{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:SOXIFCWJNEE72TCWULTYQDFYBQ","short_pith_number":"pith:SOXIFCWJ","canonical_record":{"source":{"id":"2506.14412","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2025-06-17T11:14:22Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"68781d5f16226efbe4b6ae67b12379fc3d277cfc6f28f27e4be9bbec9a0fad70","abstract_canon_sha256":"0368cfd11a60a470c6aa140d179eafedbf48ceabc11ddeb17fb3fd677092833b"},"schema_version":"1.0"},"canonical_sha256":"93ae828ac96909fd4c56a2e7880cb80c13cfc8b2e423a9b36bf49c68cb0f0b2f","source":{"kind":"arxiv","id":"2506.14412","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.14412","created_at":"2026-07-05T11:52:21Z"},{"alias_kind":"arxiv_version","alias_value":"2506.14412v2","created_at":"2026-07-05T11:52:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14412","created_at":"2026-07-05T11:52:21Z"},{"alias_kind":"pith_short_12","alias_value":"SOXIFCWJNEE7","created_at":"2026-07-05T11:52:21Z"},{"alias_kind":"pith_short_16","alias_value":"SOXIFCWJNEE72TCW","created_at":"2026-07-05T11:52:21Z"},{"alias_kind":"pith_short_8","alias_value":"SOXIFCWJ","created_at":"2026-07-05T11:52:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:SOXIFCWJNEE72TCWULTYQDFYBQ","target":"record","payload":{"canonical_record":{"source":{"id":"2506.14412","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2025-06-17T11:14:22Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"68781d5f16226efbe4b6ae67b12379fc3d277cfc6f28f27e4be9bbec9a0fad70","abstract_canon_sha256":"0368cfd11a60a470c6aa140d179eafedbf48ceabc11ddeb17fb3fd677092833b"},"schema_version":"1.0"},"canonical_sha256":"93ae828ac96909fd4c56a2e7880cb80c13cfc8b2e423a9b36bf49c68cb0f0b2f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:21.393979Z","signature_b64":"kTJfCEkiz6UspOwW4L6jqNgeZYaqzI+E5leAp8krWO8oGmsEMNJvJqaRnKOQYjhIELH6Mrln5EDlSMRla5FrAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93ae828ac96909fd4c56a2e7880cb80c13cfc8b2e423a9b36bf49c68cb0f0b2f","last_reissued_at":"2026-07-05T11:52:21.393487Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:21.393487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.14412","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":"2026-07-05T11:52:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UG+QTITGFoyba1qeK/eAGc9WmCQHek/1oEhbRrLCNEV+mwvq4Ki67vlgU8BKfAxOC7Zt3rmxCd09g0PuLGExAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T07:14:52.056579Z"},"content_sha256":"483f38a51d2cc2249e2083f1cfbe1f02e5e410bf45ced684969631234b7e6167","schema_version":"1.0","event_id":"sha256:483f38a51d2cc2249e2083f1cfbe1f02e5e410bf45ced684969631234b7e6167"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:SOXIFCWJNEE72TCWULTYQDFYBQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RAGtifier: Evaluating RAG Generation Approaches of State-of-the-Art RAG Systems for the SIGIR LiveRAG Competition","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.IR","authors_text":"Hailay Teklehaymanot, Oleh Astappiev, Tim Cofala, William Xion","submitted_at":"2025-06-17T11:14:22Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) enriches Large Language Models (LLMs) by combining their internal, parametric knowledge with external, non-parametric sources, with the goal of improving factual correctness and minimizing hallucinations. The LiveRAG 2025 challenge explores RAG solutions to maximize accuracy on DataMorgana's QA pairs, which are composed of single-hop and multi-hop questions. The challenge provides access to sparse OpenSearch and dense Pinecone indices of the Fineweb 10BT dataset. It restricts model use to LLMs with up to 10B parameters and final answer generation with Falco"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14412","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/2506.14412/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":"2026-07-05T11:52:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e8wprFgkN7yN1+rx2pO5PLChHSRuH4L1HIpbUVEmCbvsUJ4Cl6YTD8o15SIyP8eXAzYxL5N6QyahZUNs+aH0Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T07:14:52.057458Z"},"content_sha256":"a0d8fc4834469c95f492d9af0e5de1c7077cae72ac65d1ff17bd2cfbf49cba82","schema_version":"1.0","event_id":"sha256:a0d8fc4834469c95f492d9af0e5de1c7077cae72ac65d1ff17bd2cfbf49cba82"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SOXIFCWJNEE72TCWULTYQDFYBQ/bundle.json","state_url":"https://