_paper":"/paper/2403.03853","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:cef9001b1758c1a5cae97d9993dbf6af350d38eda2b5f08a73aa3d4b5dbd13a6","observation_id":"dca33805-b5df-47e6-8ef6-65a740e9008f","resolution":{"observed_at":"2026-08-06T13:22:46.639736Z","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":"2026-08-06T13:22:50.440892Z","title":null,"venue":null,"work_id":"8d3fab26-41bc-434a-be2f-75970aac55fc","year":2019},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.725744Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:185be0e1e080c044963fdcf0bee85b6a7c3dc2a728d90227d4e01fd50714af8c","observation_id":"2a2b67f8-9578-49d2-ba24-4212364c4287","resolution":{"observed_at":"2026-08-06T13:22:50.561751Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.04809","last_updated":"2020-02-12T05:38:42Z","snapshot_observed_at":"2026-08-04T08:17:39.967016Z","submitted_at":"2020-02-12T05:38:42Z","title":"Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.04809","snapshot_observed_at":"2026-08-06T13:22:46.791956Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.791956Z"},"links":{"cited_paper":"/paper/2002.04809","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:e1cb7001b37b05105628ce6efa63c1de3a1040c5c4a51758e61477300befac48","observation_id":"b162db12-7f10-4949-8a80-8ccd808f1a45","resolution":{"observed_at":"2026-08-06T13:22:46.791956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16637","last_updated":"2024-04-25T14:24:41Z","snapshot_observed_at":"2026-08-13T21:25:19.764387Z","submitted_at":"2024-04-25T14:24:41Z","title":"Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16637","snapshot_observed_at":"2026-08-06T13:22:46.867030Z","title":"arXiv preprint arXiv:2404.16637 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.867030Z"},"links":{"cited_paper":"/paper/2404.16637","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:a48817c220bb6fb42e4bbd45203cbaf04573e7e3489134c4dd233dfc41b3be4c","observation_id":"80e2e3bf-68dc-4bd1-90e9-bdb2235a8f95","resolution":{"observed_at":"2026-08-06T13:22:46.867030Z","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":"2026-08-06T13:22:46.889339Z","title":"In: International conference on machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.889339Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:e7086fa2a59fb367d61070b8422fbdf8c3d2e0957b6bcdcb77bd0d7d9672da51","observation_id":"7062b5e9-312c-44b8-b478-76755a4a6203","resolution":{"observed_at":"2026-08-06T13:22:46.889339Z","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":"2026-08-06T13:22:46.953068Z","title":"Computer Speech & Language77, 101429 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:46.953068Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:d5e12c55091046cd9d162cae7de295c1f2a451f49028cf82733bf6520fd8ad40","observation_id":"dd142509-ef06-474c-913c-6061df413d8d","resolution":{"observed_at":"2026-08-06T13:22:46.953068Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.01108","last_updated":"2020-03-01T02:57:50Z","snapshot_observed_at":"2026-08-07T19:07:36.327251Z","submitted_at":"2019-10-02T17:56:28Z","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.01108","snapshot_observed_at":"2026-08-06T13:22:47.025758Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:47.025758Z"},"links":{"cited_paper":"/paper/1910.01108","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:91ef7de7836c9fc650d695b023709200ce2efae292ec34b5077ff607ea68cb31","observation_id":"6d7b8da9-6408-42dd-9ddb-2559ec0adbc0","resolution":{"observed_at":"2026-08-06T13:22:47.025758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.07683","last_updated":"2020-10-23T16:14:58Z","snapshot_observed_at":"2026-08-11T00:50:04.275195Z","submitted_at":"2020-05-15T17:54:15Z","title":"Movement Pruning: Adaptive Sparsity by Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.07683","snapshot_observed_at":"2026-08-06T13:22:47.097104Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:47.097104Z"},"links":{"cited_paper":"/paper/2005.07683","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:24c13e9dc6bcc3c5638a0c1bdc53b63e3b19d22fe3448e5d2d2a466cf62057bf","observation_id":"15bff839-5809-4602-a976-2975f142d3a1","resolution":{"observed_at":"2026-08-06T13:22:47.097104Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.09355","last_updated":"2019-08-25T16:13:24Z","snapshot_observed_at":"2026-08-14T21:37:39.248967Z","submitted_at":"2019-08-25T16:13:24Z","title":"Patient Knowledge Distillation for BERT Model