{"as_of":"2026-08-15T12:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fce424ce43ddc68a4f5702a16a772b4b3f6a12f186e77af5634fd4e104e3784d","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T17:26:45.574894Z","state":"measured"},{"denominator":57,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":57,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T11:31:25.851340Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T01:46:26.859702Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"cited_work":{"arxiv_id":"2502.00899","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.00899","snapshot_observed_at":"2026-07-02T01:46:26.859702Z","title":"Hassle-free: A unified framework for sparse plus low-rank matrix decomposition for llms.arXiv preprint arXiv:2502.00899","venue":null,"work_id":"5c9cf0a6-761b-4cf3-8de9-a3e8446339be","year":2025},"citing_paper":{"arxiv_id":"2605.03667","last_updated":"2026-05-05T12:04:51Z","snapshot_observed_at":"2026-08-11T12:28:38.015047Z","submitted_at":"2026-05-05T12:04:51Z","title":"ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-07T17:07:18.278784Z"},"links":{"cited_paper":"/paper/2502.00899","citing_paper":"/paper/2605.03667"},"observation_digest":"sha256:394f5a2a57300ad9f3bdf098ca0b8748a3b9af25319fdb89ea9e615299c72bd4","observation_id":"9cdb1949-aa79-4e33-abc6-f56763c1fa82","resolution":{"observed_at":"2026-05-11T23:26:12.815998Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"cited_work":{"arxiv_id":"2502.00899","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.00899","snapshot_observed_at":"2026-07-02T01:46:26.859702Z","title":"Hassle-free: A unified framework for sparse plus low-rank matrix decomposition for llms.arXiv preprint arXiv:2502.00899","venue":null,"work_id":"5c9cf0a6-761b-4cf3-8de9-a3e8446339be","year":2025},"citing_paper":{"arxiv_id":"2606.03465","last_updated":"2026-06-02T10:45:21Z","snapshot_observed_at":"2026-08-12T23:20:29.892505Z","submitted_at":"2026-06-02T10:45:21Z","title":"Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-28T11:31:25.851340Z"},"links":{"cited_paper":"/paper/2502.00899","citing_paper":"/paper/2606.03465"},"observation_digest":"sha256:492c81afeb84de0ca4f9c5d83cb96f9f2eadea5749bcb2e772e890861c3eeecf","observation_id":"5abc7b6e-40e9-4834-af70-f56c5fda748b","resolution":{"observed_at":"2026-07-02T01:46:26.861562Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.00899/citation-record","integrity":"/paper/2502.00899/integrity","json":"/paper/2502.00899/citation-record.json","paper":"/paper/2502.00899"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.12712","last_updated":"2023-04-13T20:41:31Z","snapshot_observed_at":"2026-08-03T04:49:15.195814Z","submitted_at":"2023-03-22T16:51:28Z","title":"Sparks of Artificial General Intelligence: Early experiments with GPT-4","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.12712","snapshot_observed_at":"2026-08-09T17:26:45.269580Z","title":"Sparks of artificial general intelligence: Early experiments with gpt-4.arXiv preprint arXiv:2303.12712, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.269580Z"},"links":{"cited_paper":"/paper/2303.12712","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:b8d407106959480f2243d6a3ff1df8a2b58de8a2d79bee41590d08d37e6e6e08","observation_id":"f1439142-1051-4e61-ba21-f77016bd0ae6","resolution":{"observed_at":"2026-08-09T17:26:45.269580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-09T17:26:45.276912Z","title":"Gpt-4 technical report.arXiv preprint arXiv:2303.08774, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.276912Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:3f5d70614fa48c4185029f0c819fb4b3b1145675ba82008ea1eead6045ecd04a","observation_id":"e099c6f5-8877-4b7d-ba62-4882d492ac61","resolution":{"observed_at":"2026-08-09T17:26:45.276912Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-09T17:26:45.286036Z","title":"Gemini: a family of highly capable multimodal models.arXiv preprint arXiv:2312.11805, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.286036Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:ee06474a1b6d934cdf5ca159e428254c560eff083d809089fe9495b3e7cd9d8d","observation_id":"9ca15712-873b-4c62-8e57-3b109eba97cd","resolution":{"observed_at":"2026-08-09T17:26:45.286036Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-09T17:26:45.291926Z","title":"The llama 3 herd of