{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:7SRYFU6FLTV6C7XRT7M4NIBTH2","short_pith_number":"pith:7SRYFU6F","canonical_record":{"source":{"id":"2504.19323","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2025-04-27T18:28:43Z","cross_cats_sorted":["cs.AI","cs.LG","cs.PF"],"title_canon_sha256":"11ae760f62c1054a768bf338c5e1435829bc99d2f353afa521eca1a06af29f61","abstract_canon_sha256":"644b434dbe257dac1ad1c6f63e5f8acb691fbe74a911c3c792e3ffd6c804a836"},"schema_version":"1.0"},"canonical_sha256":"fca382d3c55cebe17ef19fd9c6a0333ea11436b66bd9845e9cab318bd8c58147","source":{"kind":"arxiv","id":"2504.19323","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.19323","created_at":"2026-07-05T10:55:35Z"},{"alias_kind":"arxiv_version","alias_value":"2504.19323v2","created_at":"2026-07-05T10:55:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.19323","created_at":"2026-07-05T10:55:35Z"},{"alias_kind":"pith_short_12","alias_value":"7SRYFU6FLTV6","created_at":"2026-07-05T10:55:35Z"},{"alias_kind":"pith_short_16","alias_value":"7SRYFU6FLTV6C7XR","created_at":"2026-07-05T10:55:35Z"},{"alias_kind":"pith_short_8","alias_value":"7SRYFU6F","created_at":"2026-07-05T10:55:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:7SRYFU6FLTV6C7XRT7M4NIBTH2","target":"record","payload":{"canonical_record":{"source":{"id":"2504.19323","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2025-04-27T18:28:43Z","cross_cats_sorted":["cs.AI","cs.LG","cs.PF"],"title_canon_sha256":"11ae760f62c1054a768bf338c5e1435829bc99d2f353afa521eca1a06af29f61","abstract_canon_sha256":"644b434dbe257dac1ad1c6f63e5f8acb691fbe74a911c3c792e3ffd6c804a836"},"schema_version":"1.0"},"canonical_sha256":"fca382d3c55cebe17ef19fd9c6a0333ea11436b66bd9845e9cab318bd8c58147","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:55:35.624382Z","signature_b64":"l2Veu5hbMbe6tXYEbrKSKGKPLtwmjqyjLH4dhS0ErUUPbj8WbZeU/vZuXamA7Nkn0VRBAPTgcss/8kSnBpjDDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fca382d3c55cebe17ef19fd9c6a0333ea11436b66bd9845e9cab318bd8c58147","last_reissued_at":"2026-07-05T10:55:35.623867Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:55:35.623867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.19323","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":"2026-07-05T10:55:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+4b4PjGe20fQNz0ddtfKiSVVmTNE0jByaxjJH2+SEq4dNFopvaDkbzJb+Ofm6S6ZpiPDIXNwJN3Nok/JmJ1/Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T20:24:26.950691Z"},"content_sha256":"63638318cb29e7a83b0d1d0ea31273ce40cb3285b20a589876d29708752a7ca3","schema_version":"1.0","event_id":"sha256:63638318cb29e7a83b0d1d0ea31273ce40cb3285b20a589876d29708752a7ca3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:7SRYFU6FLTV6C7XRT7M4NIBTH2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.PF"],"primary_cat":"cs.AR","authors_text":"Ananda Samajdar, Arijit Raychowdhury, Hanchen Yang, Joongun Park, Ritik Raj, Tushar Krishna, Zishen Wan, Ziwei Li","submitted_at":"2025-04-27T18:28:43Z","abstract_excerpt":"Neuro-Symbolic AI (NSAI) is an emerging paradigm that integrates neural networks with symbolic reasoning to enhance the transparency, reasoning capabilities, and data efficiency of AI systems. Recent NSAI systems have gained traction due to their exceptional performance in reasoning tasks and human-AI collaborative scenarios. Despite these algorithmic advancements, executing NSAI tasks on existing hardware (e.g., CPUs, GPUs, TPUs) remains challenging, due to their heterogeneous computing kernels, high memory intensity, and unique memory access patterns. Moreover, current NSAI algorithms exhibi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.19323","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/2504.19323/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":"2026-07-05T10:55:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TcgE4HwybSuowLYPw7py289ztYRwxQ3fSaXqqhvSr9xaqqrAYvdykvThSEkBuUv0wnybHHarqZo5rfRlh2S7AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T20:24:26.952217Z"},"content_sha256":"eb479cdb6f05ac537910912d67c4265a1fb8c32e1b2f82cf65395a71b962aaaa","schema_version":"1.0","event_id":"sha256:eb479cdb6f05ac537910912d67c4265a1fb8c32e1b2f82cf65395a71b962aaaa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7SRYFU6FLTV6C7XRT7M4NIBTH2/bundle.json","state_url":"https://pith.science/pith/7SRYFU6FLTV6C7XRT7M4NIBTH2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