{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:STDZXIHG2UYWIYMIUMW7D2B4ZN","short_pith_number":"pith:STDZXIHG","canonical_record":{"source":{"id":"2506.14084","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-17T00:52:21Z","cross_cats_sorted":[],"title_canon_sha256":"eff6f4e3ad6619417d414a21c0fa64d271352b1271e8c9e2756a46720e961930","abstract_canon_sha256":"5a791c54f3f1d65d52d3a810843eaceda087bafd152741eb8fc29bf714bd102d"},"schema_version":"1.0"},"canonical_sha256":"94c79ba0e6d531646188a32df1e83ccb7b6fad22d42543ad8244ba40adbff645","source":{"kind":"arxiv","id":"2506.14084","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.14084","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"arxiv_version","alias_value":"2506.14084v1","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14084","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"pith_short_12","alias_value":"STDZXIHG2UYW","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"pith_short_16","alias_value":"STDZXIHG2UYWIYMI","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"pith_short_8","alias_value":"STDZXIHG","created_at":"2026-07-05T11:22:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:STDZXIHG2UYWIYMIUMW7D2B4ZN","target":"record","payload":{"canonical_record":{"source":{"id":"2506.14084","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-17T00:52:21Z","cross_cats_sorted":[],"title_canon_sha256":"eff6f4e3ad6619417d414a21c0fa64d271352b1271e8c9e2756a46720e961930","abstract_canon_sha256":"5a791c54f3f1d65d52d3a810843eaceda087bafd152741eb8fc29bf714bd102d"},"schema_version":"1.0"},"canonical_sha256":"94c79ba0e6d531646188a32df1e83ccb7b6fad22d42543ad8244ba40adbff645","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:48.771676Z","signature_b64":"QXrd5tKf1NGHGsREV6JhNLQy+xT1iCYbZBFUWn9DmFE+zGJxzqAPWwWBk0pWkhRmGoR9SPK5qrE7eDNsTOLBAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94c79ba0e6d531646188a32df1e83ccb7b6fad22d42543ad8244ba40adbff645","last_reissued_at":"2026-07-05T11:22:48.771183Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:48.771183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.14084","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":"2026-07-05T11:22:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZxR7xSjBdt8w9kBAy+iH47Vw63lhHQQ39RYQ+pWXgyPCNBNEv2wwiB7vGAsuaNihnD6ctXma3kuYJMvh9jJZBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T23:13:44.732283Z"},"content_sha256":"a88cd67f63dcf680b39c001277b4b425169f3f4ea0bff4a97c7dde9ff896c44b","schema_version":"1.0","event_id":"sha256:a88cd67f63dcf680b39c001277b4b425169f3f4ea0bff4a97c7dde9ff896c44b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:STDZXIHG2UYWIYMIUMW7D2B4ZN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Lightweight Relevance Grader in RAG","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Taehee Jeong","submitted_at":"2025-06-17T00:52:21Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) addresses limitations of large language models (LLMs) by leveraging a vector database to provide more accurate and up-to-date information. When a user submits a query, RAG executes a vector search to find relevant documents, which are then used to generate a response. However, ensuring the relevance of retrieved documents with a query would be a big challenge. To address this, a secondary model, known as a relevant grader, can be served to verify its relevance. To reduce computational requirements of a relevant grader, a lightweight small language model is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14084","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/2506.14084/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":"2026-07-05T11:22:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oRNyHPp3AFoKPoOAKuJbXJC+kRB9i1UWmrgT49SNulnzEY/YbIvIfy3h5GYlVzbdNglVcEws8hiHivxXq3WpBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T23:13:44.732655Z"},"content_sha256":"8e2891ed36ff50f46b994ba1d1f5ef1f9af766237a75107dadd5575044945ec6","schema_version":"1.0","event_id":"sha256:8e2891ed36ff50f46b994ba1d1f5ef1f9af766237a75107dadd5575044945ec6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/STDZXIHG2UYWIYMIUMW7D2B4ZN/bundle.json","state_url":"https://pith.science