{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:VL3FEATYQWIG74IAV3UTJQKVE7","short_pith_number":"pith:VL3FEATY","canonical_record":{"source":{"id":"2306.11800","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-20T18:00:31Z","cross_cats_sorted":[],"title_canon_sha256":"43f88bb9e62cd1943fbbfc490abd627476a4111f7934e37911165e71a1ef1767","abstract_canon_sha256":"a56fd5ed2e16c107e1779016b905866c3b4108abf82c86a9e3313fb8735a12ba"},"schema_version":"1.0"},"canonical_sha256":"aaf652027885906ff100aee934c15527f16cb47b277c6d33eebcbab6dd644b15","source":{"kind":"arxiv","id":"2306.11800","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.11800","created_at":"2026-07-05T09:19:23Z"},{"alias_kind":"arxiv_version","alias_value":"2306.11800v3","created_at":"2026-07-05T09:19:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11800","created_at":"2026-07-05T09:19:23Z"},{"alias_kind":"pith_short_12","alias_value":"VL3FEATYQWIG","created_at":"2026-07-05T09:19:23Z"},{"alias_kind":"pith_short_16","alias_value":"VL3FEATYQWIG74IA","created_at":"2026-07-05T09:19:23Z"},{"alias_kind":"pith_short_8","alias_value":"VL3FEATY","created_at":"2026-07-05T09:19:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:VL3FEATYQWIG74IAV3UTJQKVE7","target":"record","payload":{"canonical_record":{"source":{"id":"2306.11800","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-20T18:00:31Z","cross_cats_sorted":[],"title_canon_sha256":"43f88bb9e62cd1943fbbfc490abd627476a4111f7934e37911165e71a1ef1767","abstract_canon_sha256":"a56fd5ed2e16c107e1779016b905866c3b4108abf82c86a9e3313fb8735a12ba"},"schema_version":"1.0"},"canonical_sha256":"aaf652027885906ff100aee934c15527f16cb47b277c6d33eebcbab6dd644b15","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:19:23.500844Z","signature_b64":"7jTHS1H3+ZuwWpek3DchDXEpFWQxtcvr3zlAoCnl9/b2Cyx/7L9WfS++9R+oTagquoPySKu9WUQJEbljNeMEBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aaf652027885906ff100aee934c15527f16cb47b277c6d33eebcbab6dd644b15","last_reissued_at":"2026-07-05T09:19:23.500350Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:19:23.500350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.11800","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:19:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PbWNLV1hUjl/P/kiM+FKKKRwA/uxIWSphkupwzX57QjZfoAKPWiS6eDi4yTSQd8dUGFkvZvhbFxR2niFtSugCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T09:11:12.776940Z"},"content_sha256":"3bfa7715c175eb985edc9d05c813bfcfb80105d611ab5b8403e4e59529a1f445","schema_version":"1.0","event_id":"sha256:3bfa7715c175eb985edc9d05c813bfcfb80105d611ab5b8403e4e59529a1f445"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:VL3FEATYQWIG74IAV3UTJQKVE7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexey Tumanov, Amey Agrawal, Kexin Rong, Sameer Reddy, Satwik Bhattamishra, Venkata Prabhakara Sarath Nookala, Vidushi Vashishth","submitted_at":"2023-06-20T18:00:31Z","abstract_excerpt":"With the increase in the scale of Deep Learning (DL) training workloads in terms of compute resources and time consumption, the likelihood of encountering in-training failures rises substantially, leading to lost work and resource wastage. Such failures are typically offset by a checkpointing mechanism, which comes at the cost of storage and network bandwidth overhead. State-of-the-art approaches involve lossy model compression mechanisms, which induce a tradeoff between the resulting model quality (accuracy) and compression ratio. Delta compression is then used to further reduce the overhead "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11800","kind":"arxiv","version":3},"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/2306.11800/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":"2026-07-05T09:19:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pPSAOaL76PcY/IoTimfaNtGl/cd3obtouSMGsXpPxudH29OfU6vbleFGORD8DGohxjxNWfFQ/gc2wzgAOkvKCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T09:11:12.777474Z"},"content_sha256":"b9f651406468d761299966561da21eaf5949ac9040bd23d7cf93b6dac453c676","schema_version":"1.0","event_id":"sha256:b9f651406468d761299966561da21eaf5949ac9040bd23d7cf93b6dac453c676"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VL3FEATYQWIG74IAV3UTJQKVE7/bundle.json","state_url":"https://pith.science/pith/VL3FEATYQWIG74IAV3UTJQKVE7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VL3FEATYQWIG74IAV3UTJQKVE7/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":"2026-08-11T09:11:12Z","links":{"resolver":"https://pith.science/pith/VL3FEATYQWIG74IAV3UTJQKVE7","bundle":"https://pith.science/pith/VL3FEATYQWIG74IAV3UTJQKVE7/bundle.json","state":"https://pith.science/pith/VL3FEATYQWIG74IAV3UTJQKVE7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VL3FEATYQWIG74IAV3UTJQKVE7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:VL3FEATYQWIG74IAV3UTJQKVE7","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":"a56fd5ed2e16c107e1779016b905866c3b4108abf82c86a9e3313fb8735a12ba","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-20T18:00:31Z","title_canon_sha256":"43f88bb9e62cd1943fbbfc490abd627476a4111f7934e37911165e71a1ef1767"},"schema_version":"1.0","source":{"id":"2306.11800","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.11800","created_at":"2026-07-05T09:19:23Z"},{"alias_kind":"arxiv_version","alias_value":"2306.11800v3","created_at":"2026-07-05T09:19:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11800","created_at":"2026-07-05T09:19:23Z"},{"alias_kind":"pith_short_12","alias_value":"VL3FEATYQWIG","created_at":"2026-07-05T09:19:23Z"},{"alias_kind":"pith_short_16","alias_value":"VL3FEATYQWIG74IA","created_at":"2026-07-05T09:19:23Z"},{"alias_kind":"pith_short_8","alias_value":"VL3FEATY","created_at":"2026-07-05T09:19:23Z"}],"graph_snapshots":[{"event_id":"sha256:b9f651406468d761299966561da21eaf5949ac9040bd23d7cf93b6dac453c676","target":"graph","created_at":"2026-07-05T09:19:23Z","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/2306.11800/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the increase in the scale of Deep Learning (DL) training workloads in terms of compute resources and time consumption, the likelihood of encountering in-training failures rises substantially, leading to lost work and resource wastage. Such failures are typically offset by a checkpointing mechanism, which comes at the cost of storage and network bandwidth overhead. State-of-the-art approaches involve lossy model compression mechanisms, which induce a tradeoff between the resulting model quality (accuracy) and compression ratio. Delta compression is then used to further reduce the overhead ","authors_text":"Alexey Tumanov, Amey Agrawal, Kexin Rong, Sameer Reddy, Satwik Bhattamishra, Venkata Prabhakara Sarath Nookala, Vidushi Vashishth","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-20T18:00:31Z","title":"Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11800","kind":"arxiv","version":3},"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":"sha256:3bfa7715c175eb985edc9d05c813bfcfb80105d611ab5b8403e4e59529a1f445","target":"record","created_at":"2026-07-05T09:19:23Z","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":"a56fd5ed2e16c107e1779016b905866c3b4108abf82c86a9e3313fb8735a12ba","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-20T18:00:31Z","title_canon_sha256":"43f88bb9e62cd1943fbbfc490abd627476a4111f7934e37911165e71a1ef1767"},"schema_version":"1.0","source":{"id":"2306.11800","kind":"arxiv","version":3}},"canonical_sha256":"aaf652027885906ff100aee934c15527f16cb47b277c6d33eebcbab6dd644b15","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aaf652027885906ff100aee934c15527f16cb47b277c6d33eebcbab6dd644b15","first_computed_at":"2026-07-05T09:19:23.500350Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:19:23.500350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7jTHS1H3+ZuwWpek3DchDXEpFWQxtcvr3zlAoCnl9/b2Cyx/7L9WfS++9R+oTagquoPySKu9WUQJEbljNeMEBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:19:23.500844Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.11800","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3bfa7715c175eb985edc9d05c813bfcfb80105d611ab5b8403e4e59529a1f445","sha256:b9f651406468d761299966561da21eaf5949ac9040bd23d7cf93b6dac453c676"],"state_sha256":"1263fa55ddbed049b3a4a1ac4dd2468a180e4f6ec56a1ae414a97448ffe950aa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"odjPothaCx0f4/wXDbZiq/UDp7e66rERz4K0AI3mCwwShgAVTUj6t6WczWCqQpkAL/BTyClsMRfLwsbhF6osAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T09:11:12.782588Z","bundle_sha256":"015cd681c4d709c65bfd931600b18d7dafca3be0306d2c8b701c9e16854cff15"}}