{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:6SRHLKGR54V3T5OSHZDED2MHEI","short_pith_number":"pith:6SRHLKGR","canonical_record":{"source":{"id":"2512.24366","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-12-30T17:25:15Z","cross_cats_sorted":[],"title_canon_sha256":"f07c46b9952282b43aebdccf217c765fad5db44440dd18dd9cea790b564d835e","abstract_canon_sha256":"edf1529df022ec2befdf1577c4cb39ca513ce5afcf2fea925da10688197fa90c"},"schema_version":"1.0"},"canonical_sha256":"f4a275a8d1ef2bb9f5d23e4641e9872222ef71f25d93136b72533afc592cfc61","source":{"kind":"arxiv","id":"2512.24366","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.24366","created_at":"2026-05-20T00:01:38Z"},{"alias_kind":"arxiv_version","alias_value":"2512.24366v2","created_at":"2026-05-20T00:01:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.24366","created_at":"2026-05-20T00:01:38Z"},{"alias_kind":"pith_short_12","alias_value":"6SRHLKGR54V3","created_at":"2026-05-20T00:01:38Z"},{"alias_kind":"pith_short_16","alias_value":"6SRHLKGR54V3T5OS","created_at":"2026-05-20T00:01:38Z"},{"alias_kind":"pith_short_8","alias_value":"6SRHLKGR","created_at":"2026-05-20T00:01:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:6SRHLKGR54V3T5OSHZDED2MHEI","target":"record","payload":{"canonical_record":{"source":{"id":"2512.24366","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-12-30T17:25:15Z","cross_cats_sorted":[],"title_canon_sha256":"f07c46b9952282b43aebdccf217c765fad5db44440dd18dd9cea790b564d835e","abstract_canon_sha256":"edf1529df022ec2befdf1577c4cb39ca513ce5afcf2fea925da10688197fa90c"},"schema_version":"1.0"},"canonical_sha256":"f4a275a8d1ef2bb9f5d23e4641e9872222ef71f25d93136b72533afc592cfc61","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:38.342152Z","signature_b64":"FAEoLVNloXZDbQNy+kzfg5dpogKzSxFSTyYV71akEB9u5aM9xaWnHzFATQso55QGT5Ov29wrR2ZXZGYAN6+yDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4a275a8d1ef2bb9f5d23e4641e9872222ef71f25d93136b72533afc592cfc61","last_reissued_at":"2026-05-20T00:01:38.341471Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:38.341471Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2512.24366","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:01:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9uVW6ZJXM6vlgtQXahvbpnRYFRiKUZ0/9petEd6EKu1osjkKf2PhWIXFcKfPm+3myv+5otBKpUcxUWZttn4oBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T15:51:27.038673Z"},"content_sha256":"78c75a7164ce42a31f9bf282f9876826572b1ad1da38ebadc60f16f83869926a","schema_version":"1.0","event_id":"sha256:78c75a7164ce42a31f9bf282f9876826572b1ad1da38ebadc60f16f83869926a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:6SRHLKGR54V3T5OSHZDED2MHEI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On the Factual Consistency of Text-based Explainable Recommendation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Ben Kabongo, Vincent Guigue","submitted_at":"2025-12-30T17:25:15Z","abstract_excerpt":"Text-based explainable recommendation aims to generate natural-language explanations that justify item recommendations, to improve user trust and system transparency. Although recent advances leverage LLMs to produce fluent outputs, a critical question remains underexplored: are these explanations factually consistent with the available evidence? We introduce a comprehensive framework for evaluating the factual consistency of text-based explainable recommenders. We design a prompting-based pipeline that uses LLMs to extract atomic explanatory statements from reviews, thereby constructing a gro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.24366","kind":"arxiv","version":2},"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/2512.24366/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":"2026-05-20T00:01:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X10/H2cSKfYSUm84QQGPujNedelkpXn0tfMwFHI2Mf2U0HOTJhkOAnfBSqQDAkKQYMJBmtUXVpNv/LOt+6B3CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T15:51:27.039403Z"},"content_sha256":"d24c49dad8af818fb4ea02823bcb42d3ef6c7cc2540468d6de1ca18c191dc275","schema_version":"1.0","event_id":"sha256:d24c49dad8af818fb4ea02823bcb42d3ef6c7cc2540468d6de1ca18c191dc275"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6SRHLKGR54V3T5OSHZDED2MHEI/bundle.json","state_url":"https://pith.science/pith/