/pith/STDZXIHG2UYWIYMIUMW7D2B4ZN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/STDZXIHG2UYWIYMIUMW7D2B4ZN/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":"2026-08-22T23:13:44Z","links":{"resolver":"https://pith.science/pith/STDZXIHG2UYWIYMIUMW7D2B4ZN","bundle":"https://pith.science/pith/STDZXIHG2UYWIYMIUMW7D2B4ZN/bundle.json","state":"https://pith.science/pith/STDZXIHG2UYWIYMIUMW7D2B4ZN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/STDZXIHG2UYWIYMIUMW7D2B4ZN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:STDZXIHG2UYWIYMIUMW7D2B4ZN","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":"5a791c54f3f1d65d52d3a810843eaceda087bafd152741eb8fc29bf714bd102d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-17T00:52:21Z","title_canon_sha256":"eff6f4e3ad6619417d414a21c0fa64d271352b1271e8c9e2756a46720e961930"},"schema_version":"1.0","source":{"id":"2506.14084","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.14084","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"arxiv_version","alias_value":"2506.14084v1","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14084","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"pith_short_12","alias_value":"STDZXIHG2UYW","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"pith_short_16","alias_value":"STDZXIHG2UYWIYMI","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"pith_short_8","alias_value":"STDZXIHG","created_at":"2026-07-05T11:22:48Z"}],"graph_snapshots":[{"event_id":"sha256:8e2891ed36ff50f46b994ba1d1f5ef1f9af766237a75107dadd5575044945ec6","target":"graph","created_at":"2026-07-05T11:22:48Z","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/2506.14084/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Retrieval-Augmented Generation (RAG) addresses limitations of large language models (LLMs) by leveraging a vector database to provide more accurate and up-to-date information. When a user submits a query, RAG executes a vector search to find relevant documents, which are then used to generate a response. However, ensuring the relevance of retrieved documents with a query would be a big challenge. To address this, a secondary model, known as a relevant grader, can be served to verify its relevance. To reduce computational requirements of a relevant grader, a lightweight small language model is ","authors_text":"Taehee Jeong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-17T00:52:21Z","title":"Lightweight Relevance Grader in RAG"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14084","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:a88cd67f63dcf680b39c001277b4b425169f3f4ea0bff4a97c7dde9ff896c44b","target":"record","created_at":"2026-07-05T11:22:48Z","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":"5a791c54f3f1d65d52d3a810843eaceda087bafd152741eb8fc29bf714bd102d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-17T00:52:21Z","title_canon_sha256":"eff6f4e3ad6619417d414a21c0fa64d271352b1271e8c9e2756a46720e961930"},"schema_version":"1.0","source":{"id":"2506.14084","kind":"arxiv","version":1}},"canonical_sha256":"94c79ba0e6d531646188a32df1e83ccb7b6fad22d42543ad8244ba40adbff645","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"94c79ba0e6d531646188a32df1e83ccb7b6fad22d42543ad8244ba40adbff645","first_computed_at":"2026-07-05T11:22:48.771183Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:22:48.771183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QXrd5tKf1NGHGsREV6JhNLQy+xT1iCYbZBFUWn9DmFE+zGJxzqAPWwWBk0pWkhRmGoR9SPK5qrE7eDNsTOLBAw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:22:48.771676Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.14084","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a88cd67f63dcf680b39c001277b4b425169f3f4ea0bff4a97c7dde9ff896c44b","sha256:8e2891ed36ff50f46b994ba1d1f5ef1f9af766237a75107dadd5575044945ec6"],"state_sha256":"cdf0ae2156470ca077090ed78ce93752ae15a083a1b623cdeb415d12fcea3657"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PErj9Uq1RhMn1je9XDuku4YEaJ9dV6yv96WKDBSkHqzOL6CywMQgWE9eTFdDD+ztiV9e8i8DN1ROm70yQl7gCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T23:13:44.735255Z","bundle_sha256":"026a816c346a184fe49256ffd8ab3a394fc0b8e050592e348e63320d9ce5ebf8"}}