{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:3QJDYS26WZZTMTFTXFSENPFOPJ","short_pith_number":"pith:3QJDYS26","canonical_record":{"source":{"id":"2302.08545","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-16T19:48:20Z","cross_cats_sorted":["cs.AI","cs.NI"],"title_canon_sha256":"7403deadde5b38d76617bbfb0dc78b0c5f40d0c94f514836056d734a1e223fd3","abstract_canon_sha256":"c6b7ff6eca11e332d1b82001db10a3db7172a91df7ff392af1b1424068fbec98"},"schema_version":"1.0"},"canonical_sha256":"dc123c4b5eb673364cb3b96446bcae7a6d96630ae77f0abe134e527ad53a3c69","source":{"kind":"arxiv","id":"2302.08545","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.08545","created_at":"2026-07-05T07:52:39Z"},{"alias_kind":"arxiv_version","alias_value":"2302.08545v2","created_at":"2026-07-05T07:52:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.08545","created_at":"2026-07-05T07:52:39Z"},{"alias_kind":"pith_short_12","alias_value":"3QJDYS26WZZT","created_at":"2026-07-05T07:52:39Z"},{"alias_kind":"pith_short_16","alias_value":"3QJDYS26WZZTMTFT","created_at":"2026-07-05T07:52:39Z"},{"alias_kind":"pith_short_8","alias_value":"3QJDYS26","created_at":"2026-07-05T07:52:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:3QJDYS26WZZTMTFTXFSENPFOPJ","target":"record","payload":{"canonical_record":{"source":{"id":"2302.08545","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-16T19:48:20Z","cross_cats_sorted":["cs.AI","cs.NI"],"title_canon_sha256":"7403deadde5b38d76617bbfb0dc78b0c5f40d0c94f514836056d734a1e223fd3","abstract_canon_sha256":"c6b7ff6eca11e332d1b82001db10a3db7172a91df7ff392af1b1424068fbec98"},"schema_version":"1.0"},"canonical_sha256":"dc123c4b5eb673364cb3b96446bcae7a6d96630ae77f0abe134e527ad53a3c69","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:52:39.709676Z","signature_b64":"kmrdhIvDFN94f4PHt6uGwa8peufOUvm0OxDe802vcyWIzoD8ZADH5cwSQyTMYZpgb2rjf33gbBZuyfmnhzI3Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc123c4b5eb673364cb3b96446bcae7a6d96630ae77f0abe134e527ad53a3c69","last_reissued_at":"2026-07-05T07:52:39.709194Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:52:39.709194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.08545","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":"2026-07-05T07:52:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KA0L7/esbt4Pl6hR64rmL2C4b6SvU8LxyzHQusf1HQ7WQ2oPZsSapFbKOSLfhXXcq3qqTgTXBrr8mwMUwIWpBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:39:14.942125Z"},"content_sha256":"b32d59582ca324e669aa82a9b751c23336617922528ac9cd5069aa34121ffe04","schema_version":"1.0","event_id":"sha256:b32d59582ca324e669aa82a9b751c23336617922528ac9cd5069aa34121ffe04"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:3QJDYS26WZZTMTFTXFSENPFOPJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NI"],"primary_cat":"cs.LG","authors_text":"(2) University College London, (3) VMware Research), ChonLam Lao (1), Kevin Xu (1), Michael Mitzenmacher (1), Minghao Li (1), Minlan Yu (1) ((1) Harvard University, Ran Ben Basat (2), Shay Vargaftik (3)","submitted_at":"2023-02-16T19:48:20Z","abstract_excerpt":"Deep neural networks (DNNs) are the de facto standard for essential use cases, such as image classification, computer vision, and natural language processing. As DNNs and datasets get larger, they require distributed training on increasingly larger clusters. A main bottleneck is the resulting communication overhead where workers exchange model updates (i.e., gradients) on a per-round basis. To address this bottleneck and accelerate training, a widely-deployed approach is compression. However, previous deployments often apply bi-directional compression schemes by simply using a uni-directional "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.08545","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/2302.08545/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":"2026-07-05T07:52:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l//OFFAFBRqPyZyUYQsLAPtZEMHtl2+kiLa3gXY3eYOPLrnyw/IRFdTxSH89dlDT9d5QHSTGGDsKmFuFNyrrDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:39:14.943059Z"},"content_sha256":"2492e9c3f0fee6ef70f97010bdc425d9dc023949dd8d03dee27285378cbd4df8","schema_version":"1.0","event_id":"sha256:2492e9c3f0fee6ef70f97010bdc425d9dc023949dd8d03dee27285378cbd4df8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3QJDYS26WZZTMTFTXFSENPFOPJ/bundle.json","state_url":"https://pith.science/pith/3QJDYS26WZZTMTFTXFSENPFOPJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3QJDYS26WZZTMTFTXFSENPFOPJ/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":"2026-08-09T00:39:14Z","links":{"resolver":"https://pith.science/pith/3QJDYS26WZZTMTFTXFSENPFOPJ","bundle":"https://pith.science/pith/3QJDYS26WZZTMTFTXFSENPFOPJ/bundle.json","state":"https://pith.science/pith/3QJDYS26WZZTMTFTXFSENPFOPJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3QJDYS26WZZTMTFTXFSENPFOPJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3QJDYS26WZZTMTFTXFSENPFOPJ","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":"c6b7ff6eca11e332d1b82001db10a3db7172a91df7ff392af1b1424068fbec98","cross_cats_sorted":["cs.AI","cs.NI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-16T19:48:20Z","title_canon_sha256":"7403deadde5b38d76617bbfb0dc78b0c5f40d0c94f514836056d734a1e223fd3"},"schema_version":"1.0","source":{"id":"2302.08545","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.08545","created_at":"2026-07-05T07:52:39Z"},{"alias_kind":"arxiv_version","alias_value":"2302.08545v2","created_at":"2026-07-05T07:52:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.08545","created_at":"2026-07-05T07:52:39Z"},{"alias_kind":"pith_short_12","alias_value":"3QJDYS26WZZT","created_at":"2026-07-05T07:52:39Z"},{"alias_kind":"pith_short_16","alias_value":"3QJDYS26WZZTMTFT","created_at":"2026-07-05T07:52:39Z"},{"alias_kind":"pith_short_8","alias_value":"3QJDYS26","created_at":"2026-07-05T07:52:39Z"}],"graph_snapshots":[{"event_id":"sha256:2492e9c3f0fee6ef70f97010bdc425d9dc023949dd8d03dee27285378cbd4df8","target":"graph","created_at":"2026-07-05T07:52:39Z","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/2302.08545/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks (DNNs) are the de facto standard for essential use cases, such as image classification, computer vision, and natural language processing. As DNNs and datasets get larger, they require distributed training on increasingly larger clusters. A main bottleneck is the resulting communication overhead where workers exchange model updates (i.e., gradients) on a per-round basis. To address this bottleneck and accelerate training, a widely-deployed approach is compression. However, previous deployments often apply bi-directional compression schemes by simply using a uni-directional ","authors_text":"(2) University College London, (3) VMware Research), ChonLam Lao (1), Kevin Xu (1), Michael Mitzenmacher (1), Minghao Li (1), Minlan Yu (1) ((1) Harvard University, Ran Ben Basat (2), Shay Vargaftik (3)","cross_cats":["cs.AI","cs.NI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-16T19:48:20Z","title":"THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.08545","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:b32d59582ca324e669aa82a9b751c23336617922528ac9cd5069aa34121ffe04","target":"record","created_at":"2026-07-05T07:52:39Z","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":"c6b7ff6eca11e332d1b82001db10a3db7172a91df7ff392af1b1424068fbec98","cross_cats_sorted":["cs.AI","cs.NI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-16T19:48:20Z","title_canon_sha256":"7403deadde5b38d76617bbfb0dc78b0c5f40d0c94f514836056d734a1e223fd3"},"schema_version":"1.0","source":{"id":"2302.08545","kind":"arxiv","version":2}},"canonical_sha256":"dc123c4b5eb673364cb3b96446bcae7a6d96630ae77f0abe134e527ad53a3c69","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dc123c4b5eb673364cb3b96446bcae7a6d96630ae77f0abe134e527ad53a3c69","first_computed_at":"2026-07-05T07:52:39.709194Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:52:39.709194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kmrdhIvDFN94f4PHt6uGwa8peufOUvm0OxDe802vcyWIzoD8ZADH5cwSQyTMYZpgb2rjf33gbBZuyfmnhzI3Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:52:39.709676Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.08545","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b32d59582ca324e669aa82a9b751c23336617922528ac9cd5069aa34121ffe04","sha256:2492e9c3f0fee6ef70f97010bdc425d9dc023949dd8d03dee27285378cbd4df8"],"state_sha256":"3b870a75d8a27eb84857504a7dabe6e0564e3e484535c4aca41f57d63366a320"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DIu4eWxkk7IsoAzzVgJZnM867A9P62BTb+OykVXhRJKFepHis6IQrbYQjn9E7DVmX1A6bAZcnStWj50k7TruDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T00:39:14.985307Z","bundle_sha256":"71eb0a714c0b6f07f80171b8b6d5df325c537df98712383f3ae605c8cc492df6"}}