{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:3HSDAIB7UVULKHJ7OZZFLORDN2","short_pith_number":"pith:3HSDAIB7","canonical_record":{"source":{"id":"2402.12280","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T16:47:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"47709556b977ee482a167d4f2d63ab2a04cf048e2f85ae1c812e485397dbd9ff","abstract_canon_sha256":"9e7325209d88c2dc6482819d16d44c54c37e65dce34b48aaa27f564f144714bb"},"schema_version":"1.0"},"canonical_sha256":"d9e430203fa568b51d3f767255ba236e9ef26fd536bbb77f679b7b5d074f7ab7","source":{"kind":"arxiv","id":"2402.12280","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.12280","created_at":"2026-07-05T10:48:14Z"},{"alias_kind":"arxiv_version","alias_value":"2402.12280v2","created_at":"2026-07-05T10:48:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.12280","created_at":"2026-07-05T10:48:14Z"},{"alias_kind":"pith_short_12","alias_value":"3HSDAIB7UVUL","created_at":"2026-07-05T10:48:14Z"},{"alias_kind":"pith_short_16","alias_value":"3HSDAIB7UVULKHJ7","created_at":"2026-07-05T10:48:14Z"},{"alias_kind":"pith_short_8","alias_value":"3HSDAIB7","created_at":"2026-07-05T10:48:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:3HSDAIB7UVULKHJ7OZZFLORDN2","target":"record","payload":{"canonical_record":{"source":{"id":"2402.12280","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T16:47:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"47709556b977ee482a167d4f2d63ab2a04cf048e2f85ae1c812e485397dbd9ff","abstract_canon_sha256":"9e7325209d88c2dc6482819d16d44c54c37e65dce34b48aaa27f564f144714bb"},"schema_version":"1.0"},"canonical_sha256":"d9e430203fa568b51d3f767255ba236e9ef26fd536bbb77f679b7b5d074f7ab7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:48:14.690565Z","signature_b64":"Hse/X7u1gqn7x+umFQ93521vFTcZJIQCWjB1PFbdAZHhIdklMoDxb0Hu3vhJj1wivp+j+nIPG0btQsC8Sk92Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9e430203fa568b51d3f767255ba236e9ef26fd536bbb77f679b7b5d074f7ab7","last_reissued_at":"2026-07-05T10:48:14.690091Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:48:14.690091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.12280","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":"2026-07-05T10:48:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pCegGYZsGqArHDfyQxa8pB1Uj0honUDoq/KPoQzzWpO96OGjwMG7RuwVPKdSef7uqarPJ0YTyvuVF0XYe9gGAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T06:54:27.204631Z"},"content_sha256":"e73f5cefe67000c086be1ca8ac488662a4661423918f4ce098697170ff27866e","schema_version":"1.0","event_id":"sha256:e73f5cefe67000c086be1ca8ac488662a4661423918f4ce098697170ff27866e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:3HSDAIB7UVULKHJ7OZZFLORDN2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Plato: Plan to Efficiently Decode for Large Language Model Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Atul Prakash, Danyang Zhuo, Feng Qian, Haizhong Zheng, Matthew Lentz, Qingzhao Zhang, Shuowei Jin, Xueshen Liu, Yongji Wu, Z. Morley Mao","submitted_at":"2024-02-19T16:47:04Z","abstract_excerpt":"Large language models (LLMs) have achieved remarkable success in natural language tasks, but their inference incurs substantial computational and memory overhead. To improve efficiency, parallel decoding methods like Skeleton-of-Thought (SoT) decompose prompts into sub-problems for concurrent processing. However, these methods significantly compromise answer quality by treating semantically linked sub-problems as independent. We propose Plato, a novel approach that co-designs algorithms and systems for semantic-aware parallel decoding. Plato leverages LLMs to organize sub-problems into a depen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.12280","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/2402.12280/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":"2026-07-05T10:48:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0227Jo5z316pBOWro4vx3ac6kcBcr0nkoIVa1RYhLTJWIlhXLjc+1Pq90lXO+Ds/lArK6zJa3iqDEv8T0cv6Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T06:54:27.205298Z"},"content_sha256":"105ad8de466b459293fb7765f54e77323067596da7db0f2bec5fdb7b85371678","schema_version":"1.0","event_id":"sha256:105ad8de466b459293fb7765f54e77323067596da7db0f2bec5fdb7b85371678"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3HSDAIB7UVULKHJ7OZZFLORDN2/bundle.json","state