{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:OQKJTO36Y4IT4BQGHBXXPXXX42","short_pith_number":"pith:OQKJTO36","canonical_record":{"source":{"id":"2606.13287","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-11T12:43:53Z","cross_cats_sorted":["cs.DC","math.OC"],"title_canon_sha256":"b6c5ae264735b13eff5fa36ce52768c9048b2a6497d12d7ebdd2d4370465168c","abstract_canon_sha256":"75999b1c109f33f8284c9066320338674be6df4c432702269b9ccd62f7ac8a46"},"schema_version":"1.0"},"canonical_sha256":"741499bb7ec7113e0606386f77def7e685b09bcae152b2cc92503df67ba5c899","source":{"kind":"arxiv","id":"2606.13287","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.13287","created_at":"2026-06-12T01:09:50Z"},{"alias_kind":"arxiv_version","alias_value":"2606.13287v1","created_at":"2026-06-12T01:09:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.13287","created_at":"2026-06-12T01:09:50Z"},{"alias_kind":"pith_short_12","alias_value":"OQKJTO36Y4IT","created_at":"2026-06-12T01:09:50Z"},{"alias_kind":"pith_short_16","alias_value":"OQKJTO36Y4IT4BQG","created_at":"2026-06-12T01:09:50Z"},{"alias_kind":"pith_short_8","alias_value":"OQKJTO36","created_at":"2026-06-12T01:09:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:OQKJTO36Y4IT4BQGHBXXPXXX42","target":"record","payload":{"canonical_record":{"source":{"id":"2606.13287","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-11T12:43:53Z","cross_cats_sorted":["cs.DC","math.OC"],"title_canon_sha256":"b6c5ae264735b13eff5fa36ce52768c9048b2a6497d12d7ebdd2d4370465168c","abstract_canon_sha256":"75999b1c109f33f8284c9066320338674be6df4c432702269b9ccd62f7ac8a46"},"schema_version":"1.0"},"canonical_sha256":"741499bb7ec7113e0606386f77def7e685b09bcae152b2cc92503df67ba5c899","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T01:09:50.699686Z","signature_b64":"x+1P4luuUFJddjpnir2SLGTEmGMFC5OMrrwKPFrWBZSWEJ8rQUeq902FQHlaVFskyn+QZWp5gFRMpout76kYCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"741499bb7ec7113e0606386f77def7e685b09bcae152b2cc92503df67ba5c899","last_reissued_at":"2026-06-12T01:09:50.698891Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T01:09:50.698891Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.13287","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-12T01:09:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PTIcJcQ6nK/+v4j7rHZDw/e6zyq/tQjya2j/xFtN9r/By3DYlvHYwn2H+q0LzcMMA1DZuwtKDYSxe266gqUMBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T15:45:27.530905Z"},"content_sha256":"45c6b039dcba23391d7afc4e6ecda2e485ac5773b63bc30672d8df445326af5b","schema_version":"1.0","event_id":"sha256:45c6b039dcba23391d7afc4e6ecda2e485ac5773b63bc30672d8df445326af5b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:OQKJTO36Y4IT4BQGHBXXPXXX42","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC","math.OC"],"primary_cat":"cs.LG","authors_text":"Mikael Johansson, Samuel Erickson","submitted_at":"2026-06-11T12:43:53Z","abstract_excerpt":"In modern machine learning, parallelization of training is an important strategy for increasing scale. Asynchronous stochastic gradient descent (ASGD), which maximizes the utilization of available hardware by avoiding waiting for slow workers. However, with constant step sizes, the convergence of ASGD is nonetheless affected negatively by slow workers due to large delays in updates. At the same time, it has been empirically observed in asynchronous training of deep learning models that gradient clipping \"stabilizes\" training. In this work, we provide a theoretical justification for this behavi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.13287","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/2606.13287/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":"2026-06-12T01:09:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7mpySePDD6jxJvJOxpFJIFR6s1BTeagl8iTOn31OcxIF37FhvjVGbcI21EaJKY5fLBHOiP2LN10VWW0/LPIDCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T15:45:27.531241Z"},"content_sha256":"40f6c146bcc387b14f0b2f9716f893d26fb6b54bc3bbf12c3e2205fbb965aadc","schema_version":"1.0","event_id":"sha256:40f6c146bcc387b14f0b2f9716f893d26fb6b54bc3bbf12c3e2205fbb965aadc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OQKJTO36Y4IT4BQGHBXXPXXX42/bundle.json","state_url":"https://pith.science/pith/OQKJTO36Y4IT4BQGHBXXPXXX42/