Compression","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.09355","snapshot_observed_at":"2026-08-06T13:22:47.217308Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:47.217308Z"},"links":{"cited_paper":"/paper/1908.09355","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:29d843b6389d8b6ee5bc51c270f8c05287b46415556afa424226f3d202b5f268","observation_id":"427cc078-087e-4d6b-a1d0-186875127e40","resolution":{"observed_at":"2026-08-06T13:22:47.217308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-06T13:22:47.333939Z","title":"arXiv preprint arXiv:2302.13971 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:47.333939Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:cf4a89cf766d74e359cab8c48e2b71a05a3800f75b42f500418a66e158b74eeb","observation_id":"8888c43e-686e-49a3-bf51-2325d029d1f9","resolution":{"observed_at":"2026-08-06T13:22:47.333939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.09418","last_updated":"2019-06-07T14:00:58Z","snapshot_observed_at":"2026-08-14T16:28:06.194757Z","submitted_at":"2019-05-23T01:13:24Z","title":"Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.09418","snapshot_observed_at":"2026-08-06T13:22:47.423140Z","title":"arXiv preprint arXiv:1905.09418 (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:47.423140Z"},"links":{"cited_paper":"/paper/1905.09418","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:ab526593b421a3fc6e5d7655239bf60edbd5e47de8acf89343b8a2ab8a4de718","observation_id":"a3f4a32f-4f2d-4e7a-afd8-1b776e1b54f7","resolution":{"observed_at":"2026-08-06T13:22:47.423140Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.10957","last_updated":"2020-04-06T02:53:18Z","snapshot_observed_at":"2026-08-14T23:23:42.318384Z","submitted_at":"2020-02-25T15:21:10Z","title":"MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.10957","snapshot_observed_at":"2026-08-06T13:22:47.515023Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:47.515023Z"},"links":{"cited_paper":"/paper/2002.10957","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:3308acefd432f3c66e3ec4c279291dd639948235a8f20468e70f4866a9a1c97d","observation_id":"899eaffc-bd30-49da-9ac8-13f86b13e5e4","resolution":{"observed_at":"2026-08-06T13:22:47.515023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06694","last_updated":"2024-04-11T01:18:06Z","snapshot_observed_at":"2026-08-13T05:52:56.563579Z","submitted_at":"2023-10-10T15:13:30Z","title":"Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06694","snapshot_observed_at":"2026-08-06T13:22:47.582094Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:47.582094Z"},"links":{"cited_paper":"/paper/2310.06694","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:f397e27950c41949048287f2cc939502d776bc820404a5390a028d1ce973f66f","observation_id":"0d6e61e8-845d-43aa-9d8f-5c0e98c4af06","resolution":{"observed_at":"2026-08-06T13:22:47.582094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13116","last_updated":"2024-10-21T16:22:33Z","snapshot_observed_at":"2026-08-10T17:26:33.432994Z","submitted_at":"2024-02-20T16:17:37Z","title":"A Survey on Knowledge Distillation of Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13116","snapshot_observed_at":"2026-08-06T13:22:47.656391Z","title":"org/abs/2402.13116 Pruning and Recovery Techniques for Compressing MLLMs 17","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:47.656391Z"},"links":{"cited_paper":"/paper/2402.13116","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:4dcb5c8481e623cded1e06e778455d12c532a41a367895c79fb94f60bdc73e70","observation_id":"e3447fd5-6547-4b55-a2c3-f0b007303cf5","resolution":{"observed_at":"2026-08-06T13:22:47.656391Z","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":"2026-08-06T13:22:50.207227Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":"deef9125-5f28-4eff-97e9-46132e4d5586","year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:47.717825Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:db4feb2b7403237de5eeae047720ca0c96bdf9a7a170ac12396682ba677c9b3f","observation_id":"b44a2ab7-f7f3-46ea-bb8c-342c9ae1d263","resolution":{"observed_at":"2026-08-06T13:22:50.309347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01861","last_updated":"2022-06-04T00:28:21Z","snapshot_observed_at":"2026-08-14T22:20:42.170453Z","submitted_at":"2022-06-04T00:28:21Z","title":"ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.01861","snapshot_observed_at":"2026-08-06T13:22:47.848505Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:47.848505Z"},"links":{"cited_paper":"/paper/2206.01861","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:0cad2fc17ab0d3c339e1d96d5ce34cc30557c1daa28211ea085e1b2f485c9e25","observation_id":"934d023b-00d6-45e5-a64e-f1d9d38e76e3","resolution":{"observed_at":"2026-08-06T13:22:47.848505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.13549","last_updated":"2024-11-29T15:51:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-23T15:21:52Z","title":"A