{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:4HKYOHBSR36CD764OHNZDAEEFN","short_pith_number":"pith:4HKYOHBS","canonical_record":{"source":{"id":"2410.13276","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-17T07:07:09Z","cross_cats_sorted":[],"title_canon_sha256":"0d7a11eefae4a9fb00d4c118ac9bb61dd4fa30c028229fb675f20fe21647bdcd","abstract_canon_sha256":"4fac78567c558bcc0c4e64684980f6601b339254f1475101fcad4126244d5e3d"},"schema_version":"1.0"},"canonical_sha256":"e1d5871c328efc21ffdc71db9180842b5f65207ecbb25eb55f7f38ccdcabef5b","source":{"kind":"arxiv","id":"2410.13276","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.13276","created_at":"2026-07-05T10:15:20Z"},{"alias_kind":"arxiv_version","alias_value":"2410.13276v4","created_at":"2026-07-05T10:15:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13276","created_at":"2026-07-05T10:15:20Z"},{"alias_kind":"pith_short_12","alias_value":"4HKYOHBSR36C","created_at":"2026-07-05T10:15:20Z"},{"alias_kind":"pith_short_16","alias_value":"4HKYOHBSR36CD764","created_at":"2026-07-05T10:15:20Z"},{"alias_kind":"pith_short_8","alias_value":"4HKYOHBS","created_at":"2026-07-05T10:15:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:4HKYOHBSR36CD764OHNZDAEEFN","target":"record","payload":{"canonical_record":{"source":{"id":"2410.13276","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-17T07:07:09Z","cross_cats_sorted":[],"title_canon_sha256":"0d7a11eefae4a9fb00d4c118ac9bb61dd4fa30c028229fb675f20fe21647bdcd","abstract_canon_sha256":"4fac78567c558bcc0c4e64684980f6601b339254f1475101fcad4126244d5e3d"},"schema_version":"1.0"},"canonical_sha256":"e1d5871c328efc21ffdc71db9180842b5f65207ecbb25eb55f7f38ccdcabef5b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:15:20.675478Z","signature_b64":"SOJoyS0p9rWmTt7S6Osh6Ikt7k1Sn/hp8klTppZxAhVCmqB4Y7+iaYK70W22ONKMq6h/nFJDP8rIod+cv7T4DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1d5871c328efc21ffdc71db9180842b5f65207ecbb25eb55f7f38ccdcabef5b","last_reissued_at":"2026-07-05T10:15:20.674946Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:15:20.674946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.13276","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:15:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6zbOenUB0stOdtdIXY9kdbHSepeT1YtPFgDjs7nWQBTy2cSCq3HpYJnY71SQGRiqQzXp1VXQusi3NS3hrvsBAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T11:41:20.415296Z"},"content_sha256":"8a8135501a0e6c42af0bbc9bff4708c194cb21bd83b415289eaf82a88f6e8861","schema_version":"1.0","event_id":"sha256:8a8135501a0e6c42af0bbc9bff4708c194cb21bd83b415289eaf82a88f6e8861"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:4HKYOHBSR36CD764OHNZDAEEFN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dayou Du, Fan Yang, Hayden Kwok-Hay So, Jiaxing Qi, Junjie Lai, Mao Yang, Peiyuan Zhou, Shijie Cao, Ting Cao, Yizhao Gao, Zhichen Zeng","submitted_at":"2024-10-17T07:07:09Z","abstract_excerpt":"Attention is the cornerstone of modern Large Language Models (LLMs). Yet its quadratic complexity hinders efficiency and scalability, especially for long-context processing. A promising approach is to leverage sparsity in attention. However, existing sparsity-based solutions predominantly rely on predefined patterns or heuristics at the attention head level, struggling to adapt dynamically to different contexts efficiently.\n  We propose SeerAttention, a simple yet effective attention mechanism that directly learns the block-level attention sparsity from the LLM itself. Inspired by the gating m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13276","kind":"arxiv","version":4},"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/2410.13276/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":"2026-07-05T10:15:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0x/3GE2HXG+EpKjABzesVeC5o/eQNwfYNlKVZLDv4KVajuZAIPweOJo8bDT16tNHpSse20MuiXlglKCGDws6CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T11:41:20.415701Z"},"content_sha256":"4e743e3847d585e10bf897b649c5ebb2903548bc8059057ee3a5ff01a9fae355","schema_version":"1.0","event_id":"sha256:4e743e3847d585e10bf897b649c5ebb2903548bc8059057ee3a5ff01a9fae355"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4HKYOHBSR36CD764OHNZDAEEFN/bundle.json","state_url":"https://pith.science/pith/4HKYOHBSR36CD764OHNZDAEEFN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4HKYOHBSR36CD764OHNZDAEEFN/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":"2026-07-10T11:41:20Z","links":{"resolver":"https://pith.science/pith/4HKYOHBSR36CD764OHNZDAEEFN","bundle":"https://pith.science/pith/4HKYOHBSR36CD764OHNZDAEEFN/bundle.json","state":"https://pith.science/pith/4HKYOHBSR36CD764OHNZDAEEFN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4HKYOHBSR36CD764OHNZDAEEFN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4HKYOHBSR36CD764OHNZDAEEFN","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":"4fac78567c558bcc0c4e64684980f6601b339254f1475101fcad4126244d5e3d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-17T07:07:09Z","title_canon_sha256":"0d7a11eefae4a9fb00d4c118ac9bb61dd4fa30c028229fb675f20fe21647bdcd"},"schema_version":"1.0","source":{"id":"2410.13276","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.13276","created_at":"2026-07-05T10:15:20Z"},{"alias_kind":"arxiv_version","alias_value":"2410.13276v4","created_at":"2026-07-05T10:15:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13276","created_at":"2026-07-05T10:15:20Z"},{"alias_kind":"pith_short_12","alias_value":"4HKYOHBSR36C","created_at":"2026-07-05T10:15:20Z"},{"alias_kind":"pith_short_16","alias_value":"4HKYOHBSR36CD764","created_at":"2026-07-05T10:15:20Z"},{"alias_kind":"pith_short_8","alias_value":"4HKYOHBS","created_at":"2026-07-05T10:15:20Z"}],"graph_snapshots":[{"event_id":"sha256:4e743e3847d585e10bf897b649c5ebb2903548bc8059057ee3a5ff01a9fae355","target":"graph","created_at":"2026-07-05T10:15:20Z","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/2410.13276/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Attention is the cornerstone of modern Large Language Models (LLMs). Yet its quadratic complexity hinders efficiency and scalability, especially for long-context processing. A promising approach is to leverage sparsity in attention. However, existing sparsity-based solutions predominantly rely on predefined patterns or heuristics at the attention head level, struggling to adapt dynamically to different contexts efficiently.\n  We propose SeerAttention, a simple yet effective attention mechanism that directly learns the block-level attention sparsity from the LLM itself. Inspired by the gating m","authors_text":"Dayou Du, Fan Yang, Hayden Kwok-Hay So, Jiaxing Qi, Junjie Lai, Mao Yang, Peiyuan Zhou, Shijie Cao, Ting Cao, Yizhao Gao, Zhichen Zeng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-17T07:07:09Z","title":"SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13276","kind":"arxiv","version":4},"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":"sha256:8a8135501a0e6c42af0bbc9bff4708c194cb21bd83b415289eaf82a88f6e8861","target":"record","created_at":"2026-07-05T10:15:20Z","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":"4fac78567c558bcc0c4e64684980f6601b339254f1475101fcad4126244d5e3d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-17T07:07:09Z","title_canon_sha256":"0d7a11eefae4a9fb00d4c118ac9bb61dd4fa30c028229fb675f20fe21647bdcd"},"schema_version":"1.0","source":{"id":"2410.13276","kind":"arxiv","version":4}},"canonical_sha256":"e1d5871c328efc21ffdc71db9180842b5f65207ecbb25eb55f7f38ccdcabef5b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e1d5871c328efc21ffdc71db9180842b5f65207ecbb25eb55f7f38ccdcabef5b","first_computed_at":"2026-07-05T10:15:20.674946Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:15:20.674946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SOJoyS0p9rWmTt7S6Osh6Ikt7k1Sn/hp8klTppZxAhVCmqB4Y7+iaYK70W22ONKMq6h/nFJDP8rIod+cv7T4DA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:15:20.675478Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.13276","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8a8135501a0e6c42af0bbc9bff4708c194cb21bd83b415289eaf82a88f6e8861","sha256:4e743e3847d585e10bf897b649c5ebb2903548bc8059057ee3a5ff01a9fae355"],"state_sha256":"f457579b619f113f0f086182830005a5c3ba293cc88b507bc70b79a395af6f17"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0iVVN4PzfsArEBLEfWaubx4plr7Vyt1nVm5ED34g5MCfphMX6lydtdR2UUEJFAoYa+H+Z5j0J821YwOPyW5/Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T11:41:20.417632Z","bundle_sha256":"2c250ea416b52cd8cac54964decc778732efbab984f3551e7a2ebea10c83b3bc"}}