{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:5O2NPYNFSYL6BZQQD6K3Q65OON","short_pith_number":"pith:5O2NPYNF","canonical_record":{"source":{"id":"2607.13332","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-14T23:32:18Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"790f057848dbed3f571dab60b53d50ce36d295ec4fbbbd35a2b658b3ca664521","abstract_canon_sha256":"bb40de8aa94d54c4d244f55ce020bf148a77d4704c79c5e63e8a4d36431d29a4"},"schema_version":"1.0"},"canonical_sha256":"ebb4d7e1a59617e0e6101f95b87bae73690b944a6231d653cfad94ca5bd280aa","source":{"kind":"arxiv","id":"2607.13332","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.13332","created_at":"2026-07-16T00:22:12Z"},{"alias_kind":"arxiv_version","alias_value":"2607.13332v1","created_at":"2026-07-16T00:22:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.13332","created_at":"2026-07-16T00:22:12Z"},{"alias_kind":"pith_short_12","alias_value":"5O2NPYNFSYL6","created_at":"2026-07-16T00:22:12Z"},{"alias_kind":"pith_short_16","alias_value":"5O2NPYNFSYL6BZQQ","created_at":"2026-07-16T00:22:12Z"},{"alias_kind":"pith_short_8","alias_value":"5O2NPYNF","created_at":"2026-07-16T00:22:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:5O2NPYNFSYL6BZQQD6K3Q65OON","target":"record","payload":{"canonical_record":{"source":{"id":"2607.13332","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-14T23:32:18Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"790f057848dbed3f571dab60b53d50ce36d295ec4fbbbd35a2b658b3ca664521","abstract_canon_sha256":"bb40de8aa94d54c4d244f55ce020bf148a77d4704c79c5e63e8a4d36431d29a4"},"schema_version":"1.0"},"canonical_sha256":"ebb4d7e1a59617e0e6101f95b87bae73690b944a6231d653cfad94ca5bd280aa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-16T00:22:12.555509Z","signature_b64":"qWxw+45Sf8Xd1JYYk+UV/PJc20gXLodcu3SqaHiHHH+5M30DtPudZeqkTI5iEUqqNdiYeUzmWMnexrWuARKCCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebb4d7e1a59617e0e6101f95b87bae73690b944a6231d653cfad94ca5bd280aa","last_reissued_at":"2026-07-16T00:22:12.554734Z","signature_status":"signed_v1","first_computed_at":"2026-07-16T00:22:12.554734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.13332","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":"2026-07-16T00:22:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MhBvhqTsNYX6JsUSqeWOx3HZ51EVtDb26x6Qi4Gtoq/6RY1eDNowPzSv4W9orZ8E5bHzM7pDDjiCy3s+m2KJAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T01:56:13.318906Z"},"content_sha256":"316e1ddd150bcbb113c1f2405f601fb39c208a3e62de10ceb1ab503c49bc04fd","schema_version":"1.0","event_id":"sha256:316e1ddd150bcbb113c1f2405f601fb39c208a3e62de10ceb1ab503c49bc04fd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:5O2NPYNFSYL6BZQQD6K3Q65OON","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Agora: Collective and Permissionless Internet-Scale Pretraining of Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Alexander Long, Chamin Hewa Koneputugodage, Gil Avraham, Hadi Mohaghegh Dolatabadi, Harry Xi, James Snewin, Karol Pajak, Rodney O'Donnell, Sameera Ramasinghe, Shamane Siriwardhana, Thalaiyasingam Ajanthan, Violetta Shevchenko","submitted_at":"2026-07-14T23:32:18Z","abstract_excerpt":"Training large language models at the multi-billion to trillion parameter scale is confined to datacenters, where data-parallel (DP) and model-parallel (MP) techniques presume homogeneous accelerators, high-speed interconnects, and a single orchestrating entity. Frontier model development is thereby concentrated among the few groups able to assemble such clusters. Meanwhile, an enormous pool of compute remains unusable for training: consumer and professional GPUs that are heterogeneous, preemptible, individually owned, and connected only by the internet. We present Agora, a system that makes e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.13332","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/2607.13332/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":"2026-07-16T00:22:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gJxOPbw6TFOhsg830qwUKTn7pgHCdYYc0ugSsNCqlW8IxLm6oNz+2JadCgvKOTk7MD3rQURBN/OXUJnYv3ALAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T01:56:13.319305Z"},"content_sha256":"8b5522ffd604c6ab800317ccfc22c41e2d976716b7137bb06957c7fcfba36f10","schema_version":"1.0","event_id":"sha256:8b5522ffd604c6ab800317ccfc22c41e2d976716b7137bb06957c7fcfba36f10"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5O2NPYNFSYL6BZQQD6K3Q65OON/bundle.json","state_url":"https://pith.science/pith/5O2NPYNFSYL6BZQQD6K3Q65OON/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5O2NPYNFSYL6BZQQD6K3Q65OON/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":"2026-08-05T01:56:13Z","links":{"resolver":"https://pith.science/pith/5O2NPYNFSYL6BZQQD6K3Q65OON","bundle":"https://pith.science/pith/5O2NPYNFSYL6BZQQD6K3Q65OON/bundle.json","state":"https://pith.science/pith/5O2NPYNFSYL6BZQQD6K3Q65OON/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5O2NPYNFSYL6BZQQD6K3Q65OON/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:5O2NPYNFSYL6BZQQD6K3Q65OON","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":"bb40de8aa94d54c4d244f55ce020bf148a77d4704c79c5e63e8a4d36431d29a4","cross_cats_sorted":["cs.DC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-14T23:32:18Z","title_canon_sha256":"790f057848dbed3f571dab60b53d50ce36d295ec4fbbbd35a2b658b3ca664521"},"schema_version":"1.0","source":{"id":"2607.13332","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.13332","created_at":"2026-07-16T00:22:12Z"},{"alias_kind":"arxiv_version","alias_value":"2607.13332v1","created_at":"2026-07-16T00:22:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.13332","created_at":"2026-07-16T00:22:12Z"},{"alias_kind":"pith_short_12","alias_value":"5O2NPYNFSYL6","created_at":"2026-07-16T00:22:12Z"},{"alias_kind":"pith_short_16","alias_value":"5O2NPYNFSYL6BZQQ","created_at":"2026-07-16T00:22:12Z"},{"alias_kind":"pith_short_8","alias_value":"5O2NPYNF","created_at":"2026-07-16T00:22:12Z"}],"graph_snapshots":[{"event_id":"sha256:8b5522ffd604c6ab800317ccfc22c41e2d976716b7137bb06957c7fcfba36f10","target":"graph","created_at":"2026-07-16T00:22:12Z","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/2607.13332/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Training large language models at the multi-billion to trillion parameter scale is confined to datacenters, where data-parallel (DP) and model-parallel (MP) techniques presume homogeneous accelerators, high-speed interconnects, and a single orchestrating entity. Frontier model development is thereby concentrated among the few groups able to assemble such clusters. Meanwhile, an enormous pool of compute remains unusable for training: consumer and professional GPUs that are heterogeneous, preemptible, individually owned, and connected only by the internet. We present Agora, a system that makes e","authors_text":"Alexander Long, Chamin Hewa Koneputugodage, Gil Avraham, Hadi Mohaghegh Dolatabadi, Harry Xi, James Snewin, Karol Pajak, Rodney O'Donnell, Sameera Ramasinghe, Shamane Siriwardhana, Thalaiyasingam Ajanthan, Violetta Shevchenko","cross_cats":["cs.DC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-14T23:32:18Z","title":"Agora: Collective and Permissionless Internet-Scale Pretraining of Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.13332","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":"sha256:316e1ddd150bcbb113c1f2405f601fb39c208a3e62de10ceb1ab503c49bc04fd","target":"record","created_at":"2026-07-16T00:22:12Z","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":"bb40de8aa94d54c4d244f55ce020bf148a77d4704c79c5e63e8a4d36431d29a4","cross_cats_sorted":["cs.DC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-14T23:32:18Z","title_canon_sha256":"790f057848dbed3f571dab60b53d50ce36d295ec4fbbbd35a2b658b3ca664521"},"schema_version":"1.0","source":{"id":"2607.13332","kind":"arxiv","version":1}},"canonical_sha256":"ebb4d7e1a59617e0e6101f95b87bae73690b944a6231d653cfad94ca5bd280aa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ebb4d7e1a59617e0e6101f95b87bae73690b944a6231d653cfad94ca5bd280aa","first_computed_at":"2026-07-16T00:22:12.554734Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-16T00:22:12.554734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qWxw+45Sf8Xd1JYYk+UV/PJc20gXLodcu3SqaHiHHH+5M30DtPudZeqkTI5iEUqqNdiYeUzmWMnexrWuARKCCw==","signature_status":"signed_v1","signed_at":"2026-07-16T00:22:12.555509Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.13332","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:316e1ddd150bcbb113c1f2405f601fb39c208a3e62de10ceb1ab503c49bc04fd","sha256:8b5522ffd604c6ab800317ccfc22c41e2d976716b7137bb06957c7fcfba36f10"],"state_sha256":"e32064d110bc4831072317a73830021f6f133f4e8733d7c9e2c9e9d728bb97ee"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JI6ykka2vJkc4a9qRQ2aw3IkhavdMiNKmoKQZuHh91CTJAKIm0BjsK08LYmiYjj/q7xdje0VJg4QxScYgCLZAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T01:56:13.321845Z","bundle_sha256":"7c10b7432fb388dae513c2b9d7ed391ecad6204f0afc6720f3be6cbb6add02b2"}}