{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:QS4PD7Z7IPEIFMWJ5EK7TZIDTB","short_pith_number":"pith:QS4PD7Z7","canonical_record":{"source":{"id":"2608.00029","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-15T05:16:28Z","cross_cats_sorted":["cs.AR","cs.LG","cs.PL"],"title_canon_sha256":"dd4228e1c981fc757b49b53fef7dfe55b7c3bf5481dbc3dd86b532f737820b5f","abstract_canon_sha256":"208b4864c96925082fdf16e909595d3466d1c281b90f1e19537cb261238dee7d"},"schema_version":"1.0"},"canonical_sha256":"84b8f1ff3f43c882b2c9e915f9e503986071c5d3dc3ff9cd88ab470a0d68abe0","source":{"kind":"arxiv","id":"2608.00029","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.00029","created_at":"2026-08-04T00:31:48Z"},{"alias_kind":"arxiv_version","alias_value":"2608.00029v1","created_at":"2026-08-04T00:31:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.00029","created_at":"2026-08-04T00:31:48Z"},{"alias_kind":"pith_short_12","alias_value":"QS4PD7Z7IPEI","created_at":"2026-08-04T00:31:48Z"},{"alias_kind":"pith_short_16","alias_value":"QS4PD7Z7IPEIFMWJ","created_at":"2026-08-04T00:31:48Z"},{"alias_kind":"pith_short_8","alias_value":"QS4PD7Z7","created_at":"2026-08-04T00:31:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:QS4PD7Z7IPEIFMWJ5EK7TZIDTB","target":"record","payload":{"canonical_record":{"source":{"id":"2608.00029","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-15T05:16:28Z","cross_cats_sorted":["cs.AR","cs.LG","cs.PL"],"title_canon_sha256":"dd4228e1c981fc757b49b53fef7dfe55b7c3bf5481dbc3dd86b532f737820b5f","abstract_canon_sha256":"208b4864c96925082fdf16e909595d3466d1c281b90f1e19537cb261238dee7d"},"schema_version":"1.0"},"canonical_sha256":"84b8f1ff3f43c882b2c9e915f9e503986071c5d3dc3ff9cd88ab470a0d68abe0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T00:31:48.358163Z","signature_b64":"RQTTsYAc1ebmvoHrNjSYKtAxHAIVk+xzIyQ+Jtq1hlkLGAQQbnL825lbdACH3O6t+beNl6Qkr/6HAOl3Mfe+Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84b8f1ff3f43c882b2c9e915f9e503986071c5d3dc3ff9cd88ab470a0d68abe0","last_reissued_at":"2026-08-04T00:31:48.356704Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T00:31:48.356704Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.00029","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":"2026-08-04T00:31:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xrNLhF8/VOYIBJ+i7tS6/c+8BnQdKbZ5r4oiOIGlMEG3duJYZIFSlajsvw3L8OSEodobHQnYxtlX+KdywVU5Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T02:09:34.761903Z"},"content_sha256":"e7d7ec7085c4c74b0d33f401c1b1f57f6bfeed14e64aff36c7af8138ad2004e5","schema_version":"1.0","event_id":"sha256:e7d7ec7085c4c74b0d33f401c1b1f57f6bfeed14e64aff36c7af8138ad2004e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:QS4PD7Z7IPEIFMWJ5EK7TZIDTB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Nova: An End-to-End MLIR Compiler for Deep Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AR","cs.LG","cs.PL"],"primary_cat":"cs.AI","authors_text":"Adwaid Suresh, Aparna A, Harshini V M, Jona Delcy C A, Killi Uma Maheswara Rao, Ram Charan Golla, Surendra Vendra","submitted_at":"2026-07-15T05:16:28Z","abstract_excerpt":"The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physical hardware. While high-level tensor frameworks provide flexible abstractions for model design, their eager execution models inherently lack the whole-graph visibility and granular control over hardware and memory required to maximize physical hardware utilization natively. To bridge this gap, we designed Nova, an automated end-to-end JIT compiler whose defining purpose is to achieve absolute control over this hardware mapping: fusing operations a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.00029","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/2608.00029/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":"2026-08-04T00:31:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ULukX68czoPDnkzDU/5mbTrnUdQnVw5GdkRxwQvQcmPCr0+b7RsF+Yztq5hI/AC5lMN9SV/nl2p8dJLywC/DBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T02:09:34.762441Z"},"content_sha256":"2f6927d5684e772e9b937f6d53cfa95e7a5b6857cc89e5a023a90ab9f42995ee","schema_version":"1.0","event_id":"sha256:2f6927d5684e772e9b937f6d53cfa95e7a5b6857cc89e5a023a90ab9f42995ee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QS4PD7Z7IPEIFMWJ5EK7TZIDTB/bundle.json","state_url