{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UNN3ZQQICZHHFOVSB7XBCA2VSB","short_pith_number":"pith:UNN3ZQQI","schema_version":"1.0","canonical_sha256":"a35bbcc208164e72bab20fee1103559077e82dd1145d5ce81b0774fc94167ec6","source":{"kind":"arxiv","id":"2411.05239","version":2},"attestation_state":"computed","paper":{"title":"ZipNN: Lossless Compression for AI Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Andrew Wood, Danny Harnik, Guy Girmonsky, Ilias Ennmouri, Leshem Choshen, Michal Malka, Moshik Hershcovitch, Peter Chin, Roy Leibovitz, Swaminathan Sundararaman","submitted_at":"2024-11-07T23:28:23Z","abstract_excerpt":"With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression literature deleting parts of the model weights for faster inference, we investigate a more traditional type of compression - one that represents the model in a compact form and is coupled with a decompression algorithm that returns it to its original form and size - namely lossless compression.\n  We present ZipNN a lossless compression tailored to neural networks. Somewhat surprisingly"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.05239","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-07T23:28:23Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"d41d7aba443d937655b6f123ffcee62c0e571441f13a538ac4f38e613a7dfb46","abstract_canon_sha256":"c2809b094b54d3b4d6221dd0f796e0b82744a932d1c98f25d4bfdf8812152773"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:40.297748Z","signature_b64":"wQqMxYV7Cuc0AkI6TT9Fu7vGqqvVWAczx3N2nsMqpIk9Q9JhVdol8FTE3tUMZb8PDa8622bcQXyPF4T9LCgJDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a35bbcc208164e72bab20fee1103559077e82dd1145d5ce81b0774fc94167ec6","last_reissued_at":"2026-07-05T11:15:40.297197Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:40.297197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ZipNN: Lossless Compression for AI Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Andrew Wood, Danny Harnik, Guy Girmonsky, Ilias Ennmouri, Leshem Choshen, Michal Malka, Moshik Hershcovitch, Peter Chin, Roy Leibovitz, Swaminathan Sundararaman","submitted_at":"2024-11-07T23:28:23Z","abstract_excerpt":"With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression literature deleting parts of the model weights for faster inference, we investigate a more traditional type of compression - one that represents the model in a compact form and is coupled with a decompression algorithm that returns it to its original form and size - namely lossless compression.\n  We present ZipNN a lossless compression tailored to neural networks. Somewhat surprisingly"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.05239","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.05239/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.05239","created_at":"2026-07-05T11:15:40.297285+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.05239v2","created_at":"2026-07-05T11:15:40.297285+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.05239","created_at":"2026-07-05T11:15:40.297285+00:00"},{"alias_kind":"pith_short_12","alias_value":"UNN3ZQQICZHH","created_at":"2026-07-05T11:15:40.297285+00:00"},{"alias_kind":"pith_short_16","alias_value":"UNN3ZQQICZHHFOVS","created_at":"2026-07-05T11:15:40.297285+00:00"},{"alias_kind":"pith_short_8","alias_value":"UNN3ZQQI","created_at":"2026-07-05T11:15:40.297285+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01708","citing_title":"SplitZip: Ultra Fast Lossless KV Compression for Disaggregated LLM Serving","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17104","citing_title":"TStore: Rethinking AI Model Hub with Tensor-Centric Compression","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2604.27844","citing_title":"ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01708","citing_title":"SplitZip: Ultra Fast Lossless KV Compression for Disaggregated LLM Serving","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01708","citing_title":"SplitZip: Ultra Fast Lossless KV Compression for Disaggregated LLM Serving","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17104","citing_title":"TStore: Rethinking AI Model Hub with Tensor-Centric Compression","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21072","citing_title":"Distributed Generative Inference of LLM at Internet Scales with Multi-Dimensional Communication Optimization","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UNN3ZQQICZHHFOVSB7XBCA2VSB","json":"https://pith.science/pith/UNN3ZQQICZHHFOVSB7XBCA2VSB.json","graph_json":"https://pith.science/api/pith-number/UNN3ZQQICZHHFOVSB7XBCA2VSB/graph.json","events_json":"https://pith.science/api/pith-number/UNN3ZQQICZHHFOVSB7XBCA2VSB/events.json","paper":"https://pith.science/paper/UNN3ZQQI"},"agent_actions":{"view_html":"https://pith.science/pith/UNN3ZQQICZHHFOVSB7XBCA2VSB","download_json":"https://pith.science/pith/UNN3ZQQICZHHFOVSB7XBCA2VSB.json","view_paper":"https://pith.science/paper/UNN3ZQQI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.05239&json=true","fetch_graph":"https://pith.science/api/pith-number/UNN3ZQQICZHHFOVSB7XBCA2VSB/graph.json","fetch_events":"https://pith.science/api/pith-number/UNN3ZQQICZHHFOVSB7XBCA2VSB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UNN3ZQQICZHHFOVSB7XBCA2VSB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UNN3ZQQICZHHFOVSB7XBCA2VSB/action/storage_attestation","attest_author":"https://pith.science/pith/UNN3ZQQICZHHFOVSB7XBCA2VSB/action/author_attestation","sign_citation":"https://pith.science/pith/UNN3ZQQICZHHFOVSB7XBCA2VSB/action/citation_signature","submit_replication":"https://pith.science/pith/UNN3ZQQICZHHFOVSB7XBCA2VSB/action/replication_record"}},"created_at":"2026-07-05T11:15:40.297285+00:00","updated_at":"2026-07-05T11:15:40.297285+00:00"}