models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.291926Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:a2108191c79bcead99b9fcf81776e2beba8093972df598f159c5c3b8ea06fbda","observation_id":"9c00a883-6086-48df-af66-d11440325053","resolution":{"observed_at":"2026-08-09T17:26:45.291926Z","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":"2026-08-09T17:26:46.930647Z","title":"Optimalbraindamage","venue":null,"work_id":"9cae1d15-f48d-4e2b-a513-598f6e248aa9","year":1989},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.299473Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:908ffa0d65c1c7261d184090a39d1a5ad604b7cfe600811c27435845382e927d","observation_id":"70c8129f-58ee-4186-8f30-dc1785104ec4","resolution":{"observed_at":"2026-08-09T17:26:46.937761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:45.305454Z","title":"Second order derivatives for network pruning: Optimal brain surgeon","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.305454Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:7ad80b837e3a2bbf789b22c21ab21625a92df8b231e98373425c835dd2e3e71c","observation_id":"323dcebf-9bec-4c47-ace1-f437af4adef9","resolution":{"observed_at":"2026-08-09T17:26:45.305454Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.14623","last_updated":"2023-02-28T15:03:18Z","snapshot_observed_at":"2026-08-13T12:35:13.958742Z","submitted_at":"2023-02-28T15:03:18Z","title":"Fast as CHITA: Neural Network Pruning with Combinatorial Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.14623","snapshot_observed_at":"2026-08-09T17:26:45.311804Z","title":"Fast as chita: Neural network pruning with combinatorial optimization","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.311804Z"},"links":{"cited_paper":"/paper/2302.14623","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:755f007a059c546270e8ab1c3c134f376eed2254611ae2ce968f8e1fc7db0848","observation_id":"15e43a56-2896-4d00-bea4-3c881ecc3b68","resolution":{"observed_at":"2026-08-09T17:26:45.311804Z","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":"2026-08-09T17:26:46.902240Z","title":"Fast convnets using group-wise brain damage","venue":null,"work_id":"c9fb22e6-781b-4972-8bc9-88c1b84a3ea8","year":2016},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.319495Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:273cb0ef2903e1828ffcc156644eacf934beb18c17be4f039195fac657810a52","observation_id":"73a3de7b-f005-4b0e-bc2e-f1c800d8a543","resolution":{"observed_at":"2026-08-09T17:26:46.907768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:45.327737Z","title":"Learning structured sparsity in deep neural networks.Advances in neural information processing systems, 29, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.327737Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:0484a4e44aa8c429588d6cb30b779928bd1d427f9e0e8004ebda09d1b5291cae","observation_id":"4b003056-7afd-456e-aa83-acb52c30bb98","resolution":{"observed_at":"2026-08-09T17:26:45.327737Z","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":"2026-08-09T17:26:45.333519Z","title":"Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned.arXiv preprint arXiv:1905.09418, 2019","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.333519Z"},"links":{"cited_paper":"/paper/1905.09418","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:e38842ad3e92665578d07fe50573eaaaa441965bb4d16faaacc07c8c5ac17747","observation_id":"5dc1e382-f00a-4c4a-ab89-6cbfd2520ae9","resolution":{"observed_at":"2026-08-09T17:26:45.333519Z","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":"2026-08-09T17:26:46.872888Z","title":"Data-efficient structured pruning viasubmodularoptimization","venue":null,"work_id":"b4bed4cc-1c25-4cfd-9628-9f2c681c150d","year":2022},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.340652Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:dc69819878b2a3881c184373cfae0ac81d5ea4af079cd696fc8cd3c4d2cfc931","observation_id":"070e3a14-6475-407e-be07-41bf0fc81c24","resolution":{"observed_at":"2026-08-09T17:26:46.878366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04010","last_updated":"2021-04-18T10:18:00Z","snapshot_observed_at":"2026-08-14T18:17:15.787177Z","submitted_at":"2021-02-08T05:55:47Z","title":"Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04010","snapshot_observed_at":"2026-08-09T17:26:45.347753Z","title":"Learning n: m fine-grained structured sparse neural networks from