":"https://pith.science/pith/QS4PD7Z7IPEIFMWJ5EK7TZIDTB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QS4PD7Z7IPEIFMWJ5EK7TZIDTB/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":"2026-08-10T02:09:34Z","links":{"resolver":"https://pith.science/pith/QS4PD7Z7IPEIFMWJ5EK7TZIDTB","bundle":"https://pith.science/pith/QS4PD7Z7IPEIFMWJ5EK7TZIDTB/bundle.json","state":"https://pith.science/pith/QS4PD7Z7IPEIFMWJ5EK7TZIDTB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QS4PD7Z7IPEIFMWJ5EK7TZIDTB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:QS4PD7Z7IPEIFMWJ5EK7TZIDTB","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":"208b4864c96925082fdf16e909595d3466d1c281b90f1e19537cb261238dee7d","cross_cats_sorted":["cs.AR","cs.LG","cs.PL"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-15T05:16:28Z","title_canon_sha256":"dd4228e1c981fc757b49b53fef7dfe55b7c3bf5481dbc3dd86b532f737820b5f"},"schema_version":"1.0","source":{"id":"2608.00029","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.00029","created_at":"2026-08-04T00:31:48Z"},{"alias_kind":"arxiv_version","alias_value":"2608.00029v1","created_at":"2026-08-04T00:31:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.00029","created_at":"2026-08-04T00:31:48Z"},{"alias_kind":"pith_short_12","alias_value":"QS4PD7Z7IPEI","created_at":"2026-08-04T00:31:48Z"},{"alias_kind":"pith_short_16","alias_value":"QS4PD7Z7IPEIFMWJ","created_at":"2026-08-04T00:31:48Z"},{"alias_kind":"pith_short_8","alias_value":"QS4PD7Z7","created_at":"2026-08-04T00:31:48Z"}],"graph_snapshots":[{"event_id":"sha256:2f6927d5684e772e9b937f6d53cfa95e7a5b6857cc89e5a023a90ab9f42995ee","target":"graph","created_at":"2026-08-04T00:31:48Z","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/2608.00029/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physical hardware. While high-level tensor frameworks provide flexible abstractions for model design, their eager execution models inherently lack the whole-graph visibility and granular control over hardware and memory required to maximize physical hardware utilization natively. To bridge this gap, we designed Nova, an automated end-to-end JIT compiler whose defining purpose is to achieve absolute control over this hardware mapping: fusing operations a","authors_text":"Adwaid Suresh, Aparna A, Harshini V M, Jona Delcy C A, Killi Uma Maheswara Rao, Ram Charan Golla, Surendra Vendra","cross_cats":["cs.AR","cs.LG","cs.PL"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-15T05:16:28Z","title":"Nova: An End-to-End MLIR Compiler for Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.00029","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:e7d7ec7085c4c74b0d33f401c1b1f57f6bfeed14e64aff36c7af8138ad2004e5","target":"record","created_at":"2026-08-04T00:31:48Z","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":"208b4864c96925082fdf16e909595d3466d1c281b90f1e19537cb261238dee7d","cross_cats_sorted":["cs.AR","cs.LG","cs.PL"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-15T05:16:28Z","title_canon_sha256":"dd4228e1c981fc757b49b53fef7dfe55b7c3bf5481dbc3dd86b532f737820b5f"},"schema_version":"1.0","source":{"id":"2608.00029","kind":"arxiv","version":1}},"canonical_sha256":"84b8f1ff3f43c882b2c9e915f9e503986071c5d3dc3ff9cd88ab470a0d68abe0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"84b8f1ff3f43c882b2c9e915f9e503986071c5d3dc3ff9cd88ab470a0d68abe0","first_computed_at":"2026-08-04T00:31:48.356704Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-04T00:31:48.356704Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RQTTsYAc1ebmvoHrNjSYKtAxHAIVk+xzIyQ+Jtq1hlkLGAQQbnL825lbdACH3O6t+beNl6Qkr/6HAOl3Mfe+Cg==","signature_status":"signed_v1","signed_at":"2026-08-04T00:31:48.358163Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.00029","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e7d7ec7085c4c74b0d33f401c1b1f57f6bfeed14e64aff36c7af8138ad2004e5","sha256:2f6927d5684e772e9b937f6d53cfa95e7a5b6857cc89e5a023a90ab9f42995ee"],"state_sha256":"de39302b8f39c8d4df2080420b7db3bf706898f474adba2ccbe7cefd1df2d9c0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R6rp0C/3TcBAt62NPf3uoP0t5giS/F4SYApsJOFpBk00kbVkltcJGQCx2ka7g3vHJrzD2JuzdsQEHvsWqJRvDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T02:09:34.767507Z","bundle_sha256":"ee54a7df42012a9bd1acb6b5f2275b07f2ae70604882f391a4ed3ad3439e9900"}}