Survey on Multimodal Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.13549","snapshot_observed_at":"2026-08-06T13:22:47.959885Z","title":"arXiv preprint arXiv:2306.13549 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:47.959885Z"},"links":{"cited_paper":"/paper/2306.13549","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:3c40579e1dddc23d1484e83a00920436a5ae3d7ea8afca80fdd9dc6d51f09ee7","observation_id":"3d57b322-8fa6-4449-aee6-d7525aa7edd1","resolution":{"observed_at":"2026-08-06T13:22:47.959885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.08174","last_updated":"2019-09-18T02:28:56Z","snapshot_observed_at":"2026-08-10T00:28:51.669833Z","submitted_at":"2019-09-18T02:28:56Z","title":"Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.08174","snapshot_observed_at":"2026-08-06T13:22:48.083495Z","title":"org/abs/1909.08174","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:48.083495Z"},"links":{"cited_paper":"/paper/1909.08174","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:8783bfc7a034e015881a02d35fcd92aa2ee95c4617b03176521a1e338d0753b0","observation_id":"4fcd6331-8720-4d8d-9447-6aee4685d197","resolution":{"observed_at":"2026-08-06T13:22:48.083495Z","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":"2026-08-06T13:22:50.023099Z","title":"In: Proceedings of CVPR (2024)","venue":null,"work_id":"070b1702-20aa-4bd2-8e6d-8bafa7c3d0bb","year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:48.198110Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:19716e74df05a4097b6253308c8b375d01003571e7c0635a226ccaa453f256c9","observation_id":"39b8d7ee-30b3-49b8-b505-406f390d2741","resolution":{"observed_at":"2026-08-06T13:22:50.120328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:22:48.316422Z","title":"In: 2019 Fifth Workshop on Energy Efficient Machine Learn- ing and Cognitive Computing - NeurIPS Edition (EMC2-NIPS)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:48.316422Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:bee6f5753b866f69cdd404321c5fcf4c835d2eccb8e507aca7e954d1f624062a","observation_id":"72696593-63a9-4078-9549-9df1553f1be8","resolution":{"observed_at":"2026-08-06T13:22:48.316422Z","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":"2026-08-06T13:22:49.892667Z","title":"In: Proceedings of the IEEE/CVF International Conference on Computer Vision","venue":null,"work_id":"67cf7c5e-52c6-446a-aa8f-c431bd12f733","year":2023},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:48.404334Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:faccb0304d434278b48ad8834581624389755d6a97b553974bb3264e910de980","observation_id":"1c8c4468-5aff-456f-869d-c32b64457647","resolution":{"observed_at":"2026-08-06T13:22:49.965938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.06199","last_updated":"2024-03-25T05:36:56Z","snapshot_observed_at":"2026-08-13T00:58:12.511635Z","submitted_at":"2024-03-10T12:43:27Z","title":"Mipha: A Comprehensive Overhaul of Multimodal Assistant with Small Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.06199","snapshot_observed_at":"2026-08-06T13:22:48.525847Z","title":"arXiv preprint arXiv:2403.06199 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:48.525847Z"},"links":{"cited_paper":"/paper/2403.06199","citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:5920a0d9716d6f7d9ffcd6a91d2fafd6361e453979e0cb3ffa8124cdabbc1413","observation_id":"80e71a35-495e-4fb4-979e-48df0d34a593","resolution":{"observed_at":"2026-08-06T13:22:48.525847Z","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":"2026-08-06T13:22:49.699411Z","title":"In: Proceedings of the 1st International Workshop on Efficient Multimedia Computing under Limited","venue":null,"work_id":"47ff647a-4d2c-4a3e-9821-59b14a36845c","year":2024},"citing_paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T13:22:48.676225Z"},"links":{"citing_paper":"/paper/2507.20749"},"observation_digest":"sha256:1d5c1a96f7c24a0f8c99545eb8d7d8f6c6d1d11d3ebbae6f48283f056453d202","observation_id":"cefbfc6f-6959-4fda-a4a6-fbf15b377446","resolution":{"observed_at":"2026-08-06T13:22:49.766910Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.20749","last_updated":"2025-07-28T11:57:52Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-08T16:13:24.324121Z","submitted_at":"2025-07-28T11:57:52Z","title":"Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study"},"reference_resolution":{"displayed":61,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":52,"verified_exact":0,"verified_fuzzy":8},"total_outbound_references":61},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2507.20749."}