{"as_of":"2026-08-09T23:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:06e6f1e68dbd309353a39398d1c656b1adde15df4c066234812c8d147cc1f13d","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T14:48:53.740780Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":4,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.18356","last_updated":"2023-10-31T04:21:33Z","snapshot_observed_at":"2026-07-06T16:39:40.673808Z","submitted_at":"2023-10-24T00:47:26Z","title":"LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery","version":2},"cited_work":{"arxiv_id":"2310.18356","doi":"10.48550/arxiv.2310.18356","metadata_source":"arxiv_reference","pith_arxiv_id":"2310.18356","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Lorashear: Efficient large language model structured pruning and knowledge recovery","venue":"arXiv (Cornell University)","work_id":"64d47465-b47b-4276-8968-6ff6e3805b77","year":2023},"citing_paper":{"arxiv_id":"2404.14294","last_updated":"2024-07-19T04:47:36Z","snapshot_observed_at":"2026-07-06T18:03:47.096406Z","submitted_at":"2024-04-22T15:53:08Z","title":"A Survey on Efficient Inference for Large Language Models","version":3},"reference_index":178,"source":"pdf_text","source_observed_at":"2026-05-15T02:39:33.007894Z"},"links":{"cited_paper":"/paper/2310.18356","citing_paper":"/paper/2404.14294"},"observation_digest":"sha256:667e8390e3fd39195ecc0283328fa875a1e4bd4c5e74b2d6bb5cae903b7ac7cb","observation_id":"2f74f622-dc6a-4bd4-a5c3-94f03261351b","resolution":{"observed_at":"2026-05-15T02:39:33.151162Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.18356","last_updated":"2023-10-31T04:21:33Z","snapshot_observed_at":"2026-07-06T16:39:40.673808Z","submitted_at":"2023-10-24T00:47:26Z","title":"LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.18356","snapshot_observed_at":"2026-08-08T14:48:53.740780Z","title":"Lorashear: Efficient large language model struc- tured pruning and knowledge recovery","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06663","last_updated":"2025-02-11T19:01:39Z","snapshot_observed_at":"2026-08-08T14:42:01.589221Z","submitted_at":"2025-02-10T16:51:03Z","title":"EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-08T14:48:53.740780Z"},"links":{"cited_paper":"/paper/2310.18356","citing_paper":"/paper/2502.06663"},"observation_digest":"sha256:cd2aa7815669ecb46ef1bce601869526a14506522880d1069beb32084164ec4e","observation_id":"6e0ee9c7-a947-4b34-864f-107dcb7c7a2c","resolution":{"observed_at":"2026-08-08T14:48:53.740780Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.18356","last_updated":"2023-10-31T04:21:33Z","snapshot_observed_at":"2026-07-06T16:39:40.673808Z","submitted_at":"2023-10-24T00:47:26Z","title":"LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery","version":2},"cited_work":{"arxiv_id":"2310.18356","doi":"10.48550/arxiv.2310.18356","metadata_source":"arxiv_reference","pith_arxiv_id":"2310.18356","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Lorashear: Efficient large language model structured pruning and knowledge recovery","venue":"arXiv (Cornell University)","work_id":"64d47465-b47b-4276-8968-6ff6e3805b77","year":2023},"citing_paper":{"arxiv_id":"2505.17138","last_updated":"2026-05-18T17:05:12Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-22T06:12:42Z","title":"RAP: Runtime Adaptive Pruning for LLM Inference","version":5},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-22T13:20:41.739571Z"},"links":{"cited_paper":"/paper/2310.18356","citing_paper":"/paper/2505.17138"},"observation_digest":"sha256:b88b069a269e03ef2d03f40469fdadd93ad4d59821ad1af35b47ef0d1b32c25b","observation_id":"ee6d34c4-c16c-4442-8c40-a88a941f5b71","resolution":{"observed_at":"2026-05-22T13:21:35.492380Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.18356","last_updated":"2023-10-31T04:21:33Z","snapshot_observed_at":"2026-07-06T16:39:40.673808Z","submitted_at":"2023-10-24T00:47:26Z","title":"LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.18356","snapshot_observed_at":"2026-08-07T13:30:18.984228Z","title":"Lorashear: Efficient large language model struc- tured pruning and knowledge recovery","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.22689","last_updated":"2025-05-28T03:01:28Z","snapshot_observed_at":"2026-08-07T23:10:30.178835Z","submitted_at":"2025-05-28T03:01:28Z","title":"SlimLLM: Accurate Structured Pruning for Large Language Models","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T13:30:18.984228Z"},"links":{"cited_paper":"/paper/2310.18356","citing_paper":"/paper/2505.22689"},"observation_digest":"sha256:bd97ea3a6f6d5303a0b016a5e0a31207d718aadbf61cbf45a508df1dc2266c9d","observation_id":"a7d2da69-6a0a-4fbf-b499-16bc3ad5ee34","resolution":{"observed_at":"2026-08-07T13:30:18.984228Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.18356","last_updated":"2023-10-31T04:21:33Z","snapshot_observed_at":"2026-07-06T16:39:40.673808Z","submitted_at":"2023-10-24T00:47:26Z","title":"LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.18356","snapshot_observed_at":"2026-08-07T00:56:49.788968Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.12384","last_updated":"2025-06-14T07:42:39Z","snapshot_observed_at":"2026-08-07T08:42:05.698575Z","submitted_at":"2025-06-14T07:42:39Z","title":"Model Merging for Knowledge Editing","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T00:56:49.788968Z"},"links":{"cited_paper":"/paper/2310.18356","citing_paper":"/paper/2506.12384"},"observation_digest":"sha256:5a9eff7759ead7df54223121a1410f3c84bfa854d7e6f212720f06cf35e2d8ad","observation_id":"a45c6e24-129e-4b22-a9bc-63c5ee47b8bc","resolution":{"observed_at":"2026-08-07T00:56:49.788968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.18356","last_updated":"2023-10-31T04:21:33Z","snapshot_observed_at":"2026-07-06T16:39:40.673808Z","submitted_at":"2023-10-24T00:47:26Z","title":"LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery","version":2},"cited_work":{"arxiv_id":"2310.18356","doi":"10.48550/arxiv.2310.18356","metadata_source":"arxiv_reference","pith_arxiv_id":"2310.18356","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Lorashear: Efficient large language model structured pruning and knowledge recovery","venue":"arXiv (Cornell University)","work_id":"64d47465-b47b-4276-8968-6ff6e3805b77","year":2023},"citing_paper":{"arxiv_id":"2506.12876","last_updated":"2026-05-13T04:56:46Z","snapshot_observed_at":"2026-08-01T16:23:25.586402Z","submitted_at":"2025-06-15T15:02:59Z","title":"MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-19T09:01:16.991413Z"},"links":{"cited_paper":"/paper/2310.18356","citing_paper":"/paper/2506.12876"},"observation_digest":"sha256:7204406da0e0f338d7008971617a8be6a06ccd2398987e156f23cf1d525e6806","observation_id":"e9d74994-b7e5-449e-a920-06b4435db414","resolution":{"observed_at":"2026-05-19T09:02:14.482082Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.18356","last_updated":"2023-10-31T04:21:33Z","snapshot_observed_at":"2026-07-06T16:39:40.673808Z","submitted_at":"2023-10-24T00:47:26Z","title":"LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.18356","snapshot_observed_at":"2026-08-04T13:30:57.879086Z","title":"Lorashear: Efficient large language model structured pruning and knowledge recovery.arXiv preprint arXiv:2310.18356, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.00192","last_updated":"2026-07-28T23:06:32Z","snapshot_observed_at":"2026-08-09T14:41:56.717825Z","submitted_at":"2025-09-30T19:10:35Z","title":"Train Large, Deploy Compact: Structured Compression for Compact Low-Rank Adaptation","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T13:30:57.879086Z"},"links":{"cited_paper":"/paper/2310.18356","citing_paper":"/paper/2510.00192"},"observation_digest":"sha256:453ea604ffdea52b1d9eaac01b9a5cf4115bf0a51a75cf8b49b0e50c7b82b76b","observation_id":"7b10f8df-b062-44a4-b9db-5e73b59e7c13","resolution":{"observed_at":"2026-08-04T13:30:57.879086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.18356","last_updated":"2023-10-31T04:21:33Z","snapshot_observed_at":"2026-07-06T16:39:40.673808Z","submitted_at":"2023-10-24T00:47:26Z","title":"LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery","version":2},"cited_work":{"arxiv_id":"2310.18356","doi":"10.48550/arxiv.2310.18356","metadata_source":"arxiv_reference","pith_arxiv_id":"2310.18356","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Lorashear: Efficient large language model structured pruning and knowledge recovery","venue":"arXiv (Cornell University)","work_id":"64d47465-b47b-4276-8968-6ff6e3805b77","year":2023},"citing_paper":{"arxiv_id":"2606.26538","last_updated":"2026-06-25T02:25:00Z","snapshot_observed_at":"2026-07-07T00:00:51.779266Z","submitted_at":"2026-06-25T02:25:00Z","title":"CascadeFormer: Depth-Tapered Transformers Motivated by Gradient Fan-in Asymmetry","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-06-26T05:22:26.818078Z"},"links":{"cited_paper":"/paper/2310.18356","citing_paper":"/paper/2606.26538"},"observation_digest":"sha256:d5b60131e57bf0721906dabbd1dfe48c3a866b8f966999640a7e1260826f8d0d","observation_id":"6a85f140-b305-493f-adfd-c4c1689af75b","resolution":{"observed_at":"2026-06-26T05:29:00.079790Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2310.18356/citation-record","integrity":"/paper/2310.18356/integrity","json":"/paper/2310.18356/citation-record.json","paper":"/paper/2310.18356"},"outbound":[],"paper":{"arxiv_id":"2310.18356","last_updated":"2023-10-31T04:21:33Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T16:39:40.673808Z","submitted_at":"2023-10-24T00:47:26Z","title":"LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2310.18356."}