scratch","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.347753Z"},"links":{"cited_paper":"/paper/2102.04010","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:29521b7caf32aa04744123b861e1a478319fd3e9abf307c82fa474976a75639d","observation_id":"7713b949-1a52-4986-a618-818996e4d07a","resolution":{"observed_at":"2026-08-09T17:26:45.347753Z","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":"2026-08-09T17:26:45.353766Z","title":"Learning both weights and connections for efficient neural network.Advances in neural information processing systems, 28, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.353766Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:2c382d5bca9f9e0681e840fea3aadc2697c116437df36fcf103eace23ae73235","observation_id":"b73da1c0-1a38-47a8-953f-4dd0bc6896cd","resolution":{"observed_at":"2026-08-09T17:26:45.353766Z","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":"2026-08-09T17:26:45.358495Z","title":"Dynamic network surgery for efficient dnns","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.358495Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:153b9877214dc6585f07fe27b76fa7b3b16b8a79b22ab476004ebf3be5ec018e","observation_id":"f29c5dd0-8e6f-459e-b947-27a7f757362d","resolution":{"observed_at":"2026-08-09T17:26:45.358495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.07259","last_updated":"2022-10-17T23:24:22Z","snapshot_observed_at":"2026-08-14T22:25:28.981586Z","submitted_at":"2022-03-14T16:40:31Z","title":"The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.07259","snapshot_observed_at":"2026-08-09T17:26:45.362597Z","title":"The optimal bert surgeon: Scalable and accurate second-order pruning for large language models, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.362597Z"},"links":{"cited_paper":"/paper/2203.07259","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:81e86ba14f80d326965cd7cc67386255666bae5d8abe098b5829a25deece4691","observation_id":"a46df798-23c3-4dbe-a153-7dfaf4fb688a","resolution":{"observed_at":"2026-08-09T17:26:45.362597Z","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":"2026-08-09T17:26:46.826444Z","title":"Inducingandexploiting activation sparsity for fast inference on deep neural networks","venue":null,"work_id":"9a31d953-b010-4b67-9f87-31426209794f","year":2020},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.367291Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:25f8f964c4854c4e2f3389e7fcbd2b783d0171da739c76689c5f2997d47ede51","observation_id":"512b8b2f-354e-4c26-a8fa-a2515c64d997","resolution":{"observed_at":"2026-08-09T17:26:46.832348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.13445","last_updated":"2022-04-21T12:19:20Z","snapshot_observed_at":"2026-08-13T01:16:11.169797Z","submitted_at":"2021-11-26T11:58:51Z","title":"How Well Do Sparse Imagenet Models Transfer?","version":5},"cited_work":{"arxiv_id":"2111.13445","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.13445","snapshot_observed_at":"2026-08-09T17:26:46.277629Z","title":"How Well Do Sparse Imagenet Models Transfer?","venue":"cs.CV","work_id":"cdb3a5e8-ea41-4a38-9339-04ea41e1c250","year":2021},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.371313Z"},"links":{"cited_paper":"/paper/2111.13445","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:f3320fdab7f191e41b7acf38d32d05da9967627a0197beca2ac2504af06484cf","observation_id":"27b8152c-4a77-45e3-b045-0bb3b45f1987","resolution":{"observed_at":"2026-08-09T17:26:46.283061Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07831","last_updated":"2025-09-08T14:34:07Z","snapshot_observed_at":"2026-08-12T23:44:59.570987Z","submitted_at":"2024-06-12T02:57:41Z","title":"ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07831","snapshot_observed_at":"2026-08-09T17:26:45.375949Z","title":"Alps: Improved op- timization for highly sparse one-shot pruning for large language models.arXiv preprint arXiv:2406.07831, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.375949Z"},"links":{"cited_paper":"/paper/2406.07831","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:c61bc66c6cf6b470086acfc2773e165ee2f303af2f3310035fac1e62cbf33ba8","observation_id":"42e2224b-2cea-473e-a876-be2a81e5587a","resolution":{"observed_at":"2026-08-09T17:26:45.375949Z","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":"2026-08-09T17:26:45.381428Z","title":"Sparsegpt: Massive language models can be accurately pruned in