pith.science/pith/SOXIFCWJNEE72TCWULTYQDFYBQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SOXIFCWJNEE72TCWULTYQDFYBQ/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":"2026-08-07T07:14:52Z","links":{"resolver":"https://pith.science/pith/SOXIFCWJNEE72TCWULTYQDFYBQ","bundle":"https://pith.science/pith/SOXIFCWJNEE72TCWULTYQDFYBQ/bundle.json","state":"https://pith.science/pith/SOXIFCWJNEE72TCWULTYQDFYBQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SOXIFCWJNEE72TCWULTYQDFYBQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:SOXIFCWJNEE72TCWULTYQDFYBQ","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":"0368cfd11a60a470c6aa140d179eafedbf48ceabc11ddeb17fb3fd677092833b","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2025-06-17T11:14:22Z","title_canon_sha256":"68781d5f16226efbe4b6ae67b12379fc3d277cfc6f28f27e4be9bbec9a0fad70"},"schema_version":"1.0","source":{"id":"2506.14412","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.14412","created_at":"2026-07-05T11:52:21Z"},{"alias_kind":"arxiv_version","alias_value":"2506.14412v2","created_at":"2026-07-05T11:52:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14412","created_at":"2026-07-05T11:52:21Z"},{"alias_kind":"pith_short_12","alias_value":"SOXIFCWJNEE7","created_at":"2026-07-05T11:52:21Z"},{"alias_kind":"pith_short_16","alias_value":"SOXIFCWJNEE72TCW","created_at":"2026-07-05T11:52:21Z"},{"alias_kind":"pith_short_8","alias_value":"SOXIFCWJ","created_at":"2026-07-05T11:52:21Z"}],"graph_snapshots":[{"event_id":"sha256:a0d8fc4834469c95f492d9af0e5de1c7077cae72ac65d1ff17bd2cfbf49cba82","target":"graph","created_at":"2026-07-05T11:52:21Z","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/2506.14412/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Retrieval-Augmented Generation (RAG) enriches Large Language Models (LLMs) by combining their internal, parametric knowledge with external, non-parametric sources, with the goal of improving factual correctness and minimizing hallucinations. The LiveRAG 2025 challenge explores RAG solutions to maximize accuracy on DataMorgana's QA pairs, which are composed of single-hop and multi-hop questions. The challenge provides access to sparse OpenSearch and dense Pinecone indices of the Fineweb 10BT dataset. It restricts model use to LLMs with up to 10B parameters and final answer generation with Falco","authors_text":"Hailay Teklehaymanot, Oleh Astappiev, Tim Cofala, William Xion","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2025-06-17T11:14:22Z","title":"RAGtifier: Evaluating RAG Generation Approaches of State-of-the-Art RAG Systems for the SIGIR LiveRAG Competition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14412","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":"sha256:483f38a51d2cc2249e2083f1cfbe1f02e5e410bf45ced684969631234b7e6167","target":"record","created_at":"2026-07-05T11:52:21Z","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":"0368cfd11a60a470c6aa140d179eafedbf48ceabc11ddeb17fb3fd677092833b","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2025-06-17T11:14:22Z","title_canon_sha256":"68781d5f16226efbe4b6ae67b12379fc3d277cfc6f28f27e4be9bbec9a0fad70"},"schema_version":"1.0","source":{"id":"2506.14412","kind":"arxiv","version":2}},"canonical_sha256":"93ae828ac96909fd4c56a2e7880cb80c13cfc8b2e423a9b36bf49c68cb0f0b2f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"93ae828ac96909fd4c56a2e7880cb80c13cfc8b2e423a9b36bf49c68cb0f0b2f","first_computed_at":"2026-07-05T11:52:21.393487Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:52:21.393487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kTJfCEkiz6UspOwW4L6jqNgeZYaqzI+E5leAp8krWO8oGmsEMNJvJqaRnKOQYjhIELH6Mrln5EDlSMRla5FrAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:52:21.393979Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.14412","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:483f38a51d2cc2249e2083f1cfbe1f02e5e410bf45ced684969631234b7e6167","sha256:a0d8fc4834469c95f492d9af0e5de1c7077cae72ac65d1ff17bd2cfbf49cba82"],"state_sha256":"e3bf65134c8ad7bb7e923d292ca9e0be0bf6fc240bd84854c294e6b3926be7dc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CcGmHbcMXwu01j0McJTD4StguS2SAjtykUmwlnHqCQLDMiHO5zBSp8CJQMPjszoYzFU50FW1hNvWKFwq20pwCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T07:14:52.064799Z","bundle_sha256":"33e218c75d41dafde744c1f02b92f3d1ec85c2065be270d5dc2e6e128b1e290b"}}