{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:67UY2WMHGVWLP36EJ73T34IJ7Q","short_pith_number":"pith:67UY2WMH","canonical_record":{"source":{"id":"2502.14856","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-20T18:58:10Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9facc4304dd3b9e91682897894a028dbd2395f6002aba2f4c6edf718fa0597d5","abstract_canon_sha256":"c7761754b2bfd7de435b3f2a420ec4b2a4c2aeec6a30dad71796a581ed7e4229"},"schema_version":"1.0"},"canonical_sha256":"f7e98d5987356cb7efc44ff73df109fc35c228f253e5451871b75b8368d9d69f","source":{"kind":"arxiv","id":"2502.14856","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.14856","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"arxiv_version","alias_value":"2502.14856v2","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.14856","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"pith_short_12","alias_value":"67UY2WMHGVWL","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"pith_short_16","alias_value":"67UY2WMHGVWLP36E","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"pith_short_8","alias_value":"67UY2WMH","created_at":"2026-07-05T10:28:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:67UY2WMHGVWLP36EJ73T34IJ7Q","target":"record","payload":{"canonical_record":{"source":{"id":"2502.14856","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-20T18:58:10Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9facc4304dd3b9e91682897894a028dbd2395f6002aba2f4c6edf718fa0597d5","abstract_canon_sha256":"c7761754b2bfd7de435b3f2a420ec4b2a4c2aeec6a30dad71796a581ed7e4229"},"schema_version":"1.0"},"canonical_sha256":"f7e98d5987356cb7efc44ff73df109fc35c228f253e5451871b75b8368d9d69f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:28:36.630378Z","signature_b64":"es+LyL83q4XYdXkElq6tMGjqzEYiUwCC7+P8/mgF4j02wZdklMfreJBHDyqZFZ0ike1388jUvl6IdtacLaRmBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7e98d5987356cb7efc44ff73df109fc35c228f253e5451871b75b8368d9d69f","last_reissued_at":"2026-07-05T10:28:36.629492Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:28:36.629492Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.14856","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":"2026-07-05T10:28:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"joa57g+6PT21kzmeGAjtgwyr9ITHYyyDXMiaK+Sy22cPktIp3MnPErhmVnofAqEGmZqmQWk76eRNFwPraJO8DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T12:57:22.591294Z"},"content_sha256":"a0fd49e81519377b0824f687adbde4469faaced50e57dad5a0e89969aac33842","schema_version":"1.0","event_id":"sha256:a0fd49e81519377b0824f687adbde4469faaced50e57dad5a0e89969aac33842"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:67UY2WMHGVWLP36EJ73T34IJ7Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Ao Sun, Jianyong Wang, Kaihuo Zhang, Maosong Sun, Tengyu Pan, Weilin Zhao, Weilun Zhao, Xu Han, Yudi Zhang, Yuxiang Huang, Yuxuan Li, Zhiyuan Liu","submitted_at":"2025-02-20T18:58:10Z","abstract_excerpt":"Speculative sampling has emerged as an important technique for accelerating the auto-regressive generation process of large language models (LLMs) by utilizing a draft-then-verify mechanism to produce multiple tokens per forward pass. While state-of-the-art speculative sampling methods use only a single layer and a language modeling (LM) head as the draft model to achieve impressive layer compression, their efficiency gains are substantially reduced for large-vocabulary LLMs, such as Llama-3-8B with a vocabulary of 128k tokens. To address this, we present FR-Spec, a frequency-ranked speculativ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.14856","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/2502.14856/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":"2026-07-05T10:28:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Aqg4qEdM3bammGdxYuzhk9Jsd3r99jb7xoxT+Kba+GHkZUyab/VLPj1IQ+6akKA8cLw99kN2OGWYmMNM2U0cAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T12:57:22.592314Z"},"content_sha256":"edecd8f99a8b5549c487226da88d13dc5f3726f236e146f71145a04d5a083f6a","schema_version":"1.0","event_id":"sha256:edecd8f99a8b5549c487226da88d13dc5f3726f236e146f71145a04d5a083f6a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/67UY2WMHGVWLP36EJ73T34IJ7Q/bundle.json","state_url":"https://pith.science/pith/67UY2