one-shot","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.381428Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:25eb8cf9e035e473f4905b8f93fea7471a1b880b874f49195bfd99b7d07647cb","observation_id":"cc1b31df-e2ee-4a88-8638-f35eb0bb1323","resolution":{"observed_at":"2026-08-09T17:26:45.381428Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11695","last_updated":"2024-05-06T17:47:01Z","snapshot_observed_at":"2026-08-13T04:32:33.562415Z","submitted_at":"2023-06-20T17:18:20Z","title":"A Simple and Effective Pruning Approach for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11695","snapshot_observed_at":"2026-08-09T17:26:45.387344Z","title":"Asimpleandeffectivepruningapproach for large language models.arXiv preprint arXiv:2306.11695, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.387344Z"},"links":{"cited_paper":"/paper/2306.11695","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:2d9251cff387ef6c22436c1a348c4befe2050f53e1726ed8786a5db546073cc5","observation_id":"5e30a072-ef1a-403f-9ee3-e8db08a86d02","resolution":{"observed_at":"2026-08-09T17:26:45.387344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08915","last_updated":"2024-02-26T02:51:30Z","snapshot_observed_at":"2026-08-13T05:50:03.822817Z","submitted_at":"2023-10-13T07:38:52Z","title":"Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08915","snapshot_observed_at":"2026-08-09T17:26:45.392303Z","title":"Dynamic sparse no training: Training-free fine-tuning for sparse llms","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.392303Z"},"links":{"cited_paper":"/paper/2310.08915","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:282c26623b284d22aee4428bd6a9660d647b137c95d20870bd0d588c6070b302","observation_id":"2bb0768b-f1d0-41c6-ae2b-0377f58ecf50","resolution":{"observed_at":"2026-08-09T17:26:45.392303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.01068","last_updated":"2022-06-21T17:04:40Z","snapshot_observed_at":"2026-08-06T03:13:37.403059Z","submitted_at":"2022-05-02T17:49:50Z","title":"OPT: Open Pre-trained Transformer Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.01068","snapshot_observed_at":"2026-08-09T17:26:45.397279Z","title":"Opt: Open pre-trained transformer language models.arXiv preprint arXiv:2205.01068, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.397279Z"},"links":{"cited_paper":"/paper/2205.01068","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:ebb34949b0a32ba4e835c1528ae2a3cd25294a1961a100c5ba92ae227d31cfe6","observation_id":"ba707875-c073-40e2-aaac-dc89602d1248","resolution":{"observed_at":"2026-08-09T17:26:45.397279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-09T17:26:45.402602Z","title":"Adam: Amethodforstochasticoptimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.402602Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:28456e9f218a24f086411cd66b2a2f827e9c0d937350db3c340153c519d95978","observation_id":"ac0b0cdf-f477-4b97-995a-1ad7497af0d7","resolution":{"observed_at":"2026-08-09T17:26:45.402602Z","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":"2026-08-09T17:26:46.791193Z","title":"Robust principal component pursuit via inexact alternating minimization on matrix manifolds.Journal of Mathematical Imaging and Vision, 51(3):361–377, 2015","venue":null,"work_id":"a3d92ddf-0485-457d-a6db-7e5d8a752b46","year":2015},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.408128Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:419160a3e415593d78ef3f82ee20d487d4d9164674388cd05a721fd030057127","observation_id":"d42646ac-3cc1-470f-bbdd-b7bf07c4cb1c","resolution":{"observed_at":"2026-08-09T17:26:46.797287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:46.770833Z","title":"Robust principal component analysis? Journal of the ACM (JACM), 58(3):1–37, 2011","venue":null,"work_id":"e1b212c6-3516-4aab-8869-b9c93fac213b","year":2011},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.413360Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:4881fb84fb91bd22814a2288c08f5f7ef069a4f30a0c4c73c5e457948d45e016","observation_id":"4abf8e2f-a635-4420-a3c8-0a652f8fd3c2","resolution":{"observed_at":"2026-08-09T17:26:46.777223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:46.753534Z","title":"Linearized alternating direction method with adaptive penalty for low-rank representation.Advances in neural information processing systems, 24, 