{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:6T7WBBGSIQCL7EOPJOV26VB6YJ","short_pith_number":"pith:6T7WBBGS","canonical_record":{"source":{"id":"2605.22923","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2026-05-21T18:01:51Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"a1b9c7f64901cddb30c50ebfb82e684e4ee9a95727c24c4c16c87cdcdc21cc57","abstract_canon_sha256":"0a23d481bd4e21c617f732bbd3376bff6d06f8ddff5457fd505350ddadbc14e8"},"schema_version":"1.0"},"canonical_sha256":"f4ff6084d24404bf91cf4babaf543ec24fc55efcf2af7e8a314f2ae9b2209f85","source":{"kind":"arxiv","id":"2605.22923","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.22923","created_at":"2026-05-25T02:01:30Z"},{"alias_kind":"arxiv_version","alias_value":"2605.22923v1","created_at":"2026-05-25T02:01:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.22923","created_at":"2026-05-25T02:01:30Z"},{"alias_kind":"pith_short_12","alias_value":"6T7WBBGSIQCL","created_at":"2026-05-25T02:01:30Z"},{"alias_kind":"pith_short_16","alias_value":"6T7WBBGSIQCL7EOP","created_at":"2026-05-25T02:01:30Z"},{"alias_kind":"pith_short_8","alias_value":"6T7WBBGS","created_at":"2026-05-25T02:01:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:6T7WBBGSIQCL7EOPJOV26VB6YJ","target":"record","payload":{"canonical_record":{"source":{"id":"2605.22923","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2026-05-21T18:01:51Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"a1b9c7f64901cddb30c50ebfb82e684e4ee9a95727c24c4c16c87cdcdc21cc57","abstract_canon_sha256":"0a23d481bd4e21c617f732bbd3376bff6d06f8ddff5457fd505350ddadbc14e8"},"schema_version":"1.0"},"canonical_sha256":"f4ff6084d24404bf91cf4babaf543ec24fc55efcf2af7e8a314f2ae9b2209f85","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:30.253223Z","signature_b64":"7U/9DjHTSx28b9Q9skXegQ/Yj97NHxNSOsQZ29C+wTGqMEouFcTP16X95N65cLPOgawU70VjOQfqpHiljR0GAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4ff6084d24404bf91cf4babaf543ec24fc55efcf2af7e8a314f2ae9b2209f85","last_reissued_at":"2026-05-25T02:01:30.252757Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:30.252757Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.22923","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":"2026-05-25T02:01:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1aUYGwpdToCPFeQ62GA6bm4gb9FfEalHtHIp0+Wwli8weY+msdKMTUV0qBgxoMYMHvWYnE0XACsYFICSO8HPDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T11:29:34.693647Z"},"content_sha256":"96b9db78068c751b84b4933878b97ca06d08f79f69fc403062f2b6d97a37e281","schema_version":"1.0","event_id":"sha256:96b9db78068c751b84b4933878b97ca06d08f79f69fc403062f2b6d97a37e281"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:6T7WBBGSIQCL7EOPJOV26VB6YJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AI-Friendly LaTeX: Using LaTeX Code as a Knowledge Source for Retrieval-Augmented Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Tom Verhoeff","submitted_at":"2026-05-21T18:01:51Z","abstract_excerpt":"Large language models can answer questions about textbooks, lecture notes, and programming exercises more reliably when their answers are grounded in an explicit knowledge source. Retrieval-augmented generation (RAG) is a common approach: relevant fragments of a document are retrieved and inserted into the model context before answering. For mathematical and technical material, the original LaTeX source can be a better starting point than a PDF, because it contains structural information, labels, sectioning commands, macros, and authorial intent that are often lost or distorted in PDF extracti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.22923","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/2605.22923/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":"2026-05-25T02:01:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LP+WaPTdoy2bbcXmgXg08Sfz0ql7gTAKGGp0JlDe1Gjbgqb6pfD9q/tfCxXhlb9jlryr5XPO9QTX8A/RFS59Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T11:29:34.694469Z"},"content_sha256":"20511b4ef3e5699af6e98d474ad6c25ba3f86d3e2e716bc45958fdfe18535013","schema_version":"1.0","event_id":"sha256:20511b4ef3e5699af6e98d474ad6c25ba3f86d3e2e716bc45958fdfe18535013"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6T7WBBGSIQCL7EOPJOV26VB6YJ/bundle.json","state_url":"https://pith.science