WMHGVWLP36EJ73T34IJ7Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/67UY2WMHGVWLP36EJ73T34IJ7Q/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":"2026-08-07T12:57:22Z","links":{"resolver":"https://pith.science/pith/67UY2WMHGVWLP36EJ73T34IJ7Q","bundle":"https://pith.science/pith/67UY2WMHGVWLP36EJ73T34IJ7Q/bundle.json","state":"https://pith.science/pith/67UY2WMHGVWLP36EJ73T34IJ7Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/67UY2WMHGVWLP36EJ73T34IJ7Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:67UY2WMHGVWLP36EJ73T34IJ7Q","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":"c7761754b2bfd7de435b3f2a420ec4b2a4c2aeec6a30dad71796a581ed7e4229","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-20T18:58:10Z","title_canon_sha256":"9facc4304dd3b9e91682897894a028dbd2395f6002aba2f4c6edf718fa0597d5"},"schema_version":"1.0","source":{"id":"2502.14856","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.14856","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"arxiv_version","alias_value":"2502.14856v2","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.14856","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"pith_short_12","alias_value":"67UY2WMHGVWL","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"pith_short_16","alias_value":"67UY2WMHGVWLP36E","created_at":"2026-07-05T10:28:36Z"},{"alias_kind":"pith_short_8","alias_value":"67UY2WMH","created_at":"2026-07-05T10:28:36Z"}],"graph_snapshots":[{"event_id":"sha256:edecd8f99a8b5549c487226da88d13dc5f3726f236e146f71145a04d5a083f6a","target":"graph","created_at":"2026-07-05T10:28:36Z","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/2502.14856/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Speculative sampling has emerged as an important technique for accelerating the auto-regressive generation process of large language models (LLMs) by utilizing a draft-then-verify mechanism to produce multiple tokens per forward pass. While state-of-the-art speculative sampling methods use only a single layer and a language modeling (LM) head as the draft model to achieve impressive layer compression, their efficiency gains are substantially reduced for large-vocabulary LLMs, such as Llama-3-8B with a vocabulary of 128k tokens. To address this, we present FR-Spec, a frequency-ranked speculativ","authors_text":"Ao Sun, Jianyong Wang, Kaihuo Zhang, Maosong Sun, Tengyu Pan, Weilin Zhao, Weilun Zhao, Xu Han, Yudi Zhang, Yuxiang Huang, Yuxuan Li, Zhiyuan Liu","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-20T18:58:10Z","title":"FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.14856","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:a0fd49e81519377b0824f687adbde4469faaced50e57dad5a0e89969aac33842","target":"record","created_at":"2026-07-05T10:28:36Z","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":"c7761754b2bfd7de435b3f2a420ec4b2a4c2aeec6a30dad71796a581ed7e4229","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-20T18:58:10Z","title_canon_sha256":"9facc4304dd3b9e91682897894a028dbd2395f6002aba2f4c6edf718fa0597d5"},"schema_version":"1.0","source":{"id":"2502.14856","kind":"arxiv","version":2}},"canonical_sha256":"f7e98d5987356cb7efc44ff73df109fc35c228f253e5451871b75b8368d9d69f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f7e98d5987356cb7efc44ff73df109fc35c228f253e5451871b75b8368d9d69f","first_computed_at":"2026-07-05T10:28:36.629492Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:28:36.629492Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"es+LyL83q4XYdXkElq6tMGjqzEYiUwCC7+P8/mgF4j02wZdklMfreJBHDyqZFZ0ike1388jUvl6IdtacLaRmBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:28:36.630378Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.14856","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a0fd49e81519377b0824f687adbde4469faaced50e57dad5a0e89969aac33842","sha256:edecd8f99a8b5549c487226da88d13dc5f3726f236e146f71145a04d5a083f6a"],"state_sha256":"bc090bf963a5bed4373065d62716f70c809b0953bf2eef3f1c2c432dd5dd0dbb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sOJzEdxCOjAS16kLI6OoHK9T02B7Gc6iLvQRDOvpbnyeknCXHlT4JOJjpp4jn/dPGH6htEszJfEENHqugMTXAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T12:57:22.599795Z","bundle_sha256":"0660a0952fbc48b0a75ca5211686e5e85f95ac0253f9e6fb0d175f4385bbf1a1"}}