2011","venue":null,"work_id":"aeb11947-31b2-41ed-b251-49f1a6f452bc","year":2011},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.418338Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:74c4e57987051c99dd0f3106df42d5dcb9e7238518e9c277ad97faedd2b3d079","observation_id":"ac5cbc91-60a2-45ad-9b45-b5922c8b8659","resolution":{"observed_at":"2026-08-09T17:26:46.759956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2009.53948","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T17:26:46.164988Z","title":"Parrilo, and Alan S","venue":null,"work_id":"f43919ed-5195-4482-9b17-33b87186f90c","year":2009},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.423110Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:1d8b53a439cd94a2b8a7e74861ee6a5f4387a30c114ec802f7f6b6bdf5d6e028","observation_id":"280fd5d6-1065-428f-8cd3-f2a111c8cf78","resolution":{"observed_at":"2026-08-09T17:26:46.173716Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:46.734294Z","title":"Godec: Randomized low-rank & sparse matrix decomposition in noisy case","venue":null,"work_id":"3e2d7565-907b-49a9-ba71-c4a2dfe4adec","year":2011},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.428771Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:caeecd01e3d211534798bd3148250a9e194a30b6ca030e56b000fd20ac3abbdd","observation_id":"201c6c66-04f5-4cd7-bde2-a1d533abfe47","resolution":{"observed_at":"2026-08-09T17:26:46.740989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:46.710966Z","title":null,"venue":null,"work_id":"84eaf0f1-5e59-4a2b-a145-684ba5273869","year":2023},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.435206Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:b1e2418b61a7d9e374856bbe53ad5c497296bd271b53565f7d1948ea3e52e6b9","observation_id":"287da164-a8b0-4b9a-8d51-aa2706cbd8af","resolution":{"observed_at":"2026-08-09T17:26:46.719098Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:46.692767Z","title":"Non-convex robust pca","venue":null,"work_id":"13c1047f-3707-4544-a4ce-8fbc8b6b5b38","year":2014},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.441328Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:dad937db5600f63dc40a7cf15fe4569aad37a328d37766f563c8af1ae4304700","observation_id":"3ae5bc5a-6093-478f-8815-4bf347c40c60","resolution":{"observed_at":"2026-08-09T17:26:46.698004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:46.675941Z","title":"On compressing deep models by low rank and sparse decomposition","venue":null,"work_id":"199a911a-8f42-4fac-819b-9395e9446f89","year":2017},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.447970Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:ae427a7fd3829dc7a3cc1eeea59c2f07f2e29552b722ad1b874225a16b74fb7c","observation_id":"eedea305-f1c7-4d65-b2c9-580925935b44","resolution":{"observed_at":"2026-08-09T17:26:46.681679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:46.659016Z","title":"Losparse: Structured compression of large language models based on low-rank and sparse approximation","venue":null,"work_id":"6b817e61-1de1-4dae-b262-283ec46b650a","year":2023},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.453674Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:e3cb0ed7065f3a2308098115a6d09c187b3fb1c0d053b23575f7f1a3b88ade5e","observation_id":"554ab21c-97b7-49fe-80f7-fcef56e26e90","resolution":{"observed_at":"2026-08-09T17:26:46.664764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.13652","last_updated":"2025-05-20T13:06:00Z","snapshot_observed_at":"2026-08-12T22:40:50.337164Z","submitted_at":"2024-09-20T17:02:00Z","title":"OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.13652","snapshot_observed_at":"2026-08-09T17:26:45.459024Z","title":"Oats: Outlier-aware pruning through sparse and low rank decomposition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.459024Z"},"links":{"cited_paper":"/paper/2409.13652","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:48c0b58d875132513a6c5af7726374d8076ed08b896279ae23836f389b65f316","observation_id":"7394dcd8-34d1-4850-b1f0-7e4ff57ec7dd","resolution":{"observed_at":"2026-08-09T17:26:45.459024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16325","last_updated":"2025-01-25T22:24:04Z","snapshot_observed_at":"2026-08-12T23:58:02.046401Z","submitted_at":"2024-05-25T18:43:05Z","title":"SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16325","snapshot_observed_at":"2026-08-09T17:26:45.465953Z","title":"Slope: Double-pruned sparse plus lazy low-rank adapter pretraining of llms.arXiv preprint