7SRYFU6FLTV6C7XRT7M4NIBTH2/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":"2026-08-06T20:24:26Z","links":{"resolver":"https://pith.science/pith/7SRYFU6FLTV6C7XRT7M4NIBTH2","bundle":"https://pith.science/pith/7SRYFU6FLTV6C7XRT7M4NIBTH2/bundle.json","state":"https://pith.science/pith/7SRYFU6FLTV6C7XRT7M4NIBTH2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7SRYFU6FLTV6C7XRT7M4NIBTH2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:7SRYFU6FLTV6C7XRT7M4NIBTH2","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":"644b434dbe257dac1ad1c6f63e5f8acb691fbe74a911c3c792e3ffd6c804a836","cross_cats_sorted":["cs.AI","cs.LG","cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2025-04-27T18:28:43Z","title_canon_sha256":"11ae760f62c1054a768bf338c5e1435829bc99d2f353afa521eca1a06af29f61"},"schema_version":"1.0","source":{"id":"2504.19323","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.19323","created_at":"2026-07-05T10:55:35Z"},{"alias_kind":"arxiv_version","alias_value":"2504.19323v2","created_at":"2026-07-05T10:55:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.19323","created_at":"2026-07-05T10:55:35Z"},{"alias_kind":"pith_short_12","alias_value":"7SRYFU6FLTV6","created_at":"2026-07-05T10:55:35Z"},{"alias_kind":"pith_short_16","alias_value":"7SRYFU6FLTV6C7XR","created_at":"2026-07-05T10:55:35Z"},{"alias_kind":"pith_short_8","alias_value":"7SRYFU6F","created_at":"2026-07-05T10:55:35Z"}],"graph_snapshots":[{"event_id":"sha256:eb479cdb6f05ac537910912d67c4265a1fb8c32e1b2f82cf65395a71b962aaaa","target":"graph","created_at":"2026-07-05T10:55:35Z","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/2504.19323/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neuro-Symbolic AI (NSAI) is an emerging paradigm that integrates neural networks with symbolic reasoning to enhance the transparency, reasoning capabilities, and data efficiency of AI systems. Recent NSAI systems have gained traction due to their exceptional performance in reasoning tasks and human-AI collaborative scenarios. Despite these algorithmic advancements, executing NSAI tasks on existing hardware (e.g., CPUs, GPUs, TPUs) remains challenging, due to their heterogeneous computing kernels, high memory intensity, and unique memory access patterns. Moreover, current NSAI algorithms exhibi","authors_text":"Ananda Samajdar, Arijit Raychowdhury, Hanchen Yang, Joongun Park, Ritik Raj, Tushar Krishna, Zishen Wan, Ziwei Li","cross_cats":["cs.AI","cs.LG","cs.PF"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2025-04-27T18:28:43Z","title":"NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AI"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.19323","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":"sha256:63638318cb29e7a83b0d1d0ea31273ce40cb3285b20a589876d29708752a7ca3","target":"record","created_at":"2026-07-05T10:55:35Z","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":"644b434dbe257dac1ad1c6f63e5f8acb691fbe74a911c3c792e3ffd6c804a836","cross_cats_sorted":["cs.AI","cs.LG","cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2025-04-27T18:28:43Z","title_canon_sha256":"11ae760f62c1054a768bf338c5e1435829bc99d2f353afa521eca1a06af29f61"},"schema_version":"1.0","source":{"id":"2504.19323","kind":"arxiv","version":2}},"canonical_sha256":"fca382d3c55cebe17ef19fd9c6a0333ea11436b66bd9845e9cab318bd8c58147","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fca382d3c55cebe17ef19fd9c6a0333ea11436b66bd9845e9cab318bd8c58147","first_computed_at":"2026-07-05T10:55:35.623867Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:55:35.623867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"l2Veu5hbMbe6tXYEbrKSKGKPLtwmjqyjLH4dhS0ErUUPbj8WbZeU/vZuXamA7Nkn0VRBAPTgcss/8kSnBpjDDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:55:35.624382Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.19323","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:63638318cb29e7a83b0d1d0ea31273ce40cb3285b20a589876d29708752a7ca3","sha256:eb479cdb6f05ac537910912d67c4265a1fb8c32e1b2f82cf65395a71b962aaaa"],"state_sha256":"b96005cbf892deff2b347f49a2edf7e485a962687aa1d0606d49a4c0d8f1997e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8srqJAYFCzjCvvBI8eA6ILcPfgzVOtE3qn4G22yJwjFDMyzvxbt2dD38LxR0q39WajSA5rUOY4pxND6p6b3fCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T20:24:26.958938Z","bundle_sha256":"c2dfc12a3622d663c63c121055902f98790bafc33bb114faed27daf03571d9d2"}}