6SRHLKGR54V3T5OSHZDED2MHEI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6SRHLKGR54V3T5OSHZDED2MHEI/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":"2026-05-26T15:51:27Z","links":{"resolver":"https://pith.science/pith/6SRHLKGR54V3T5OSHZDED2MHEI","bundle":"https://pith.science/pith/6SRHLKGR54V3T5OSHZDED2MHEI/bundle.json","state":"https://pith.science/pith/6SRHLKGR54V3T5OSHZDED2MHEI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6SRHLKGR54V3T5OSHZDED2MHEI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6SRHLKGR54V3T5OSHZDED2MHEI","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":"edf1529df022ec2befdf1577c4cb39ca513ce5afcf2fea925da10688197fa90c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-12-30T17:25:15Z","title_canon_sha256":"f07c46b9952282b43aebdccf217c765fad5db44440dd18dd9cea790b564d835e"},"schema_version":"1.0","source":{"id":"2512.24366","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.24366","created_at":"2026-05-20T00:01:38Z"},{"alias_kind":"arxiv_version","alias_value":"2512.24366v2","created_at":"2026-05-20T00:01:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.24366","created_at":"2026-05-20T00:01:38Z"},{"alias_kind":"pith_short_12","alias_value":"6SRHLKGR54V3","created_at":"2026-05-20T00:01:38Z"},{"alias_kind":"pith_short_16","alias_value":"6SRHLKGR54V3T5OS","created_at":"2026-05-20T00:01:38Z"},{"alias_kind":"pith_short_8","alias_value":"6SRHLKGR","created_at":"2026-05-20T00:01:38Z"}],"graph_snapshots":[{"event_id":"sha256:d24c49dad8af818fb4ea02823bcb42d3ef6c7cc2540468d6de1ca18c191dc275","target":"graph","created_at":"2026-05-20T00:01:38Z","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/2512.24366/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text-based explainable recommendation aims to generate natural-language explanations that justify item recommendations, to improve user trust and system transparency. Although recent advances leverage LLMs to produce fluent outputs, a critical question remains underexplored: are these explanations factually consistent with the available evidence? We introduce a comprehensive framework for evaluating the factual consistency of text-based explainable recommenders. We design a prompting-based pipeline that uses LLMs to extract atomic explanatory statements from reviews, thereby constructing a gro","authors_text":"Ben Kabongo, Vincent Guigue","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-12-30T17:25:15Z","title":"On the Factual Consistency of Text-based Explainable Recommendation Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.24366","kind":"arxiv","version":2},"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":"sha256:78c75a7164ce42a31f9bf282f9876826572b1ad1da38ebadc60f16f83869926a","target":"record","created_at":"2026-05-20T00:01:38Z","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":"edf1529df022ec2befdf1577c4cb39ca513ce5afcf2fea925da10688197fa90c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-12-30T17:25:15Z","title_canon_sha256":"f07c46b9952282b43aebdccf217c765fad5db44440dd18dd9cea790b564d835e"},"schema_version":"1.0","source":{"id":"2512.24366","kind":"arxiv","version":2}},"canonical_sha256":"f4a275a8d1ef2bb9f5d23e4641e9872222ef71f25d93136b72533afc592cfc61","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f4a275a8d1ef2bb9f5d23e4641e9872222ef71f25d93136b72533afc592cfc61","first_computed_at":"2026-05-20T00:01:38.341471Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:38.341471Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FAEoLVNloXZDbQNy+kzfg5dpogKzSxFSTyYV71akEB9u5aM9xaWnHzFATQso55QGT5Ov29wrR2ZXZGYAN6+yDQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:38.342152Z","signed_message":"canonical_sha256_bytes"},"source_id":"2512.24366","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:78c75a7164ce42a31f9bf282f9876826572b1ad1da38ebadc60f16f83869926a","sha256:d24c49dad8af818fb4ea02823bcb42d3ef6c7cc2540468d6de1ca18c191dc275"],"state_sha256":"6ecbf62dfe09044c16b8ae5e993fecba99943f29f5567dfb0de6df56e24c7281"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TTXl5uFX8BpnqCXWuG0M3w4hSnS0S0nfzltswrtjIKLkZ5AubMMVau9psQhCdn1LHfeP8eQ5EPzhbsTZsEqdBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T15:51:27.042568Z","bundle_sha256":"04e6e1eb5388cf63b6c6e456c72c205b25438f60a8142eb64b5f823a2e6667c8"}}