{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:OP7QAYCZNWGAXPO2DQLP6N4DUP","short_pith_number":"pith:OP7QAYCZ","canonical_record":{"source":{"id":"2411.07641","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-12T08:46:43Z","cross_cats_sorted":[],"title_canon_sha256":"d0d2c1b1937187104ce4d1ea35a8bdbd4cbba2f45aa6203daa8ea2c4de8f6f9c","abstract_canon_sha256":"5db941b5a28337e981b6570321305c2c14b3dc8782f913292761018009ee8625"},"schema_version":"1.0"},"canonical_sha256":"73ff0060596d8c0bbdda1c16ff3783a3f4a254712d8ac3c4663ddb6f248ead7a","source":{"kind":"arxiv","id":"2411.07641","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.07641","created_at":"2026-07-05T09:34:26Z"},{"alias_kind":"arxiv_version","alias_value":"2411.07641v1","created_at":"2026-07-05T09:34:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.07641","created_at":"2026-07-05T09:34:26Z"},{"alias_kind":"pith_short_12","alias_value":"OP7QAYCZNWGA","created_at":"2026-07-05T09:34:26Z"},{"alias_kind":"pith_short_16","alias_value":"OP7QAYCZNWGAXPO2","created_at":"2026-07-05T09:34:26Z"},{"alias_kind":"pith_short_8","alias_value":"OP7QAYCZ","created_at":"2026-07-05T09:34:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:OP7QAYCZNWGAXPO2DQLP6N4DUP","target":"record","payload":{"canonical_record":{"source":{"id":"2411.07641","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-12T08:46:43Z","cross_cats_sorted":[],"title_canon_sha256":"d0d2c1b1937187104ce4d1ea35a8bdbd4cbba2f45aa6203daa8ea2c4de8f6f9c","abstract_canon_sha256":"5db941b5a28337e981b6570321305c2c14b3dc8782f913292761018009ee8625"},"schema_version":"1.0"},"canonical_sha256":"73ff0060596d8c0bbdda1c16ff3783a3f4a254712d8ac3c4663ddb6f248ead7a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:34:26.665068Z","signature_b64":"JX5kI910+mu0ZyHOYpUcMHCUfqviTwSgm1FAoPvz6u8008MZtcxNm69jmpbFX3SsyxvZO9jg0bAy+mAXD6BIDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73ff0060596d8c0bbdda1c16ff3783a3f4a254712d8ac3c4663ddb6f248ead7a","last_reissued_at":"2026-07-05T09:34:26.664571Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:34:26.664571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.07641","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":"2026-07-05T09:34:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gehN2yNXscDee2L/FQoe3McANZW5W7VqKFoYMJwOPutdHnpV/ri9QIvpquJnrwIcv89nEHtQclUb/0M2q4q2DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T09:21:27.723796Z"},"content_sha256":"3bbc4d21ac8a66792d8aa11188b0427bb006ca11935e3307c73cbd18caee51fe","schema_version":"1.0","event_id":"sha256:3bbc4d21ac8a66792d8aa11188b0427bb006ca11935e3307c73cbd18caee51fe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:OP7QAYCZNWGAXPO2DQLP6N4DUP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Top-$n\\sigma$: Not All Logits Are You Need","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chenxia Tang, Hongli Xu, Jianchun Liu, Liusheng Huang","submitted_at":"2024-11-12T08:46:43Z","abstract_excerpt":"Large language models (LLMs) typically employ greedy decoding or low-temperature sampling for reasoning tasks, reflecting a perceived trade-off between diversity and accuracy. We challenge this convention by introducing top-$n\\sigma$, a novel sampling method that operates directly on pre-softmax logits by leveraging a statistical threshold. Our key insight is that logits naturally separate into a Gaussian-distributed noisy region and a distinct informative region, enabling efficient token filtering without complex probability manipulations. Unlike existing methods (e.g., top-$p$, min-$p$) that"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.07641","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/2411.07641/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":"2026-07-05T09:34:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SZQ+BFQoqFgAwublwk0a2iqpHOkNSMGT3TdecpERvIjeWFW6jrBzCDkZNU2eHvpdJdOGE0UX1YfSY4E+siq1AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T09:21:27.724323Z"},"content_sha256":"5abcf0175cec71609e2a893586dc1d7aaa073532f744b1b4c8d331155431951e","schema_version":"1.0","event_id":"sha256:5abcf0175cec71609e2a893586dc1d7aaa073532f744b1b4c8d331155431951e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OP7QAYCZNWGAXPO2DQLP6N4DUP/bundle.json","state_url