{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:BH4ZYJ5JPORG34UNRLAC4GCOKM","short_pith_number":"pith:BH4ZYJ5J","canonical_record":{"source":{"id":"2605.16610","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T20:21:14Z","cross_cats_sorted":["cs.GL"],"title_canon_sha256":"07fca9b0ee1d9e7f61617e3aa593dbf458e51fe85dc23d16e296536f61831659","abstract_canon_sha256":"2b39f46337082b57479f7fe347e97cfebf69ab56d428ddb019b21c6027d42eb9"},"schema_version":"1.0"},"canonical_sha256":"09f99c27a97ba26df28d8ac02e184e532ffb80296d3a4ef1bb3678f0bbce5483","source":{"kind":"arxiv","id":"2605.16610","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16610","created_at":"2026-05-20T00:02:32Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16610v1","created_at":"2026-05-20T00:02:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16610","created_at":"2026-05-20T00:02:32Z"},{"alias_kind":"pith_short_12","alias_value":"BH4ZYJ5JPORG","created_at":"2026-05-20T00:02:32Z"},{"alias_kind":"pith_short_16","alias_value":"BH4ZYJ5JPORG34UN","created_at":"2026-05-20T00:02:32Z"},{"alias_kind":"pith_short_8","alias_value":"BH4ZYJ5J","created_at":"2026-05-20T00:02:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:BH4ZYJ5JPORG34UNRLAC4GCOKM","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16610","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T20:21:14Z","cross_cats_sorted":["cs.GL"],"title_canon_sha256":"07fca9b0ee1d9e7f61617e3aa593dbf458e51fe85dc23d16e296536f61831659","abstract_canon_sha256":"2b39f46337082b57479f7fe347e97cfebf69ab56d428ddb019b21c6027d42eb9"},"schema_version":"1.0"},"canonical_sha256":"09f99c27a97ba26df28d8ac02e184e532ffb80296d3a4ef1bb3678f0bbce5483","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:02:32.354522Z","signature_b64":"cx9FrugCNcUjrhIpJ12K1CTBjpbKKpwZqWhEZWzlf0WoiFTAXj6j9ikEUjo24kkNVcjFHmkAXWvnQ2LL8ZQXDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"09f99c27a97ba26df28d8ac02e184e532ffb80296d3a4ef1bb3678f0bbce5483","last_reissued_at":"2026-05-20T00:02:32.353709Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:02:32.353709Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16610","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":"2026-05-20T00:02:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YVR3OWh6I8TmZS2nDGX9U36k3jusnZ9A64e3F+vkwVdsc+PxTL+eu3walhwD9pdMiiLDYY76Rde8bHF13wlyDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T01:24:50.136850Z"},"content_sha256":"916d8b4dc74cf38da6aff5d266bf388b1709289131aa9bc07f9f9f76bbb3ea54","schema_version":"1.0","event_id":"sha256:916d8b4dc74cf38da6aff5d266bf388b1709289131aa9bc07f9f9f76bbb3ea54"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:BH4ZYJ5JPORG34UNRLAC4GCOKM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tensor Cookbook: Mastering Tensors through Diagrams","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GL"],"primary_cat":"cs.LG","authors_text":"Beheshteh T. Rakhshan, Guillaume Rabusseau","submitted_at":"2026-05-15T20:21:14Z","abstract_excerpt":"High-dimensional data arise naturally in many areas of science and engineering, including machine learning, signal processing, computational physics, and statistics. Such data are often represented as tensors, multi-dimensional generalizations of matrices. While tensors provide a natural representation for multi-modal structure, their direct manipulation quickly becomes challenging as the order grows: the number of parameters increases exponentially, and algebraic expressions involving many indices become difficult to interpret and implement. Tensor networks (TNs) provide an effective framewor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.16610","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/2605.16610/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-19T19:21:56.791911Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.594759Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"b965f2d6682c8309184580906903d32d82e013933cb3c81e27081039cccab3d2"},"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":"2026-05-20T00:02:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0T/H3NSZ3suGqJypZlniZjtnaPdWckxu2uiFNgkyrY8AWOPzOLwSBbJkwh0h/5kDoUOBYpfTDht2pfmshiugCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T01:24:50.137723Z"},"content_sha256":"753f4d1cf91c2b7a56693c27663b27703378cf73a2a9b355ecd63b60b6bd47dc","schema_version":"1.0","event_id":"sha256:753f4d1cf91c2b7a56693c27663b27703378cf73a2a9b355ecd63b60b6bd47dc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BH4ZYJ5JPORG34UNRLAC4GCOKM/bundle.json","state_url":"https://pith.science/pith/BH4ZYJ5JPORG34UNRLAC4GCOKM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BH4ZYJ5JPORG34UNRLAC4GCOKM/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":"2026-05-26T01:24:50Z","links":{"resolver":"https://pith.science/pith/BH4ZYJ5JPORG34UNRLAC4GCOKM","bundle":"https://pith.science/pith/BH4ZYJ5JPORG34UNRLAC4GCOKM/bundle.json","state":"https://pith.science/pith/BH4ZYJ5JPORG34UNRLAC4GCOKM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BH4ZYJ5JPORG34UNRLAC4GCOKM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:BH4ZYJ5JPORG34UNRLAC4GCOKM","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":"2b39f46337082b57479f7fe347e97cfebf69ab56d428ddb019b21c6027d42eb9","cross_cats_sorted":["cs.GL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T20:21:14Z","title_canon_sha256":"07fca9b0ee1d9e7f61617e3aa593dbf458e51fe85dc23d16e296536f61831659"},"schema_version":"1.0","source":{"id":"2605.16610","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16610","created_at":"2026-05-20T00:02:32Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16610v1","created_at":"2026-05-20T00:02:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16610","created_at":"2026-05-20T00:02:32Z"},{"alias_kind":"pith_short_12","alias_value":"BH4ZYJ5JPORG","created_at":"2026-05-20T00:02:32Z"},{"alias_kind":"pith_short_16","alias_value":"BH4ZYJ5JPORG34UN","created_at":"2026-05-20T00:02:32Z"},{"alias_kind":"pith_short_8","alias_value":"BH4ZYJ5J","created_at":"2026-05-20T00:02:32Z"}],"graph_snapshots":[{"event_id":"sha256:753f4d1cf91c2b7a56693c27663b27703378cf73a2a9b355ecd63b60b6bd47dc","target":"graph","created_at":"2026-05-20T00:02:32Z","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":[{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T19:21:56.791911Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.594759Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.16610/integrity.json","findings":[],"snapshot_sha256":"b965f2d6682c8309184580906903d32d82e013933cb3c81e27081039cccab3d2","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"High-dimensional data arise naturally in many areas of science and engineering, including machine learning, signal processing, computational physics, and statistics. Such data are often represented as tensors, multi-dimensional generalizations of matrices. While tensors provide a natural representation for multi-modal structure, their direct manipulation quickly becomes challenging as the order grows: the number of parameters increases exponentially, and algebraic expressions involving many indices become difficult to interpret and implement. Tensor networks (TNs) provide an effective framewor","authors_text":"Beheshteh T. Rakhshan, Guillaume Rabusseau","cross_cats":["cs.GL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T20:21:14Z","title":"Tensor Cookbook: Mastering Tensors through Diagrams"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.16610","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":"sha256:916d8b4dc74cf38da6aff5d266bf388b1709289131aa9bc07f9f9f76bbb3ea54","target":"record","created_at":"2026-05-20T00:02:32Z","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":"2b39f46337082b57479f7fe347e97cfebf69ab56d428ddb019b21c6027d42eb9","cross_cats_sorted":["cs.GL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T20:21:14Z","title_canon_sha256":"07fca9b0ee1d9e7f61617e3aa593dbf458e51fe85dc23d16e296536f61831659"},"schema_version":"1.0","source":{"id":"2605.16610","kind":"arxiv","version":1}},"canonical_sha256":"09f99c27a97ba26df28d8ac02e184e532ffb80296d3a4ef1bb3678f0bbce5483","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"09f99c27a97ba26df28d8ac02e184e532ffb80296d3a4ef1bb3678f0bbce5483","first_computed_at":"2026-05-20T00:02:32.353709Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:02:32.353709Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cx9FrugCNcUjrhIpJ12K1CTBjpbKKpwZqWhEZWzlf0WoiFTAXj6j9ikEUjo24kkNVcjFHmkAXWvnQ2LL8ZQXDA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:02:32.354522Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16610","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:916d8b4dc74cf38da6aff5d266bf388b1709289131aa9bc07f9f9f76bbb3ea54","sha256:753f4d1cf91c2b7a56693c27663b27703378cf73a2a9b355ecd63b60b6bd47dc"],"state_sha256":"c860064511d454d9542f45e25b6bdd9fa41027b20105cd143c4e04392d8cfde7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tPXZup4EAHY9mSyB2IDktz9SPZIcpUxyAk/9zL7FcIORZr3zHCweNnDKPnxproknN1mmtDL98rZ4Z4f5u21oDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T01:24:50.143476Z","bundle_sha256":"98a5719c0da18e1d45928120576e7175852c1d81eb18d8a5c32304dc20edc501"}}