{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:AU5FTV63HHLALVILJPT262WTID","short_pith_number":"pith:AU5FTV63","canonical_record":{"source":{"id":"2503.07154","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-10T10:27:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b0b6f44c8f735b268997bba9152299e5e2a0684c1fd26569bb763c982a14ca86","abstract_canon_sha256":"a11d7e3b683e0299b2ffd452858c14e9a56c527b825e06ed749e915059fd789a"},"schema_version":"1.0"},"canonical_sha256":"053a59d7db39d605d50b4be7af6ad340dc4855ed7071e9117e60b12d5f94d9f4","source":{"kind":"arxiv","id":"2503.07154","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.07154","created_at":"2026-06-02T02:04:05Z"},{"alias_kind":"arxiv_version","alias_value":"2503.07154v3","created_at":"2026-06-02T02:04:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.07154","created_at":"2026-06-02T02:04:05Z"},{"alias_kind":"pith_short_12","alias_value":"AU5FTV63HHLA","created_at":"2026-06-02T02:04:05Z"},{"alias_kind":"pith_short_16","alias_value":"AU5FTV63HHLALVIL","created_at":"2026-06-02T02:04:05Z"},{"alias_kind":"pith_short_8","alias_value":"AU5FTV63","created_at":"2026-06-02T02:04:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:AU5FTV63HHLALVILJPT262WTID","target":"record","payload":{"canonical_record":{"source":{"id":"2503.07154","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-10T10:27:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b0b6f44c8f735b268997bba9152299e5e2a0684c1fd26569bb763c982a14ca86","abstract_canon_sha256":"a11d7e3b683e0299b2ffd452858c14e9a56c527b825e06ed749e915059fd789a"},"schema_version":"1.0"},"canonical_sha256":"053a59d7db39d605d50b4be7af6ad340dc4855ed7071e9117e60b12d5f94d9f4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:05.337270Z","signature_b64":"6RDGk4tAqVB0xZPNV+6Yl/til3Nsmvvf+Eye1rNBsOoQMMnAdS5++rXql2Jptlo6iGTrWbS8iWswPjDTdMLSAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"053a59d7db39d605d50b4be7af6ad340dc4855ed7071e9117e60b12d5f94d9f4","last_reissued_at":"2026-06-02T02:04:05.336764Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:05.336764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.07154","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-02T02:04:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AraxfpbY5esw0WSWqZMqr62Inusir79EKAdgBZvsjX0qxyr03E39IbcdNLaEYwb3ubPhcvbkPRBBNmbL8bnkCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T07:39:55.409239Z"},"content_sha256":"07739a048b6aaaefa0678038769700cad2755b1e4eac5ae52a6d840f28f6b7c5","schema_version":"1.0","event_id":"sha256:07739a048b6aaaefa0678038769700cad2755b1e4eac5ae52a6d840f28f6b7c5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:AU5FTV63HHLALVILJPT262WTID","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jiaming Song, Linqi Zhou","submitted_at":"2025-03-10T10:27:30Z","abstract_excerpt":"Generative pre-training is often framed through a false dichotomy between autoregressive models for discrete signals and diffusion models for continuous signals. We argue that the dichotomy is false because it conflates model family, data representation, training objective, and inference procedure. Autoregression is an inference procedure that expands a sequence through normalized conditional draws, while diffusion is a refinement procedure that repeatedly revises an existing state. The more useful contrast is therefore not autoregressive versus diffusion, but discrete tokens learned with cros"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.07154","kind":"arxiv","version":3},"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/2503.07154/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":"2026-06-02T02:04:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QE110Z5OaTR4+11ZXAz8P7aOvq8Ejf4AYPokO91dF5wA3DvAJw81Pc0j/fRsT7pFE3oQQUG+Qi6oJ6ZXmYhOAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T07:39:55.410026Z"},"content_sha256":"79ba14e5f1ee5f7d1ff0b32cb7eb9028fbb12560bbbc330eda52d431184777db","schema_version":"1.0","event_id":"sha256:79ba14e5f1ee5f7d1ff0b32cb7eb9028fbb12560bbbc330eda52d431184777db"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AU5FTV63HHLALVILJPT262WTID/bundle.json","state_url":"https://pith.science/pith/AU5FTV63HHLALVILJPT262WTID/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AU5FTV63HHLALVILJPT262WTID/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":"2026-06-10T07:39:55Z","links":{"resolver":"https://pith.science/pith/AU5FTV63HHLALVILJPT262WTID","bundle":"https://pith.science/pith/AU5FTV63HHLALVILJPT262WTID/bundle.json","state":"https://pith.science/pith/AU5FTV63HHLALVILJPT262WTID/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AU5FTV63HHLALVILJPT262WTID/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:AU5FTV63HHLALVILJPT262WTID","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":"a11d7e3b683e0299b2ffd452858c14e9a56c527b825e06ed749e915059fd789a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-10T10:27:30Z","title_canon_sha256":"b0b6f44c8f735b268997bba9152299e5e2a0684c1fd26569bb763c982a14ca86"},"schema_version":"1.0","source":{"id":"2503.07154","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.07154","created_at":"2026-06-02T02:04:05Z"},{"alias_kind":"arxiv_version","alias_value":"2503.07154v3","created_at":"2026-06-02T02:04:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.07154","created_at":"2026-06-02T02:04:05Z"},{"alias_kind":"pith_short_12","alias_value":"AU5FTV63HHLA","created_at":"2026-06-02T02:04:05Z"},{"alias_kind":"pith_short_16","alias_value":"AU5FTV63HHLALVIL","created_at":"2026-06-02T02:04:05Z"},{"alias_kind":"pith_short_8","alias_value":"AU5FTV63","created_at":"2026-06-02T02:04:05Z"}],"graph_snapshots":[{"event_id":"sha256:79ba14e5f1ee5f7d1ff0b32cb7eb9028fbb12560bbbc330eda52d431184777db","target":"graph","created_at":"2026-06-02T02:04:05Z","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/2503.07154/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generative pre-training is often framed through a false dichotomy between autoregressive models for discrete signals and diffusion models for continuous signals. We argue that the dichotomy is false because it conflates model family, data representation, training objective, and inference procedure. Autoregression is an inference procedure that expands a sequence through normalized conditional draws, while diffusion is a refinement procedure that repeatedly revises an existing state. The more useful contrast is therefore not autoregressive versus diffusion, but discrete tokens learned with cros","authors_text":"Jiaming Song, Linqi Zhou","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-10T10:27:30Z","title":"Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.07154","kind":"arxiv","version":3},"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":"sha256:07739a048b6aaaefa0678038769700cad2755b1e4eac5ae52a6d840f28f6b7c5","target":"record","created_at":"2026-06-02T02:04:05Z","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":"a11d7e3b683e0299b2ffd452858c14e9a56c527b825e06ed749e915059fd789a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-10T10:27:30Z","title_canon_sha256":"b0b6f44c8f735b268997bba9152299e5e2a0684c1fd26569bb763c982a14ca86"},"schema_version":"1.0","source":{"id":"2503.07154","kind":"arxiv","version":3}},"canonical_sha256":"053a59d7db39d605d50b4be7af6ad340dc4855ed7071e9117e60b12d5f94d9f4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"053a59d7db39d605d50b4be7af6ad340dc4855ed7071e9117e60b12d5f94d9f4","first_computed_at":"2026-06-02T02:04:05.336764Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T02:04:05.336764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6RDGk4tAqVB0xZPNV+6Yl/til3Nsmvvf+Eye1rNBsOoQMMnAdS5++rXql2Jptlo6iGTrWbS8iWswPjDTdMLSAQ==","signature_status":"signed_v1","signed_at":"2026-06-02T02:04:05.337270Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.07154","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:07739a048b6aaaefa0678038769700cad2755b1e4eac5ae52a6d840f28f6b7c5","sha256:79ba14e5f1ee5f7d1ff0b32cb7eb9028fbb12560bbbc330eda52d431184777db"],"state_sha256":"bb195ea02b6b59f174d08b77c4ab50de5c51a4bfd13796958a316ddba84b5ab4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tBJgxYUddHpWeDaNuKubXdbiYpOrjYF3WLEUVEkTQC7MrP2mpHAjLntsTFuFFBe7opvfpo5p6bdaeKi0iP4hBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T07:39:55.414440Z","bundle_sha256":"d85577d6e0eafa1a8ff08b9cf8ed12707eccb8f71c7540681200d177d91f5e8a"}}