":"https://pith.science/pith/OP7QAYCZNWGAXPO2DQLP6N4DUP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OP7QAYCZNWGAXPO2DQLP6N4DUP/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":"2026-08-10T09:21:27Z","links":{"resolver":"https://pith.science/pith/OP7QAYCZNWGAXPO2DQLP6N4DUP","bundle":"https://pith.science/pith/OP7QAYCZNWGAXPO2DQLP6N4DUP/bundle.json","state":"https://pith.science/pith/OP7QAYCZNWGAXPO2DQLP6N4DUP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OP7QAYCZNWGAXPO2DQLP6N4DUP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:OP7QAYCZNWGAXPO2DQLP6N4DUP","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":"5db941b5a28337e981b6570321305c2c14b3dc8782f913292761018009ee8625","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-12T08:46:43Z","title_canon_sha256":"d0d2c1b1937187104ce4d1ea35a8bdbd4cbba2f45aa6203daa8ea2c4de8f6f9c"},"schema_version":"1.0","source":{"id":"2411.07641","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.07641","created_at":"2026-07-05T09:34:26Z"},{"alias_kind":"arxiv_version","alias_value":"2411.07641v1","created_at":"2026-07-05T09:34:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.07641","created_at":"2026-07-05T09:34:26Z"},{"alias_kind":"pith_short_12","alias_value":"OP7QAYCZNWGA","created_at":"2026-07-05T09:34:26Z"},{"alias_kind":"pith_short_16","alias_value":"OP7QAYCZNWGAXPO2","created_at":"2026-07-05T09:34:26Z"},{"alias_kind":"pith_short_8","alias_value":"OP7QAYCZ","created_at":"2026-07-05T09:34:26Z"}],"graph_snapshots":[{"event_id":"sha256:5abcf0175cec71609e2a893586dc1d7aaa073532f744b1b4c8d331155431951e","target":"graph","created_at":"2026-07-05T09:34:26Z","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/2411.07641/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) typically employ greedy decoding or low-temperature sampling for reasoning tasks, reflecting a perceived trade-off between diversity and accuracy. We challenge this convention by introducing top-$n\\sigma$, a novel sampling method that operates directly on pre-softmax logits by leveraging a statistical threshold. Our key insight is that logits naturally separate into a Gaussian-distributed noisy region and a distinct informative region, enabling efficient token filtering without complex probability manipulations. Unlike existing methods (e.g., top-$p$, min-$p$) that","authors_text":"Chenxia Tang, Hongli Xu, Jianchun Liu, Liusheng Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-12T08:46:43Z","title":"Top-$n\\sigma$: Not All Logits Are You Need"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.07641","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":"sha256:3bbc4d21ac8a66792d8aa11188b0427bb006ca11935e3307c73cbd18caee51fe","target":"record","created_at":"2026-07-05T09:34:26Z","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":"5db941b5a28337e981b6570321305c2c14b3dc8782f913292761018009ee8625","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-12T08:46:43Z","title_canon_sha256":"d0d2c1b1937187104ce4d1ea35a8bdbd4cbba2f45aa6203daa8ea2c4de8f6f9c"},"schema_version":"1.0","source":{"id":"2411.07641","kind":"arxiv","version":1}},"canonical_sha256":"73ff0060596d8c0bbdda1c16ff3783a3f4a254712d8ac3c4663ddb6f248ead7a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73ff0060596d8c0bbdda1c16ff3783a3f4a254712d8ac3c4663ddb6f248ead7a","first_computed_at":"2026-07-05T09:34:26.664571Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:34:26.664571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JX5kI910+mu0ZyHOYpUcMHCUfqviTwSgm1FAoPvz6u8008MZtcxNm69jmpbFX3SsyxvZO9jg0bAy+mAXD6BIDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:34:26.665068Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.07641","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3bbc4d21ac8a66792d8aa11188b0427bb006ca11935e3307c73cbd18caee51fe","sha256:5abcf0175cec71609e2a893586dc1d7aaa073532f744b1b4c8d331155431951e"],"state_sha256":"6a48ef28c79c2dff161861679ded244960a34cab0c4e7ee2958b66a8462f2211"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+f5twL+GpXDDHFJ4DB3JXB2M+nvc/narL82ACttd05qQ6BTQ7Ct+vEOyulNym8xYilhdbtxMHXXdg1eY8Pq8Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T09:21:27.728671Z","bundle_sha256":"4a1703cd80cabdb62cc86fb821bbdd2e224dd505f2b246a3a3b9ff635bd94fc0"}}