arXiv:2405.16325, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.465953Z"},"links":{"cited_paper":"/paper/2405.16325","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:b5eb58d2c7175a64ebfe4337f8494e8ad74cef37d8becabbb3bfbae7e5c27401","observation_id":"7657d52f-6d28-451b-b329-5051d37c9f0c","resolution":{"observed_at":"2026-08-09T17:26:45.465953Z","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":"2026-08-09T17:26:46.642215Z","title":"Springer, 2020","venue":null,"work_id":"9ce1dc5a-2c53-4e12-a7af-7f8119cff649","year":2020},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.472660Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:27eee0f63d708e7772ef1607df67a6e7f37cd501053877be2ea89b84e54b296a","observation_id":"1ebf4436-8f39-4af8-9da0-cbe9f8ab0b39","resolution":{"observed_at":"2026-08-09T17:26:46.647251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08659","last_updated":"2023-11-28T16:06:59Z","snapshot_observed_at":"2026-08-15T03:28:05.715945Z","submitted_at":"2023-10-12T18:34:08Z","title":"LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08659","snapshot_observed_at":"2026-08-09T17:26:45.478477Z","title":"Loftq: Lora-fine-tuning-aware quantization for large language models.arXiv preprint arXiv:2310.08659, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.478477Z"},"links":{"cited_paper":"/paper/2310.08659","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:b0961ed693fbb36d61544bc21f8dde317fcdb92f6211d031d3404e09d904fe6e","observation_id":"c7257759-b6f9-401d-9f4f-720fa22eab29","resolution":{"observed_at":"2026-08-09T17:26:45.478477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12023","last_updated":"2024-08-27T00:48:35Z","snapshot_observed_at":"2026-08-15T04:38:25.550463Z","submitted_at":"2023-11-20T18:57:41Z","title":"LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.12023","snapshot_observed_at":"2026-08-09T17:26:45.483874Z","title":"Lq-lora: Low-rank plus quantized matrix decomposition for efficient language model finetuning.arXiv preprint arXiv:2311.12023, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.483874Z"},"links":{"cited_paper":"/paper/2311.12023","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:2e10bf255e5c40df0fdf186d22046940bd0c9a2e51300be1eb2dcf9ba82d5dc2","observation_id":"0f26366d-42d3-401e-b344-a8ac4a24df25","resolution":{"observed_at":"2026-08-09T17:26:45.483874Z","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":"2026-08-09T17:26:45.491839Z","title":"The approximation of one matrix by another of lower rank","venue":null,"work_id":null,"year":1936},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.491839Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:787038a17b190073b03a5c63cb63c2ea570cf0ca1af68939a930385966ef2762","observation_id":"08a1fa59-5d4d-44fe-8ef6-ee1d8cbb5c65","resolution":{"observed_at":"2026-08-09T17:26:45.491839Z","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":"2026-08-09T17:26:46.612597Z","title":"Rank-sparsity incoherence for matrix decomposition.SIAM Journal on Optimization, 21(2):572–596, 2011","venue":null,"work_id":"b5ac998c-6716-4585-9bb6-f0af86043986","year":2011},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.496424Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:a781a0681fce0281997de32925a5c49f465150064400c454976ffd43e2d5c8cc","observation_id":"086e4763-1b66-484c-b7a1-351648872fa1","resolution":{"observed_at":"2026-08-09T17:26:46.618487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:46.592939Z","title":"Distributed opti- mizationandstatisticallearningviathealternatingdirectionmethodofmultipliers","venue":null,"work_id":"1f91d6e4-6d7a-4b3f-9b97-373d5c8ee4bc","year":2011},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.500648Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:38d6235a51cd43d33f304648ff4e9bb6e05c4147f49c76fb6f34ef716987c845","observation_id":"b4ba7c4a-4e7c-400e-9446-9f7d58d7a776","resolution":{"observed_at":"2026-08-09T17:26:46.600479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12983","last_updated":"2024-03-02T19:38:10Z","snapshot_observed_at":"2026-08-13T04:04:53.751365Z","submitted_at":"2024-03-02T19:38:10Z","title":"OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12983","snapshot_observed_at":"2026-08-09T17:26:45.505094Z","title":"Osscar: One-shot structured pruning in vision and language models with combinatorial optimization.arXiv preprint