{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:ULQG3GYFHWGGS4ROLV2VN4DBN3","short_pith_number":"pith:ULQG3GYF","canonical_record":{"source":{"id":"2507.08751","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-11T17:02:33Z","cross_cats_sorted":[],"title_canon_sha256":"9f35d88f80d588fedae2bf9b8affd7e83fb85b2425f237cc8903b4cc0c25bfa9","abstract_canon_sha256":"3102bb417036f71c483176e4cdb3d1d4926358ee15156bef63e573fe5b066fc3"},"schema_version":"1.0"},"canonical_sha256":"a2e06d9b053d8c69722e5d7556f0616ef23854791b3c275c3896b058d298a5af","source":{"kind":"arxiv","id":"2507.08751","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.08751","created_at":"2026-07-05T11:35:45Z"},{"alias_kind":"arxiv_version","alias_value":"2507.08751v1","created_at":"2026-07-05T11:35:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.08751","created_at":"2026-07-05T11:35:45Z"},{"alias_kind":"pith_short_12","alias_value":"ULQG3GYFHWGG","created_at":"2026-07-05T11:35:45Z"},{"alias_kind":"pith_short_16","alias_value":"ULQG3GYFHWGGS4RO","created_at":"2026-07-05T11:35:45Z"},{"alias_kind":"pith_short_8","alias_value":"ULQG3GYF","created_at":"2026-07-05T11:35:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:ULQG3GYFHWGGS4ROLV2VN4DBN3","target":"record","payload":{"canonical_record":{"source":{"id":"2507.08751","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-11T17:02:33Z","cross_cats_sorted":[],"title_canon_sha256":"9f35d88f80d588fedae2bf9b8affd7e83fb85b2425f237cc8903b4cc0c25bfa9","abstract_canon_sha256":"3102bb417036f71c483176e4cdb3d1d4926358ee15156bef63e573fe5b066fc3"},"schema_version":"1.0"},"canonical_sha256":"a2e06d9b053d8c69722e5d7556f0616ef23854791b3c275c3896b058d298a5af","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:45.954551Z","signature_b64":"Kw+CbCknnTZ+bvTzwEBjDX1GHGzTWSqrEOLqVpxbCHM5UX5HhebNoV8W73yyRRhsScBMmq7A3r2s+Mw+9BzmAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2e06d9b053d8c69722e5d7556f0616ef23854791b3c275c3896b058d298a5af","last_reissued_at":"2026-07-05T11:35:45.954130Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:45.954130Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.08751","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":"2026-07-05T11:35:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DlVYFIYY8BrxEfrHhBplwpREEnRWEk3WtP7AmS8VKRnRv0LOfRTwa240lj59wPc0humHy1MuLpqdW0swJkd2Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T01:30:57.472837Z"},"content_sha256":"7b5fefddfb4787e626a9393baf38603651fadb24a423ab0e488dcde2db71c9f3","schema_version":"1.0","event_id":"sha256:7b5fefddfb4787e626a9393baf38603651fadb24a423ab0e488dcde2db71c9f3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:ULQG3GYFHWGGS4ROLV2VN4DBN3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ML-Based Automata Simplification for Symbolic Accelerators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Darssan Eswaramoorthi, Rasha Karakchi, Rye Stahle-Smith, Tiffany Yu","submitted_at":"2025-07-11T17:02:33Z","abstract_excerpt":"Symbolic accelerators are increasingly used for symbolic data processing in domains such as genomics, NLP, and cybersecurity. However, these accelerators face scalability issues due to excessive memory use and routing complexity, especially when targeting a large set. We present AutoSlim, a machine learning-based graph simplification framework designed to reduce the complexity of symbolic accelerators built on Non-deterministic Finite Automata (NFA) deployed on FPGA-based overlays such as NAPOLY+. AutoSlim uses Random Forest classification to prune low-impact transitions based on edge scores a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.08751","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/2507.08751/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":"2026-07-05T11:35:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rutECnE7ZN1jo5Qg8+XsmWP2xT+H2tWM6N+hFVsvsKUttv9+T+qyUOPWHmkRTTlUHHW/9Q43Bs9T2oRXJ4XBDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T01:30:57.473216Z"},"content_sha256":"0ca229bd3b3f5ac987954b35aed1afd188de634cfacd157153553e2c58160b2c","schema_version":"1.0","event_id":"sha256:0ca229bd3b3f5ac987954b35aed1afd188de634cfacd157153553e2c58160b2c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ULQG3GYFHWGGS4ROLV2VN4DBN3/bundle.json","state_url":"https://pith.science