{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:NUKTPVHFO7WTGIELWEVRCMAP5N","short_pith_number":"pith:NUKTPVHF","canonical_record":{"source":{"id":"2504.16339","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2025-04-23T01:23:41Z","cross_cats_sorted":[],"title_canon_sha256":"3d421ec9a7ea85b5789a3ab649261730a13e8b082bb02777bb6eedb122433c02","abstract_canon_sha256":"65d533a6d1a97fd81bfd000d6ff87883da42348b047b4cad91c9d3241fba405c"},"schema_version":"1.0"},"canonical_sha256":"6d1537d4e577ed33208bb12b11300feb64843440e4defd6d34c633850a228bf9","source":{"kind":"arxiv","id":"2504.16339","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.16339","created_at":"2026-07-05T10:52:45Z"},{"alias_kind":"arxiv_version","alias_value":"2504.16339v1","created_at":"2026-07-05T10:52:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.16339","created_at":"2026-07-05T10:52:45Z"},{"alias_kind":"pith_short_12","alias_value":"NUKTPVHFO7WT","created_at":"2026-07-05T10:52:45Z"},{"alias_kind":"pith_short_16","alias_value":"NUKTPVHFO7WTGIEL","created_at":"2026-07-05T10:52:45Z"},{"alias_kind":"pith_short_8","alias_value":"NUKTPVHF","created_at":"2026-07-05T10:52:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:NUKTPVHFO7WTGIELWEVRCMAP5N","target":"record","payload":{"canonical_record":{"source":{"id":"2504.16339","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2025-04-23T01:23:41Z","cross_cats_sorted":[],"title_canon_sha256":"3d421ec9a7ea85b5789a3ab649261730a13e8b082bb02777bb6eedb122433c02","abstract_canon_sha256":"65d533a6d1a97fd81bfd000d6ff87883da42348b047b4cad91c9d3241fba405c"},"schema_version":"1.0"},"canonical_sha256":"6d1537d4e577ed33208bb12b11300feb64843440e4defd6d34c633850a228bf9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:52:45.138266Z","signature_b64":"oJuw+/KZQ78U/nI6F8aVPWlfkxB3ECeUWYL8H3IfW0H45vo0W36IgNkSj8sNGjh76HydSgtU4vPjmjP+pDMnCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d1537d4e577ed33208bb12b11300feb64843440e4defd6d34c633850a228bf9","last_reissued_at":"2026-07-05T10:52:45.137826Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:52:45.137826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.16339","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":"2026-07-05T10:52:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cwblfDh4RiEJ7Zv2UXrDmzXxAiBrVxY69xqg7ngvNHdOkaQQFUFKwEBImB+I1ugEodcnv5N/PlS3vRpSEe9ECg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T06:38:41.782887Z"},"content_sha256":"bd89d9c6a477adddb0b2e13099d3e8e824b4fb8d3040a225337fbd25835ef9df","schema_version":"1.0","event_id":"sha256:bd89d9c6a477adddb0b2e13099d3e8e824b4fb8d3040a225337fbd25835ef9df"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:NUKTPVHFO7WTGIELWEVRCMAP5N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Transitive Array: An Efficient GEMM Accelerator with Result Reuse","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Bowen Duan, Chiyue Wei, Cong Guo, Hai Li, Jiaming Tang, Song Han, Yiran Chen","submitted_at":"2025-04-23T01:23:41Z","abstract_excerpt":"Deep Neural Networks (DNNs) and Large Language Models (LLMs) have revolutionized artificial intelligence, yet their deployment faces significant memory and computational challenges, especially in resource-constrained environments. Quantization techniques have mitigated some of these issues by reducing data precision, primarily focusing on General Matrix Multiplication (GEMM). This study introduces a novel sparsity paradigm, transitive sparsity, which leverages the reuse of previously computed results to substantially minimize computational overhead in GEMM operations. By representing transitiv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.16339","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/2504.16339/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":"2026-07-05T10:52:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ouejvLboEZ08OvhkvjfANQSV2iwWOvqnM85/hcgtjgBgcyZ+1jVZ7aAwsnMkXxaUBO9Boe1j+8pdafqtmZoWCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T06:38:41.783394Z"},"content_sha256":"8d0167881cde9f3319fb690ba4e00ced7c2ad4b9b61ab79d15b01e934016d1fd","schema_version":"1.0","event_id":"sha256:8d0167881cde9f3319fb690ba4e00ced7c2ad4b9b61ab79d15b01e934016d1fd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NUKTPVHFO7WTGIELWEVRCMAP5N/bundle.json","state_url":"https://