{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:XHCNSVV2W63KDTH6UFEQXTN25Y","short_pith_number":"pith:XHCNSVV2","canonical_record":{"source":{"id":"2305.02538","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-04T04:20:20Z","cross_cats_sorted":[],"title_canon_sha256":"7d949db197b056276c02b605538f6e7ff477f365c0f5e9925a399ba0d6fd89c6","abstract_canon_sha256":"3e22a5ef519cb561cd47b89da99a3f9cad5a6a5e8861f86e43a4d6946c889b82"},"schema_version":"1.0"},"canonical_sha256":"b9c4d956bab7b6a1ccfea1490bcdbaee35373a43a96eb04b0ce17919fbbe8745","source":{"kind":"arxiv","id":"2305.02538","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.02538","created_at":"2026-07-05T06:07:22Z"},{"alias_kind":"arxiv_version","alias_value":"2305.02538v2","created_at":"2026-07-05T06:07:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.02538","created_at":"2026-07-05T06:07:22Z"},{"alias_kind":"pith_short_12","alias_value":"XHCNSVV2W63K","created_at":"2026-07-05T06:07:22Z"},{"alias_kind":"pith_short_16","alias_value":"XHCNSVV2W63KDTH6","created_at":"2026-07-05T06:07:22Z"},{"alias_kind":"pith_short_8","alias_value":"XHCNSVV2","created_at":"2026-07-05T06:07:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:XHCNSVV2W63KDTH6UFEQXTN25Y","target":"record","payload":{"canonical_record":{"source":{"id":"2305.02538","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-04T04:20:20Z","cross_cats_sorted":[],"title_canon_sha256":"7d949db197b056276c02b605538f6e7ff477f365c0f5e9925a399ba0d6fd89c6","abstract_canon_sha256":"3e22a5ef519cb561cd47b89da99a3f9cad5a6a5e8861f86e43a4d6946c889b82"},"schema_version":"1.0"},"canonical_sha256":"b9c4d956bab7b6a1ccfea1490bcdbaee35373a43a96eb04b0ce17919fbbe8745","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:07:22.132909Z","signature_b64":"IPg1JhfJGFevNKZISVqttCnwsQxI2zuSV5ycT9agWX+SdCsVglmQDZ2lYF+J0vpDwNzmtABKiqQHcxvUSMOyDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9c4d956bab7b6a1ccfea1490bcdbaee35373a43a96eb04b0ce17919fbbe8745","last_reissued_at":"2026-07-05T06:07:22.132441Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:07:22.132441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.02538","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:07:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RAPutMNxYXyHVyy0vrZbJR2opekfRSMxGbI0NlqHv4c9qHOAQfKN6dYH+YC+pcWvSqLesqILyoCgjkyjznhHDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T18:31:32.305899Z"},"content_sha256":"0d13756cb35ba54f95cdb2666ccc73fea6bae9df618f786c23a7e1afb992efc2","schema_version":"1.0","event_id":"sha256:0d13756cb35ba54f95cdb2666ccc73fea6bae9df618f786c23a7e1afb992efc2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:XHCNSVV2W63KDTH6UFEQXTN25Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cuttlefish: Low-Rank Model Training without All the Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dimitris Papailiopoulos, Eric P. Xing, Hongyi Wang, Pongsakorn U-Chupala, Saurabh Agarwal, Yoshiki Tanaka","submitted_at":"2023-05-04T04:20:20Z","abstract_excerpt":"Recent research has shown that training low-rank neural networks can effectively reduce the total number of trainable parameters without sacrificing predictive accuracy, resulting in end-to-end speedups. However, low-rank model training necessitates adjusting several additional factorization hyperparameters, such as the rank of the factorization at each layer. In this paper, we tackle this challenge by introducing Cuttlefish, an automated low-rank training approach that eliminates the need for tuning factorization hyperparameters. Cuttlefish leverages the observation that after a few epochs of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.02538","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/2305.02538/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:07:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2jC60Cr5Gw4gdOHnNiw634pHdv4yMR1afTMl3cuA//mob/hkjvNllCZ8w2MUwPPbrOxjS7Kd/PqbWjCz5g9XCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T18:31:32.306392Z"},"content_sha256":"d3c4a14bec3136321b323aae4b5d8e62e2841067ba239740f51622b22a66c415","schema_version":"1.0","event_id":"sha256:d3c4a14bec3136321b323aae4b5d8e62e2841067ba239740f51622b22a66c415"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XHCNSVV2W63KDTH6UFEQXTN25Y/bundle.json","state_url":"https://