{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:NWYULQQAZU7LSBEC5YCDVBXYDK","short_pith_number":"pith:NWYULQQA","canonical_record":{"source":{"id":"2606.05901","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-04T09:07:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7df7122f34c71f055764e7e985dd9a685fc3f728e8492de25f43f75325772339","abstract_canon_sha256":"b91db9b892fdf699e0fdc43544621d01b2ca50027dee7c2e30e6ece7fcee6810"},"schema_version":"1.0"},"canonical_sha256":"6db145c200cd3eb90482ee043a86f81a9cf95e495f024eafbf3229374f1cd946","source":{"kind":"arxiv","id":"2606.05901","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.05901","created_at":"2026-06-05T01:15:26Z"},{"alias_kind":"arxiv_version","alias_value":"2606.05901v1","created_at":"2026-06-05T01:15:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05901","created_at":"2026-06-05T01:15:26Z"},{"alias_kind":"pith_short_12","alias_value":"NWYULQQAZU7L","created_at":"2026-06-05T01:15:26Z"},{"alias_kind":"pith_short_16","alias_value":"NWYULQQAZU7LSBEC","created_at":"2026-06-05T01:15:26Z"},{"alias_kind":"pith_short_8","alias_value":"NWYULQQA","created_at":"2026-06-05T01:15:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:NWYULQQAZU7LSBEC5YCDVBXYDK","target":"record","payload":{"canonical_record":{"source":{"id":"2606.05901","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-04T09:07:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7df7122f34c71f055764e7e985dd9a685fc3f728e8492de25f43f75325772339","abstract_canon_sha256":"b91db9b892fdf699e0fdc43544621d01b2ca50027dee7c2e30e6ece7fcee6810"},"schema_version":"1.0"},"canonical_sha256":"6db145c200cd3eb90482ee043a86f81a9cf95e495f024eafbf3229374f1cd946","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:15:26.832754Z","signature_b64":"ujxQaRWIs6MJ9psdRGQFPw7RDTDFrw7zUK/WXW8DDjtgbKD4RNWQU6jtd9k8dsL+Srw7C0Btq2MRB2rj198NBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6db145c200cd3eb90482ee043a86f81a9cf95e495f024eafbf3229374f1cd946","last_reissued_at":"2026-06-05T01:15:26.832376Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:15:26.832376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.05901","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":"2026-06-05T01:15:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kE7O8aUjkFkrj2P+Y28ImlUhTgITGsM3dbLASWkJIbL93djs2Di9lOz61u4Mpz1Z0x2T7wPe1ybn3W0J9gFqDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T20:18:52.959835Z"},"content_sha256":"ebeb843ec49f5d11cf220be1f1229e01932bd1338ccc5b8d4b5c961a942f078b","schema_version":"1.0","event_id":"sha256:ebeb843ec49f5d11cf220be1f1229e01932bd1338ccc5b8d4b5c961a942f078b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:NWYULQQAZU7LSBEC5YCDVBXYDK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Christopher J. Wedge, Danny Dixon, Jacek Ca{\\l}a, Joshua Stutter","submitted_at":"2026-06-04T09:07:06Z","abstract_excerpt":"Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing. Despite these advances, LLMs and LLM-based systems remain prone to a variety of failure modes. Retrieval-augmented generation (RAG) systems have emerged as a common deployment scenario seeking to both avoid the well known risk of the LLM \"hallucinating\" information, and to enable reasoning and question answering over proprietary information that the LLM did not have access to during training without resorting to expensive model fine-tuning.\n  In this work, we explore the idea of using a li"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05901","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/2606.05901/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":"2026-06-05T01:15:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mHWjvLIzi7WG3zUcVqHSmUISudG2sRcp0t/7yxWOW7NegeP/e3ijxlOgfpsee6ArIv0mk+Jll2Ts0X6t4XQfAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T20:18:52.960205Z"},"content_sha256":"0b23a064b07e492c855d56798f350d403024ed3d331a7836d6523ac3045ccf3d","schema_version":"1.0","event_id":"sha256:0b23a064b07e492c855d56798f350d403024ed3d331a7836d6523ac3045ccf3d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NWYULQQAZU7LSBEC5YCDVBXYDK/bundle.json","state_url":"https://pith.science