/pith/ULQG3GYFHWGGS4ROLV2VN4DBN3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ULQG3GYFHWGGS4ROLV2VN4DBN3/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":"2026-07-21T01:30:57Z","links":{"resolver":"https://pith.science/pith/ULQG3GYFHWGGS4ROLV2VN4DBN3","bundle":"https://pith.science/pith/ULQG3GYFHWGGS4ROLV2VN4DBN3/bundle.json","state":"https://pith.science/pith/ULQG3GYFHWGGS4ROLV2VN4DBN3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ULQG3GYFHWGGS4ROLV2VN4DBN3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ULQG3GYFHWGGS4ROLV2VN4DBN3","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":"3102bb417036f71c483176e4cdb3d1d4926358ee15156bef63e573fe5b066fc3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-11T17:02:33Z","title_canon_sha256":"9f35d88f80d588fedae2bf9b8affd7e83fb85b2425f237cc8903b4cc0c25bfa9"},"schema_version":"1.0","source":{"id":"2507.08751","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.08751","created_at":"2026-07-05T11:35:45Z"},{"alias_kind":"arxiv_version","alias_value":"2507.08751v1","created_at":"2026-07-05T11:35:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.08751","created_at":"2026-07-05T11:35:45Z"},{"alias_kind":"pith_short_12","alias_value":"ULQG3GYFHWGG","created_at":"2026-07-05T11:35:45Z"},{"alias_kind":"pith_short_16","alias_value":"ULQG3GYFHWGGS4RO","created_at":"2026-07-05T11:35:45Z"},{"alias_kind":"pith_short_8","alias_value":"ULQG3GYF","created_at":"2026-07-05T11:35:45Z"}],"graph_snapshots":[{"event_id":"sha256:0ca229bd3b3f5ac987954b35aed1afd188de634cfacd157153553e2c58160b2c","target":"graph","created_at":"2026-07-05T11:35:45Z","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/2507.08751/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Symbolic accelerators are increasingly used for symbolic data processing in domains such as genomics, NLP, and cybersecurity. However, these accelerators face scalability issues due to excessive memory use and routing complexity, especially when targeting a large set. We present AutoSlim, a machine learning-based graph simplification framework designed to reduce the complexity of symbolic accelerators built on Non-deterministic Finite Automata (NFA) deployed on FPGA-based overlays such as NAPOLY+. AutoSlim uses Random Forest classification to prune low-impact transitions based on edge scores a","authors_text":"Darssan Eswaramoorthi, Rasha Karakchi, Rye Stahle-Smith, Tiffany Yu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-11T17:02:33Z","title":"ML-Based Automata Simplification for Symbolic Accelerators"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.08751","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":"sha256:7b5fefddfb4787e626a9393baf38603651fadb24a423ab0e488dcde2db71c9f3","target":"record","created_at":"2026-07-05T11:35:45Z","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":"3102bb417036f71c483176e4cdb3d1d4926358ee15156bef63e573fe5b066fc3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-11T17:02:33Z","title_canon_sha256":"9f35d88f80d588fedae2bf9b8affd7e83fb85b2425f237cc8903b4cc0c25bfa9"},"schema_version":"1.0","source":{"id":"2507.08751","kind":"arxiv","version":1}},"canonical_sha256":"a2e06d9b053d8c69722e5d7556f0616ef23854791b3c275c3896b058d298a5af","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a2e06d9b053d8c69722e5d7556f0616ef23854791b3c275c3896b058d298a5af","first_computed_at":"2026-07-05T11:35:45.954130Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:35:45.954130Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Kw+CbCknnTZ+bvTzwEBjDX1GHGzTWSqrEOLqVpxbCHM5UX5HhebNoV8W73yyRRhsScBMmq7A3r2s+Mw+9BzmAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:35:45.954551Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.08751","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7b5fefddfb4787e626a9393baf38603651fadb24a423ab0e488dcde2db71c9f3","sha256:0ca229bd3b3f5ac987954b35aed1afd188de634cfacd157153553e2c58160b2c"],"state_sha256":"b8ae28d51aa4c1de191a0ea802e1df13ad04202e5db7a7de8cd8f6e290260889"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G2FMKUljXc84Ajft0NhuLKT7EeyTLjntyPa3ebGptpl6eBoUoVIMCVwfje5uI3JhFgBbnOA7fpwSjSqPgUPDBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-21T01:30:57.475380Z","bundle_sha256":"5c8b3f5bda35d6f5b5601f9a217ed159e525d8d067a69abd0a00701229ea4f45"}}