pith.science/pith/XHCNSVV2W63KDTH6UFEQXTN25Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XHCNSVV2W63KDTH6UFEQXTN25Y/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T18:31:32Z","links":{"resolver":"https://pith.science/pith/XHCNSVV2W63KDTH6UFEQXTN25Y","bundle":"https://pith.science/pith/XHCNSVV2W63KDTH6UFEQXTN25Y/bundle.json","state":"https://pith.science/pith/XHCNSVV2W63KDTH6UFEQXTN25Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XHCNSVV2W63KDTH6UFEQXTN25Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:XHCNSVV2W63KDTH6UFEQXTN25Y","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3e22a5ef519cb561cd47b89da99a3f9cad5a6a5e8861f86e43a4d6946c889b82","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-04T04:20:20Z","title_canon_sha256":"7d949db197b056276c02b605538f6e7ff477f365c0f5e9925a399ba0d6fd89c6"},"schema_version":"1.0","source":{"id":"2305.02538","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.02538","created_at":"2026-07-05T06:07:22Z"},{"alias_kind":"arxiv_version","alias_value":"2305.02538v2","created_at":"2026-07-05T06:07:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.02538","created_at":"2026-07-05T06:07:22Z"},{"alias_kind":"pith_short_12","alias_value":"XHCNSVV2W63K","created_at":"2026-07-05T06:07:22Z"},{"alias_kind":"pith_short_16","alias_value":"XHCNSVV2W63KDTH6","created_at":"2026-07-05T06:07:22Z"},{"alias_kind":"pith_short_8","alias_value":"XHCNSVV2","created_at":"2026-07-05T06:07:22Z"}],"graph_snapshots":[{"event_id":"sha256:d3c4a14bec3136321b323aae4b5d8e62e2841067ba239740f51622b22a66c415","target":"graph","created_at":"2026-07-05T06:07:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2305.02538/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent research has shown that training low-rank neural networks can effectively reduce the total number of trainable parameters without sacrificing predictive accuracy, resulting in end-to-end speedups. However, low-rank model training necessitates adjusting several additional factorization hyperparameters, such as the rank of the factorization at each layer. In this paper, we tackle this challenge by introducing Cuttlefish, an automated low-rank training approach that eliminates the need for tuning factorization hyperparameters. Cuttlefish leverages the observation that after a few epochs of","authors_text":"Dimitris Papailiopoulos, Eric P. Xing, Hongyi Wang, Pongsakorn U-Chupala, Saurabh Agarwal, Yoshiki Tanaka","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-04T04:20:20Z","title":"Cuttlefish: Low-Rank Model Training without All the Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.02538","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0d13756cb35ba54f95cdb2666ccc73fea6bae9df618f786c23a7e1afb992efc2","target":"record","created_at":"2026-07-05T06:07:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3e22a5ef519cb561cd47b89da99a3f9cad5a6a5e8861f86e43a4d6946c889b82","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-04T04:20:20Z","title_canon_sha256":"7d949db197b056276c02b605538f6e7ff477f365c0f5e9925a399ba0d6fd89c6"},"schema_version":"1.0","source":{"id":"2305.02538","kind":"arxiv","version":2}},"canonical_sha256":"b9c4d956bab7b6a1ccfea1490bcdbaee35373a43a96eb04b0ce17919fbbe8745","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b9c4d956bab7b6a1ccfea1490bcdbaee35373a43a96eb04b0ce17919fbbe8745","first_computed_at":"2026-07-05T06:07:22.132441Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:07:22.132441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IPg1JhfJGFevNKZISVqttCnwsQxI2zuSV5ycT9agWX+SdCsVglmQDZ2lYF+J0vpDwNzmtABKiqQHcxvUSMOyDw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:07:22.132909Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.02538","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0d13756cb35ba54f95cdb2666ccc73fea6bae9df618f786c23a7e1afb992efc2","sha256:d3c4a14bec3136321b323aae4b5d8e62e2841067ba239740f51622b22a66c415"],"state_sha256":"a54bba637baea231ef5d38890a28edc9aeb701b4420fb462d0dec78df5c7b4a1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kt43vZX2fgNEs8/j72Qk/P5qhjOe9P1DlDgXORLuPG8sejh7Lb3zXNoxRUn0W7ittUzYRO2bOEG5tp22uCYEAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T18:31:32.310018Z","bundle_sha256":"2880a19db452cbc530f3f98136f27869cffafbceb9b994a1afffeb05022f8eda"}}