/pith/6T7WBBGSIQCL7EOPJOV26VB6YJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6T7WBBGSIQCL7EOPJOV26VB6YJ/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":"2026-06-10T11:29:34Z","links":{"resolver":"https://pith.science/pith/6T7WBBGSIQCL7EOPJOV26VB6YJ","bundle":"https://pith.science/pith/6T7WBBGSIQCL7EOPJOV26VB6YJ/bundle.json","state":"https://pith.science/pith/6T7WBBGSIQCL7EOPJOV26VB6YJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6T7WBBGSIQCL7EOPJOV26VB6YJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:6T7WBBGSIQCL7EOPJOV26VB6YJ","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":"0a23d481bd4e21c617f732bbd3376bff6d06f8ddff5457fd505350ddadbc14e8","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2026-05-21T18:01:51Z","title_canon_sha256":"a1b9c7f64901cddb30c50ebfb82e684e4ee9a95727c24c4c16c87cdcdc21cc57"},"schema_version":"1.0","source":{"id":"2605.22923","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.22923","created_at":"2026-05-25T02:01:30Z"},{"alias_kind":"arxiv_version","alias_value":"2605.22923v1","created_at":"2026-05-25T02:01:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.22923","created_at":"2026-05-25T02:01:30Z"},{"alias_kind":"pith_short_12","alias_value":"6T7WBBGSIQCL","created_at":"2026-05-25T02:01:30Z"},{"alias_kind":"pith_short_16","alias_value":"6T7WBBGSIQCL7EOP","created_at":"2026-05-25T02:01:30Z"},{"alias_kind":"pith_short_8","alias_value":"6T7WBBGS","created_at":"2026-05-25T02:01:30Z"}],"graph_snapshots":[{"event_id":"sha256:20511b4ef3e5699af6e98d474ad6c25ba3f86d3e2e716bc45958fdfe18535013","target":"graph","created_at":"2026-05-25T02:01:30Z","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/2605.22923/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models can answer questions about textbooks, lecture notes, and programming exercises more reliably when their answers are grounded in an explicit knowledge source. Retrieval-augmented generation (RAG) is a common approach: relevant fragments of a document are retrieved and inserted into the model context before answering. For mathematical and technical material, the original LaTeX source can be a better starting point than a PDF, because it contains structural information, labels, sectioning commands, macros, and authorial intent that are often lost or distorted in PDF extracti","authors_text":"Tom Verhoeff","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2026-05-21T18:01:51Z","title":"AI-Friendly LaTeX: Using LaTeX Code as a Knowledge Source for Retrieval-Augmented Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.22923","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":"sha256:96b9db78068c751b84b4933878b97ca06d08f79f69fc403062f2b6d97a37e281","target":"record","created_at":"2026-05-25T02:01:30Z","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":"0a23d481bd4e21c617f732bbd3376bff6d06f8ddff5457fd505350ddadbc14e8","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2026-05-21T18:01:51Z","title_canon_sha256":"a1b9c7f64901cddb30c50ebfb82e684e4ee9a95727c24c4c16c87cdcdc21cc57"},"schema_version":"1.0","source":{"id":"2605.22923","kind":"arxiv","version":1}},"canonical_sha256":"f4ff6084d24404bf91cf4babaf543ec24fc55efcf2af7e8a314f2ae9b2209f85","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f4ff6084d24404bf91cf4babaf543ec24fc55efcf2af7e8a314f2ae9b2209f85","first_computed_at":"2026-05-25T02:01:30.252757Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-25T02:01:30.252757Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7U/9DjHTSx28b9Q9skXegQ/Yj97NHxNSOsQZ29C+wTGqMEouFcTP16X95N65cLPOgawU70VjOQfqpHiljR0GAQ==","signature_status":"signed_v1","signed_at":"2026-05-25T02:01:30.253223Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.22923","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:96b9db78068c751b84b4933878b97ca06d08f79f69fc403062f2b6d97a37e281","sha256:20511b4ef3e5699af6e98d474ad6c25ba3f86d3e2e716bc45958fdfe18535013"],"state_sha256":"c010c39dbb33be01077dd52e5b9c1fe68b40c33c80785e7acc2fbcb57bb1fa91"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XBfkC9V2iEDKsaZPF0gtiPgJ3Uen5yA3EktCvy7U1DkqBWd9DYrA6+6Wevji8kL5SROKH3uFY9hRxtiuqEkRDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T11:29:34.698882Z","bundle_sha256":"22b6ae1d78d9e4a5087354923a0c812e7d82763f16b41a89307d4ae5476a71bd"}}