{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QDJQVOWOK7T5RKP4YTS6IWE5YX","short_pith_number":"pith:QDJQVOWO","schema_version":"1.0","canonical_sha256":"80d30abace57e7d8a9fcc4e5e4589dc5ec906441ec69025f29e879a420101214","source":{"kind":"arxiv","id":"2411.10543","version":1},"attestation_state":"computed","paper":{"title":"SoftLMs: Efficient Adaptive Low-Rank Approximation of Language Models using Soft-Thresholding Mechanism","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Linfeng Wen, Mingu Kang, Priyansh Bhatnagar","submitted_at":"2024-11-15T19:29:51Z","abstract_excerpt":"Extensive efforts have been made to boost the performance in the domain of language models by introducing various attention-based transformers. However, the inclusion of linear layers with large dimensions contributes to significant computational and memory overheads. The escalating computational demands of these models necessitate the development of various compression techniques to ensure their deployment on devices, particularly in resource-constrained environments. In this paper, we propose a novel compression methodology that dynamically determines the rank of each layer using a soft thre"},"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":"2411.10543","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-15T19:29:51Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"db132e4c00ccc69069dba275fc1f575025e529abf47b6b368aeb19db2c4252b8","abstract_canon_sha256":"66701fb55a0ca49d25251712eb05cbb24f96d749baf15cc0ee41b39a31899428"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:51.321482Z","signature_b64":"72147+3POn6McOh7UMIN/MGlvTUiSsjvjcTTM1Dopk+T74Wb+sicjrFmAhHqSiHRgF+t8Q5ozszFlv+1qR2aAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80d30abace57e7d8a9fcc4e5e4589dc5ec906441ec69025f29e879a420101214","last_reissued_at":"2026-07-05T09:36:51.320969Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:51.320969Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SoftLMs: Efficient Adaptive Low-Rank Approximation of Language Models using Soft-Thresholding Mechanism","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Linfeng Wen, Mingu Kang, Priyansh Bhatnagar","submitted_at":"2024-11-15T19:29:51Z","abstract_excerpt":"Extensive efforts have been made to boost the performance in the domain of language models by introducing various attention-based transformers. However, the inclusion of linear layers with large dimensions contributes to significant computational and memory overheads. The escalating computational demands of these models necessitate the development of various compression techniques to ensure their deployment on devices, particularly in resource-constrained environments. In this paper, we propose a novel compression methodology that dynamically determines the rank of each layer using a soft thre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.10543","kind":"arxiv","version":1},"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/2411.10543/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":"2411.10543","created_at":"2026-07-05T09:36:51.321024+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.10543v1","created_at":"2026-07-05T09:36:51.321024+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.10543","created_at":"2026-07-05T09:36:51.321024+00:00"},{"alias_kind":"pith_short_12","alias_value":"QDJQVOWOK7T5","created_at":"2026-07-05T09:36:51.321024+00:00"},{"alias_kind":"pith_short_16","alias_value":"QDJQVOWOK7T5RKP4","created_at":"2026-07-05T09:36:51.321024+00:00"},{"alias_kind":"pith_short_8","alias_value":"QDJQVOWO","created_at":"2026-07-05T09:36:51.321024+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QDJQVOWOK7T5RKP4YTS6IWE5YX","json":"https://pith.science/pith/QDJQVOWOK7T5RKP4YTS6IWE5YX.json","graph_json":"https://pith.science/api/pith-number/QDJQVOWOK7T5RKP4YTS6IWE5YX/graph.json","events_json":"https://pith.science/api/pith-number/QDJQVOWOK7T5RKP4YTS6IWE5YX/events.json","paper":"https://pith.science/paper/QDJQVOWO"},"agent_actions":{"view_html":"https://pith.science/pith/QDJQVOWOK7T5RKP4YTS6IWE5YX","download_json":"https://pith.science/pith/QDJQVOWOK7T5RKP4YTS6IWE5YX.json","view_paper":"https://pith.science/paper/QDJQVOWO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.10543&json=true","fetch_graph":"https://pith.science/api/pith-number/QDJQVOWOK7T5RKP4YTS6IWE5YX/graph.json","fetch_events":"https://pith.science/api/pith-number/QDJQVOWOK7T5RKP4YTS6IWE5YX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QDJQVOWOK7T5RKP4YTS6IWE5YX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QDJQVOWOK7T5RKP4YTS6IWE5YX/action/storage_attestation","attest_author":"https://pith.science/pith/QDJQVOWOK7T5RKP4YTS6IWE5YX/action/author_attestation","sign_citation":"https://pith.science/pith/QDJQVOWOK7T5RKP4YTS6IWE5YX/action/citation_signature","submit_replication":"https://pith.science/pith/QDJQVOWOK7T5RKP4YTS6IWE5YX/action/replication_record"}},"created_at":"2026-07-05T09:36:51.321024+00:00","updated_at":"2026-07-05T09:36:51.321024+00:00"}