{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:CWYBFGHUOTYUIKFRKXQWL7AYWN","short_pith_number":"pith:CWYBFGHU","canonical_record":{"source":{"id":"2507.21394","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-29T00:01:57Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"9ec3c616a667f62dc3f0ad8086b10be2a5d05a376732b647be4ade632abfc32a","abstract_canon_sha256":"6c790acabd3aeb07ffe36b1f6ff64149765df6f2fc8f71df1c28f8500eb5b0b0"},"schema_version":"1.0"},"canonical_sha256":"15b01298f474f14428b155e165fc18b347845067f1343e927a64e27498eacbac","source":{"kind":"arxiv","id":"2507.21394","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.21394","created_at":"2026-07-05T11:49:58Z"},{"alias_kind":"arxiv_version","alias_value":"2507.21394v3","created_at":"2026-07-05T11:49:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.21394","created_at":"2026-07-05T11:49:58Z"},{"alias_kind":"pith_short_12","alias_value":"CWYBFGHUOTYU","created_at":"2026-07-05T11:49:58Z"},{"alias_kind":"pith_short_16","alias_value":"CWYBFGHUOTYUIKFR","created_at":"2026-07-05T11:49:58Z"},{"alias_kind":"pith_short_8","alias_value":"CWYBFGHU","created_at":"2026-07-05T11:49:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:CWYBFGHUOTYUIKFRKXQWL7AYWN","target":"record","payload":{"canonical_record":{"source":{"id":"2507.21394","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-29T00:01:57Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"9ec3c616a667f62dc3f0ad8086b10be2a5d05a376732b647be4ade632abfc32a","abstract_canon_sha256":"6c790acabd3aeb07ffe36b1f6ff64149765df6f2fc8f71df1c28f8500eb5b0b0"},"schema_version":"1.0"},"canonical_sha256":"15b01298f474f14428b155e165fc18b347845067f1343e927a64e27498eacbac","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:58.405093Z","signature_b64":"aPDOt+0pr5JsSGTrKrnaad6Uqgw3i8Bag19lJlp9v+i1XPXyQTHIle2WeHddjG242uR9C8TR70FH6rvUKuaCBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15b01298f474f14428b155e165fc18b347845067f1343e927a64e27498eacbac","last_reissued_at":"2026-07-05T11:49:58.404678Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:58.404678Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.21394","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:49:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AmjLiFqiUjsfgOm33b3otvQvhrJyi9WL7RrbD/5jTb+lJJd56jgW4hOyn69Sqj0GLeXyNGJRahBg/GBOvejOBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:12:14.936358Z"},"content_sha256":"f19ada342f4739bfff0182f6a0c05319906fb34207446912e358af0df8cc8498","schema_version":"1.0","event_id":"sha256:f19ada342f4739bfff0182f6a0c05319906fb34207446912e358af0df8cc8498"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:CWYBFGHUOTYUIKFRKXQWL7AYWN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Systolic Array-based Accelerator for Structured State-Space Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Aakash Sarkar, Ajay Joshi, Cansu Demirkiran, Milos Popovic, Shiva Raja","submitted_at":"2025-07-29T00:01:57Z","abstract_excerpt":"Sequence modeling is crucial for AI to understand temporal data and detect complex time-dependent patterns. While recurrent neural networks (RNNs), convolutional neural networks (CNNs), and Transformers have advanced in capturing long-range dependencies, they struggle with achieving high accuracy with very long sequences due to limited memory retention (fixed context window). State-Space Models (SSMs) leverage exponentially decaying memory enabling lengthy context window and so they process very long data sequences more efficiently than recurrent and Transformer-based models. Unlike traditiona"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.21394","kind":"arxiv","version":3},"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/2507.21394/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":"2026-07-05T11:49:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/53m7hY8RPt1giQ7q096BU4sy37QKY7dz0J7qlFVRjL9pgWzNiGyfZgAen84T9+yri/RFZ7hcgskRODmJy+rBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:12:14.936879Z"},"content_sha256":"bfb1b2d57fc893d787a609b2699a9d0a67002f6d6708ca69cadcba290cb00be5","schema_version":"1.0","event_id":"sha256:bfb1b2d57fc893d787a609b2699a9d0a67002f6d6708ca69cadcba290cb00be5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CWYBFGHUOTYUIKFRKXQWL7AYWN/bundle.json","state_url":"https://pith.science/pith/CWYBFGHUOTYUIKFRKXQWL7AYWN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CWYBFGHUOTYUIKFRKXQWL7AYWN/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":"2026-08-07T22:12:14Z","links":{"resolver":"https://pith.science/pith/CWYBFGHUOTYUIKFRKXQWL7AYWN","bundle":"https://pith.science/pith/CWYBFGHUOTYUIKFRKXQWL7AYWN/bundle.json","state":"https://pith.science/pith/CWYBFGHUOTYUIKFRKXQWL7AYWN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CWYBFGHUOTYUIKFRKXQWL7AYWN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:CWYBFGHUOTYUIKFRKXQWL7AYWN","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":"6c790acabd3aeb07ffe36b1f6ff64149765df6f2fc8f71df1c28f8500eb5b0b0","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-29T00:01:57Z","title_canon_sha256":"9ec3c616a667f62dc3f0ad8086b10be2a5d05a376732b647be4ade632abfc32a"},"schema_version":"1.0","source":{"id":"2507.21394","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.21394","created_at":"2026-07-05T11:49:58Z"},{"alias_kind":"arxiv_version","alias_value":"2507.21394v3","created_at":"2026-07-05T11:49:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.21394","created_at":"2026-07-05T11:49:58Z"},{"alias_kind":"pith_short_12","alias_value":"CWYBFGHUOTYU","created_at":"2026-07-05T11:49:58Z"},{"alias_kind":"pith_short_16","alias_value":"CWYBFGHUOTYUIKFR","created_at":"2026-07-05T11:49:58Z"},{"alias_kind":"pith_short_8","alias_value":"CWYBFGHU","created_at":"2026-07-05T11:49:58Z"}],"graph_snapshots":[{"event_id":"sha256:bfb1b2d57fc893d787a609b2699a9d0a67002f6d6708ca69cadcba290cb00be5","target":"graph","created_at":"2026-07-05T11:49:58Z","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/2507.21394/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Sequence modeling is crucial for AI to understand temporal data and detect complex time-dependent patterns. While recurrent neural networks (RNNs), convolutional neural networks (CNNs), and Transformers have advanced in capturing long-range dependencies, they struggle with achieving high accuracy with very long sequences due to limited memory retention (fixed context window). State-Space Models (SSMs) leverage exponentially decaying memory enabling lengthy context window and so they process very long data sequences more efficiently than recurrent and Transformer-based models. Unlike traditiona","authors_text":"Aakash Sarkar, Ajay Joshi, Cansu Demirkiran, Milos Popovic, Shiva Raja","cross_cats":["cs.SY","eess.SY"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-29T00:01:57Z","title":"Systolic Array-based Accelerator for Structured State-Space Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.21394","kind":"arxiv","version":3},"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:f19ada342f4739bfff0182f6a0c05319906fb34207446912e358af0df8cc8498","target":"record","created_at":"2026-07-05T11:49:58Z","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":"6c790acabd3aeb07ffe36b1f6ff64149765df6f2fc8f71df1c28f8500eb5b0b0","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-29T00:01:57Z","title_canon_sha256":"9ec3c616a667f62dc3f0ad8086b10be2a5d05a376732b647be4ade632abfc32a"},"schema_version":"1.0","source":{"id":"2507.21394","kind":"arxiv","version":3}},"canonical_sha256":"15b01298f474f14428b155e165fc18b347845067f1343e927a64e27498eacbac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"15b01298f474f14428b155e165fc18b347845067f1343e927a64e27498eacbac","first_computed_at":"2026-07-05T11:49:58.404678Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:49:58.404678Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aPDOt+0pr5JsSGTrKrnaad6Uqgw3i8Bag19lJlp9v+i1XPXyQTHIle2WeHddjG242uR9C8TR70FH6rvUKuaCBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:49:58.405093Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.21394","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f19ada342f4739bfff0182f6a0c05319906fb34207446912e358af0df8cc8498","sha256:bfb1b2d57fc893d787a609b2699a9d0a67002f6d6708ca69cadcba290cb00be5"],"state_sha256":"8fa34bc00fb697d2eba328b4ba54a5ca0f80f7b8942dbb6f19fb006bbf93a5e0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U0eReIJ2IkaBtwwX3UTIsnRoHf2+HZHXozcqnq11iMQDDZdJfu9HadtvW1rVzwcXUymn9iMSQgytr4UcHQ3TAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T22:12:14.943099Z","bundle_sha256":"2679bd7c6a67df8f32cc0a8bab887962a58ff8bbf51876274cb338556004786b"}}