pith.science/pith/NUKTPVHFO7WTGIELWEVRCMAP5N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NUKTPVHFO7WTGIELWEVRCMAP5N/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":"2026-08-19T06:38:41Z","links":{"resolver":"https://pith.science/pith/NUKTPVHFO7WTGIELWEVRCMAP5N","bundle":"https://pith.science/pith/NUKTPVHFO7WTGIELWEVRCMAP5N/bundle.json","state":"https://pith.science/pith/NUKTPVHFO7WTGIELWEVRCMAP5N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NUKTPVHFO7WTGIELWEVRCMAP5N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NUKTPVHFO7WTGIELWEVRCMAP5N","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":"65d533a6d1a97fd81bfd000d6ff87883da42348b047b4cad91c9d3241fba405c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2025-04-23T01:23:41Z","title_canon_sha256":"3d421ec9a7ea85b5789a3ab649261730a13e8b082bb02777bb6eedb122433c02"},"schema_version":"1.0","source":{"id":"2504.16339","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.16339","created_at":"2026-07-05T10:52:45Z"},{"alias_kind":"arxiv_version","alias_value":"2504.16339v1","created_at":"2026-07-05T10:52:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.16339","created_at":"2026-07-05T10:52:45Z"},{"alias_kind":"pith_short_12","alias_value":"NUKTPVHFO7WT","created_at":"2026-07-05T10:52:45Z"},{"alias_kind":"pith_short_16","alias_value":"NUKTPVHFO7WTGIEL","created_at":"2026-07-05T10:52:45Z"},{"alias_kind":"pith_short_8","alias_value":"NUKTPVHF","created_at":"2026-07-05T10:52:45Z"}],"graph_snapshots":[{"event_id":"sha256:8d0167881cde9f3319fb690ba4e00ced7c2ad4b9b61ab79d15b01e934016d1fd","target":"graph","created_at":"2026-07-05T10:52:45Z","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/2504.16339/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep Neural Networks (DNNs) and Large Language Models (LLMs) have revolutionized artificial intelligence, yet their deployment faces significant memory and computational challenges, especially in resource-constrained environments. Quantization techniques have mitigated some of these issues by reducing data precision, primarily focusing on General Matrix Multiplication (GEMM). This study introduces a novel sparsity paradigm, transitive sparsity, which leverages the reuse of previously computed results to substantially minimize computational overhead in GEMM operations. By representing transitiv","authors_text":"Bowen Duan, Chiyue Wei, Cong Guo, Hai Li, Jiaming Tang, Song Han, Yiran Chen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2025-04-23T01:23:41Z","title":"Transitive Array: An Efficient GEMM Accelerator with Result Reuse"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.16339","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:bd89d9c6a477adddb0b2e13099d3e8e824b4fb8d3040a225337fbd25835ef9df","target":"record","created_at":"2026-07-05T10:52:45Z","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":"65d533a6d1a97fd81bfd000d6ff87883da42348b047b4cad91c9d3241fba405c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2025-04-23T01:23:41Z","title_canon_sha256":"3d421ec9a7ea85b5789a3ab649261730a13e8b082bb02777bb6eedb122433c02"},"schema_version":"1.0","source":{"id":"2504.16339","kind":"arxiv","version":1}},"canonical_sha256":"6d1537d4e577ed33208bb12b11300feb64843440e4defd6d34c633850a228bf9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6d1537d4e577ed33208bb12b11300feb64843440e4defd6d34c633850a228bf9","first_computed_at":"2026-07-05T10:52:45.137826Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:52:45.137826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oJuw+/KZQ78U/nI6F8aVPWlfkxB3ECeUWYL8H3IfW0H45vo0W36IgNkSj8sNGjh76HydSgtU4vPjmjP+pDMnCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:52:45.138266Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.16339","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bd89d9c6a477adddb0b2e13099d3e8e824b4fb8d3040a225337fbd25835ef9df","sha256:8d0167881cde9f3319fb690ba4e00ced7c2ad4b9b61ab79d15b01e934016d1fd"],"state_sha256":"5d22e13493014a96463663e05928e4b3250006feee93b3e995465416a2f1eb4c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+rinm+B3n20Rc961IJiDUwCqF2YXqz0V7NfEFKV5U3hLgW45iSbdhiYHvrngBx0C/0TpdF+XXoqiFMlNMNc2AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T06:38:41.788878Z","bundle_sha256":"f49198d32c2f6aa6483323d84a325974249dfc93175e3c8f9089e1d54313ec8b"}}