arXiv:2403.12983, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.505094Z"},"links":{"cited_paper":"/paper/2403.12983","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:0e10bb94ca386c8d4420816b133cb53839134c50f58ccf695cb5f26858c1b238","observation_id":"6525c939-4197-4870-bf76-9c1189247719","resolution":{"observed_at":"2026-08-09T17:26:45.505094Z","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":"2026-08-09T17:26:46.568395Z","title":"Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions.SIAM review, 53 (2):217–288, 2011","venue":null,"work_id":"f437cf53-70a4-4e97-9215-7f60b7e51d69","year":2011},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.510523Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:ec5c154baa20619825a661f6e0442567944780dd05bd7e0036796c07fad0c6de","observation_id":"45212dc1-b6c0-452f-bed8-795df1285910","resolution":{"observed_at":"2026-08-09T17:26:46.579399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:46.533768Z","title":null,"venue":null,"work_id":"fd95a01e-aed0-467e-9410-af9d04be39a7","year":2020},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.514878Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:fad57b668a2ad229d79d8e44fdaf95e70aafe7f6834974970d7ad45a873b7653","observation_id":"f20e03e8-1d00-40d6-b8b0-cf7fafa5379c","resolution":{"observed_at":"2026-08-09T17:26:46.545744Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:46.510478Z","title":"URL https://huggingface.co/docs/transformers/ perplexity","venue":null,"work_id":"78696df6-8005-4827-be6d-c4a9e0543249","year":2022},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.519093Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:6bc4f43dabbb192762543a5280b31404ff1537224329f3a9eef726b13e0a6537","observation_id":"0df58e60-92b2-456f-8965-37ece2215c54","resolution":{"observed_at":"2026-08-09T17:26:46.517129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:46.489572Z","title":"Pointer sentinel mixture models","venue":null,"work_id":"f001bcca-11ac-4e25-b791-fdd504d8ab88","year":2017},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.523563Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:49847e953a7239a3e0710e2a899708e1e967d2cee59826a58ade9afe1637d5a2","observation_id":"e7901336-8624-46c0-9a03-e256a2248aad","resolution":{"observed_at":"2026-08-09T17:26:46.496944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-09T17:26:45.528782Z","title":"The penn treebank: Annotating predicate argument structure","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.528782Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:ed1afeb84d100ab91e0846a9c04ca3f969f181cae7904e88a5dba4b926058a1e","observation_id":"55b2dc7d-9376-4675-9a1f-b175c76e5f8e","resolution":{"observed_at":"2026-08-09T17:26:45.528782Z","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":"2026-08-09T17:26:45.534313Z","title":"A framework for few-shot language model evaluation, 12 2023.URL https://zenodo","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.534313Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:0a5325427d89e9e56f6b26dae14359ba3fd710ee37b1036d8ed423f8a8e6a7fd","observation_id":"3b3d258b-f4dc-4ddd-b414-5a6aa5a707ba","resolution":{"observed_at":"2026-08-09T17:26:45.534313Z","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":"2026-08-09T17:26:45.539068Z","title":"Piqa: Reasoning about physical commonsense in natural language","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.539068Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:4067db8c09c4228279ce48a9d1f706103e50b7b8e718a6eddab242174d7bb3e4","observation_id":"1a252bdf-1e43-47fb-91c4-4604af9baa79","resolution":{"observed_at":"2026-08-09T17:26:45.539068Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.05457","last_updated":"2018-03-14T18:04:21Z","snapshot_observed_at":"2026-08-14T19:36:07.505691Z","submitted_at":"2018-03-14T18:04:21Z","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.05457","snapshot_observed_at":"2026-08-09T17:26:45.544156Z","title":"Think you have solved question answering? try arc, the ai2 reasoning challenge","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.544156Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:d03c502d1fda9ae0794dca155b7962b68ae66821e137a3eefb603dadfde6007e","observation_id":"b198d023-76bd-4419-8e87-3419347873d8","resolution":{"observed_at":"2026-08-09T17:26:45.544156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.07830","last_updated":"2019-05-19T23:57:23Z","snapshot_observed_at":"2026-08-15T09:37:44.321271Z","submitted_at":"2019-05-19T23:57:23Z","title":"HellaSwag: Can a Machine Really Finish Your Sentence?