_url":"https://pith.science/pith/3HSDAIB7UVULKHJ7OZZFLORDN2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3HSDAIB7UVULKHJ7OZZFLORDN2/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":"2026-08-04T06:54:27Z","links":{"resolver":"https://pith.science/pith/3HSDAIB7UVULKHJ7OZZFLORDN2","bundle":"https://pith.science/pith/3HSDAIB7UVULKHJ7OZZFLORDN2/bundle.json","state":"https://pith.science/pith/3HSDAIB7UVULKHJ7OZZFLORDN2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3HSDAIB7UVULKHJ7OZZFLORDN2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:3HSDAIB7UVULKHJ7OZZFLORDN2","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":"9e7325209d88c2dc6482819d16d44c54c37e65dce34b48aaa27f564f144714bb","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T16:47:04Z","title_canon_sha256":"47709556b977ee482a167d4f2d63ab2a04cf048e2f85ae1c812e485397dbd9ff"},"schema_version":"1.0","source":{"id":"2402.12280","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.12280","created_at":"2026-07-05T10:48:14Z"},{"alias_kind":"arxiv_version","alias_value":"2402.12280v2","created_at":"2026-07-05T10:48:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.12280","created_at":"2026-07-05T10:48:14Z"},{"alias_kind":"pith_short_12","alias_value":"3HSDAIB7UVUL","created_at":"2026-07-05T10:48:14Z"},{"alias_kind":"pith_short_16","alias_value":"3HSDAIB7UVULKHJ7","created_at":"2026-07-05T10:48:14Z"},{"alias_kind":"pith_short_8","alias_value":"3HSDAIB7","created_at":"2026-07-05T10:48:14Z"}],"graph_snapshots":[{"event_id":"sha256:105ad8de466b459293fb7765f54e77323067596da7db0f2bec5fdb7b85371678","target":"graph","created_at":"2026-07-05T10:48:14Z","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/2402.12280/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have achieved remarkable success in natural language tasks, but their inference incurs substantial computational and memory overhead. To improve efficiency, parallel decoding methods like Skeleton-of-Thought (SoT) decompose prompts into sub-problems for concurrent processing. However, these methods significantly compromise answer quality by treating semantically linked sub-problems as independent. We propose Plato, a novel approach that co-designs algorithms and systems for semantic-aware parallel decoding. Plato leverages LLMs to organize sub-problems into a depen","authors_text":"Atul Prakash, Danyang Zhuo, Feng Qian, Haizhong Zheng, Matthew Lentz, Qingzhao Zhang, Shuowei Jin, Xueshen Liu, Yongji Wu, Z. Morley Mao","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T16:47:04Z","title":"Plato: Plan to Efficiently Decode for Large Language Model Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.12280","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:e73f5cefe67000c086be1ca8ac488662a4661423918f4ce098697170ff27866e","target":"record","created_at":"2026-07-05T10:48:14Z","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":"9e7325209d88c2dc6482819d16d44c54c37e65dce34b48aaa27f564f144714bb","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T16:47:04Z","title_canon_sha256":"47709556b977ee482a167d4f2d63ab2a04cf048e2f85ae1c812e485397dbd9ff"},"schema_version":"1.0","source":{"id":"2402.12280","kind":"arxiv","version":2}},"canonical_sha256":"d9e430203fa568b51d3f767255ba236e9ef26fd536bbb77f679b7b5d074f7ab7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d9e430203fa568b51d3f767255ba236e9ef26fd536bbb77f679b7b5d074f7ab7","first_computed_at":"2026-07-05T10:48:14.690091Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:48:14.690091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Hse/X7u1gqn7x+umFQ93521vFTcZJIQCWjB1PFbdAZHhIdklMoDxb0Hu3vhJj1wivp+j+nIPG0btQsC8Sk92Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:48:14.690565Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.12280","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e73f5cefe67000c086be1ca8ac488662a4661423918f4ce098697170ff27866e","sha256:105ad8de466b459293fb7765f54e77323067596da7db0f2bec5fdb7b85371678"],"state_sha256":"187822d6d286bdcca640babec5c2f72dbacb00b1be62a841d3213becfd4b41a4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RGEMyN0n0sptdTGvukkmwXsJXFS2FiScJDKlRBQd+OJPNvZt4Jrwwndh4C+eVeKkJVhqwGtGufwGLC17E6JlAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T06:54:27.210961Z","bundle_sha256":"9d21571702beb27467365205269024449af124cfdc33ba2343c9a63dddd66b0f"}}