/pith/NWYULQQAZU7LSBEC5YCDVBXYDK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NWYULQQAZU7LSBEC5YCDVBXYDK/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":"2026-06-20T20:18:52Z","links":{"resolver":"https://pith.science/pith/NWYULQQAZU7LSBEC5YCDVBXYDK","bundle":"https://pith.science/pith/NWYULQQAZU7LSBEC5YCDVBXYDK/bundle.json","state":"https://pith.science/pith/NWYULQQAZU7LSBEC5YCDVBXYDK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NWYULQQAZU7LSBEC5YCDVBXYDK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:NWYULQQAZU7LSBEC5YCDVBXYDK","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":"b91db9b892fdf699e0fdc43544621d01b2ca50027dee7c2e30e6ece7fcee6810","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-04T09:07:06Z","title_canon_sha256":"7df7122f34c71f055764e7e985dd9a685fc3f728e8492de25f43f75325772339"},"schema_version":"1.0","source":{"id":"2606.05901","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.05901","created_at":"2026-06-05T01:15:26Z"},{"alias_kind":"arxiv_version","alias_value":"2606.05901v1","created_at":"2026-06-05T01:15:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05901","created_at":"2026-06-05T01:15:26Z"},{"alias_kind":"pith_short_12","alias_value":"NWYULQQAZU7L","created_at":"2026-06-05T01:15:26Z"},{"alias_kind":"pith_short_16","alias_value":"NWYULQQAZU7LSBEC","created_at":"2026-06-05T01:15:26Z"},{"alias_kind":"pith_short_8","alias_value":"NWYULQQA","created_at":"2026-06-05T01:15:26Z"}],"graph_snapshots":[{"event_id":"sha256:0b23a064b07e492c855d56798f350d403024ed3d331a7836d6523ac3045ccf3d","target":"graph","created_at":"2026-06-05T01:15:26Z","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/2606.05901/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing. Despite these advances, LLMs and LLM-based systems remain prone to a variety of failure modes. Retrieval-augmented generation (RAG) systems have emerged as a common deployment scenario seeking to both avoid the well known risk of the LLM \"hallucinating\" information, and to enable reasoning and question answering over proprietary information that the LLM did not have access to during training without resorting to expensive model fine-tuning.\n  In this work, we explore the idea of using a li","authors_text":"Christopher J. Wedge, Danny Dixon, Jacek Ca{\\l}a, Joshua Stutter","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-04T09:07:06Z","title":"Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05901","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:ebeb843ec49f5d11cf220be1f1229e01932bd1338ccc5b8d4b5c961a942f078b","target":"record","created_at":"2026-06-05T01:15:26Z","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":"b91db9b892fdf699e0fdc43544621d01b2ca50027dee7c2e30e6ece7fcee6810","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-04T09:07:06Z","title_canon_sha256":"7df7122f34c71f055764e7e985dd9a685fc3f728e8492de25f43f75325772339"},"schema_version":"1.0","source":{"id":"2606.05901","kind":"arxiv","version":1}},"canonical_sha256":"6db145c200cd3eb90482ee043a86f81a9cf95e495f024eafbf3229374f1cd946","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6db145c200cd3eb90482ee043a86f81a9cf95e495f024eafbf3229374f1cd946","first_computed_at":"2026-06-05T01:15:26.832376Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-05T01:15:26.832376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ujxQaRWIs6MJ9psdRGQFPw7RDTDFrw7zUK/WXW8DDjtgbKD4RNWQU6jtd9k8dsL+Srw7C0Btq2MRB2rj198NBQ==","signature_status":"signed_v1","signed_at":"2026-06-05T01:15:26.832754Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.05901","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ebeb843ec49f5d11cf220be1f1229e01932bd1338ccc5b8d4b5c961a942f078b","sha256:0b23a064b07e492c855d56798f350d403024ed3d331a7836d6523ac3045ccf3d"],"state_sha256":"5717a7e2fe5a82de4cc85b75e400bda6421b6f4b0b64ca35f19bc16a0c14c43e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NrOmcksVZdiODmtKAtPi/7kZg4tynf6XCFM/m5t8nMkLb732Jx+/XppiM82celM7D8xaVzlPREhEUAz01npNAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T20:18:52.962262Z","bundle_sha256":"3cf8368ab70859eca5b2930848341a95a08ce3a4484726e7c969b7e5da54d0ab"}}