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.07830","snapshot_observed_at":"2026-08-09T17:26:45.549263Z","title":"Hellaswag: Can a machine really finish your sentence?arXiv preprint arXiv:1905.07830, 2019","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.549263Z"},"links":{"cited_paper":"/paper/1905.07830","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:03d31effa631f88e79cc1657851b9b8f7d3e15d60522fc78e7ffa4f1152c4433","observation_id":"25ae0331-935d-4ff9-9738-497c3c1dae24","resolution":{"observed_at":"2026-08-09T17:26:45.549263Z","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":"2026-08-09T17:26:45.554128Z","title":"Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.554128Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:66cf2cf35eefef3924f3a109ee6491dd72722cd477ba748b406c78aec9a369b3","observation_id":"ca9c5f48-ebd7-48c2-baf7-45844f0811a9","resolution":{"observed_at":"2026-08-09T17:26:45.554128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.03061","last_updated":"2020-10-06T22:23:00Z","snapshot_observed_at":"2026-08-13T21:24:29.996274Z","submitted_at":"2020-10-06T22:23:00Z","title":"A Survey on Recognizing Textual Entailment as an NLP Evaluation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.03061","snapshot_observed_at":"2026-08-09T17:26:45.559281Z","title":"A survey on recognizing textual entailment as an nlp evaluation.arXiv preprint arXiv:2010.03061, 2020","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.559281Z"},"links":{"cited_paper":"/paper/2010.03061","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:8d266d669de7e7c911198fb1a65516d127d0c50127e27035f67c2cbd8b37fac5","observation_id":"538f3029-6a30-48f6-a295-3e6ec5eb0112","resolution":{"observed_at":"2026-08-09T17:26:45.559281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.10738","last_updated":"2019-07-24T21:37:16Z","snapshot_observed_at":"2026-08-05T08:00:03.179363Z","submitted_at":"2019-07-24T21:37:16Z","title":"Careful Selection of Knowledge to solve Open Book Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.10738","snapshot_observed_at":"2026-08-09T17:26:45.564609Z","title":"Careful selection of knowledge to solve open book question answering.arXiv preprint arXiv:1907.10738, 2019","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.564609Z"},"links":{"cited_paper":"/paper/1907.10738","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:d69ff40986c778898b2b41ad6c554aa767e569a185ea913a897c6216def48ee6","observation_id":"dbdf7098-7262-45bf-98e9-7d8556f0eaae","resolution":{"observed_at":"2026-08-09T17:26:45.564609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10044","last_updated":"2019-05-24T05:48:49Z","snapshot_observed_at":"2026-08-14T15:52:43.138964Z","submitted_at":"2019-05-24T05:48:49Z","title":"BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10044","snapshot_observed_at":"2026-08-09T17:26:45.569664Z","title":"Boolq: Exploring the surprising difficulty of natural yes/no questions","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.569664Z"},"links":{"cited_paper":"/paper/1905.10044","citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:f8428bc02622de47976780cec7f3fe1c9e5cd7e818ef913f0b62f43627c7117d","observation_id":"6a160651-dce4-446a-b70c-ea8db0a525b3","resolution":{"observed_at":"2026-08-09T17:26:45.569664Z","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":"4152.7127","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T17:26:45.675946Z","title":"Interactive supercomputing on 40,000 cores for machine learning and data analysis","venue":null,"work_id":"a65d67fe-c679-4481-9c8a-ab3b34a267e3","year":2018},"citing_paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-09T17:26:45.574894Z"},"links":{"citing_paper":"/paper/2502.00899"},"observation_digest":"sha256:84fab390cef4bfcfa7893cbd75a16a6acb02fc0154a02371438b4a14c5658401","observation_id":"a96c3741-45ad-4b8d-932e-8e8febb790d8","resolution":{"observed_at":"2026-08-09T17:26:45.686144Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.00899","last_updated":"2025-02-02T20:23:32Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-14T00:37:34.804891Z","submitted_at":"2025-02-02T20:23:32Z","title":"HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":35,"verified_exact":2,"verified_fuzzy":17},"total_outbound_references":55},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 2 inbound Pith citation observations for arXiv:2502.00899."}