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OQKJTO36Y4IT4BQGHBXXPXXX42/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":"2026-08-14T15:45:27Z","links":{"resolver":"https://pith.science/pith/OQKJTO36Y4IT4BQGHBXXPXXX42","bundle":"https://pith.science/pith/OQKJTO36Y4IT4BQGHBXXPXXX42/bundle.json","state":"https://pith.science/pith/OQKJTO36Y4IT4BQGHBXXPXXX42/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OQKJTO36Y4IT4BQGHBXXPXXX42/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:OQKJTO36Y4IT4BQGHBXXPXXX42","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":"75999b1c109f33f8284c9066320338674be6df4c432702269b9ccd62f7ac8a46","cross_cats_sorted":["cs.DC","math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-11T12:43:53Z","title_canon_sha256":"b6c5ae264735b13eff5fa36ce52768c9048b2a6497d12d7ebdd2d4370465168c"},"schema_version":"1.0","source":{"id":"2606.13287","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.13287","created_at":"2026-06-12T01:09:50Z"},{"alias_kind":"arxiv_version","alias_value":"2606.13287v1","created_at":"2026-06-12T01:09:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.13287","created_at":"2026-06-12T01:09:50Z"},{"alias_kind":"pith_short_12","alias_value":"OQKJTO36Y4IT","created_at":"2026-06-12T01:09:50Z"},{"alias_kind":"pith_short_16","alias_value":"OQKJTO36Y4IT4BQG","created_at":"2026-06-12T01:09:50Z"},{"alias_kind":"pith_short_8","alias_value":"OQKJTO36","created_at":"2026-06-12T01:09:50Z"}],"graph_snapshots":[{"event_id":"sha256:40f6c146bcc387b14f0b2f9716f893d26fb6b54bc3bbf12c3e2205fbb965aadc","target":"graph","created_at":"2026-06-12T01:09:50Z","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/2606.13287/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In modern machine learning, parallelization of training is an important strategy for increasing scale. Asynchronous stochastic gradient descent (ASGD), which maximizes the utilization of available hardware by avoiding waiting for slow workers. However, with constant step sizes, the convergence of ASGD is nonetheless affected negatively by slow workers due to large delays in updates. At the same time, it has been empirically observed in asynchronous training of deep learning models that gradient clipping \"stabilizes\" training. In this work, we provide a theoretical justification for this behavi","authors_text":"Mikael Johansson, Samuel Erickson","cross_cats":["cs.DC","math.OC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-11T12:43:53Z","title":"Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.13287","kind":"arxiv","version":1},"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":"sha256:45c6b039dcba23391d7afc4e6ecda2e485ac5773b63bc30672d8df445326af5b","target":"record","created_at":"2026-06-12T01:09:50Z","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":"75999b1c109f33f8284c9066320338674be6df4c432702269b9ccd62f7ac8a46","cross_cats_sorted":["cs.DC","math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-11T12:43:53Z","title_canon_sha256":"b6c5ae264735b13eff5fa36ce52768c9048b2a6497d12d7ebdd2d4370465168c"},"schema_version":"1.0","source":{"id":"2606.13287","kind":"arxiv","version":1}},"canonical_sha256":"741499bb7ec7113e0606386f77def7e685b09bcae152b2cc92503df67ba5c899","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"741499bb7ec7113e0606386f77def7e685b09bcae152b2cc92503df67ba5c899","first_computed_at":"2026-06-12T01:09:50.698891Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-12T01:09:50.698891Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x+1P4luuUFJddjpnir2SLGTEmGMFC5OMrrwKPFrWBZSWEJ8rQUeq902FQHlaVFskyn+QZWp5gFRMpout76kYCQ==","signature_status":"signed_v1","signed_at":"2026-06-12T01:09:50.699686Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.13287","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:45c6b039dcba23391d7afc4e6ecda2e485ac5773b63bc30672d8df445326af5b","sha256:40f6c146bcc387b14f0b2f9716f893d26fb6b54bc3bbf12c3e2205fbb965aadc"],"state_sha256":"e4afe8c2fc28f8f648e038ff41bd83dd7abb42697194065a98c2f5f6432177ab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SJJVolXA9Qke3diTP/gihC0o6RM4l/H5Oj/wZkpw208UFOvsCsaZ1pheI/pNBvg2JfcwM/F96TYaVrgWXNowDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T15:45:27.534372Z","bundle_sha256":"c760b03c86a50a7100ccc9f983c0bf85f43291f9dc7c29f2af59ee8294e94476"}}