{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4XILISKLLFJRXWXA7BHXVUXNRB","short_pith_number":"pith:4XILISKL","schema_version":"1.0","canonical_sha256":"e5d0b4494b59531bdae0f84f7ad2ed887ea5d00bdf0f707e3712ce000f4e48c6","source":{"kind":"arxiv","id":"2605.17164","version":1},"attestation_state":"computed","paper":{"title":"Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.PL"],"primary_cat":"cs.DC","authors_text":"Hanshi Sun, Jianwen Yan, Li-wen Chang, Mengtian Yang, Mingheng Wu, Zhekun Zhang","submitted_at":"2026-05-16T21:28:22Z","abstract_excerpt":"Deploying large-scale LLM training and inference with optimal performance is exceptionally challenging due to a complex design space of parallelism strategies, system optimizations, and hardware configurations. Accurate and rapid performance simulation is critical for guiding optimization efforts and system studies by validating \"what-if\" Hooker Figure hypotheses. To address this, we introduce Charon, a unified, modular, and fine-grained simulator for accurately predicting LLM performance. Experiments show Charon achieves high accuracy across different models and configurations, with an overal"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.17164","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2026-05-16T21:28:22Z","cross_cats_sorted":["cs.AI","cs.LG","cs.PL"],"title_canon_sha256":"9f4e80cecded052e86875a008513d3404705f30987709d32f5c411e26b068a43","abstract_canon_sha256":"45863d6f5187cfe6d5fb4fc39b11f09c91b44b94c986e42246ccd2c4df0ea29b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:42.829404Z","signature_b64":"8ljeZLiR7HoqKe9p09DyNk9JBABLvaK8+59pAbhjjsjrfKLpNTMax5xvPoV128Byowi4dOvNawTegMHapL9/Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5d0b4494b59531bdae0f84f7ad2ed887ea5d00bdf0f707e3712ce000f4e48c6","last_reissued_at":"2026-05-20T00:03:42.828599Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:42.828599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.PL"],"primary_cat":"cs.DC","authors_text":"Hanshi Sun, Jianwen Yan, Li-wen Chang, Mengtian Yang, Mingheng Wu, Zhekun Zhang","submitted_at":"2026-05-16T21:28:22Z","abstract_excerpt":"Deploying large-scale LLM training and inference with optimal performance is exceptionally challenging due to a complex design space of parallelism strategies, system optimizations, and hardware configurations. Accurate and rapid performance simulation is critical for guiding optimization efforts and system studies by validating \"what-if\" Hooker Figure hypotheses. To address this, we introduce Charon, a unified, modular, and fine-grained simulator for accurately predicting LLM performance. Experiments show Charon achieves high accuracy across different models and configurations, with an overal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17164","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/2605.17164/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T22:33:23.757040Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T22:01:57.985090Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"297ac14ea47740bf1c1ec6d39bd8d7b73fa54c3348924f2d618d4c578bea869e"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.17164","created_at":"2026-05-20T00:03:42.828716+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.17164v1","created_at":"2026-05-20T00:03:42.828716+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17164","created_at":"2026-05-20T00:03:42.828716+00:00"},{"alias_kind":"pith_short_12","alias_value":"4XILISKLLFJR","created_at":"2026-05-20T00:03:42.828716+00:00"},{"alias_kind":"pith_short_16","alias_value":"4XILISKLLFJRXWXA","created_at":"2026-05-20T00:03:42.828716+00:00"},{"alias_kind":"pith_short_8","alias_value":"4XILISKL","created_at":"2026-05-20T00:03:42.828716+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4XILISKLLFJRXWXA7BHXVUXNRB","json":"https://pith.science/pith/4XILISKLLFJRXWXA7BHXVUXNRB.json","graph_json":"https://pith.science/api/pith-number/4XILISKLLFJRXWXA7BHXVUXNRB/graph.json","events_json":"https://pith.science/api/pith-number/4XILISKLLFJRXWXA7BHXVUXNRB/events.json","paper":"https://pith.science/paper/4XILISKL"},"agent_actions":{"view_html":"https://pith.science/pith/4XILISKLLFJRXWXA7BHXVUXNRB","download_json":"https://pith.science/pith/4XILISKLLFJRXWXA7BHXVUXNRB.json","view_paper":"https://pith.science/paper/4XILISKL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.17164&json=true","fetch_graph":"https://pith.science/api/pith-number/4XILISKLLFJRXWXA7BHXVUXNRB/graph.json","fetch_events":"https://pith.science/api/pith-number/4XILISKLLFJRXWXA7BHXVUXNRB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4XILISKLLFJRXWXA7BHXVUXNRB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4XILISKLLFJRXWXA7BHXVUXNRB/action/storage_attestation","attest_author":"https://pith.science/pith/4XILISKLLFJRXWXA7BHXVUXNRB/action/author_attestation","sign_citation":"https://pith.science/pith/4XILISKLLFJRXWXA7BHXVUXNRB/action/citation_signature","submit_replication":"https://pith.science/pith/4XILISKLLFJRXWXA7BHXVUXNRB/action/replication_record"}},"created_at":"2026-05-20T00:03:42.828716+00:00","updated_at":"2026-05-20T00:03:42.828716+00:00"}