{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:NYDZ7KQQFLS3DBWZFED3ST63O7","short_pith_number":"pith:NYDZ7KQQ","canonical_record":{"source":{"id":"2506.10408","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-12T07:01:56Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"b7657a3dbbab46c2917fdfc6ecc61dab0e7ca9a3a83c3ff6f9443b04d88e259e","abstract_canon_sha256":"9785ec28b3b48e42b8ea49d5c842251c2a9c3cae2a749233682c8ad28b302564"},"schema_version":"1.0"},"canonical_sha256":"6e079faa102ae5b186d92907b94fdb77e3ca7e54b808f5f5c0a612c9fafae1df","source":{"kind":"arxiv","id":"2506.10408","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.10408","created_at":"2026-07-05T11:20:22Z"},{"alias_kind":"arxiv_version","alias_value":"2506.10408v1","created_at":"2026-07-05T11:20:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.10408","created_at":"2026-07-05T11:20:22Z"},{"alias_kind":"pith_short_12","alias_value":"NYDZ7KQQFLS3","created_at":"2026-07-05T11:20:22Z"},{"alias_kind":"pith_short_16","alias_value":"NYDZ7KQQFLS3DBWZ","created_at":"2026-07-05T11:20:22Z"},{"alias_kind":"pith_short_8","alias_value":"NYDZ7KQQ","created_at":"2026-07-05T11:20:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:NYDZ7KQQFLS3DBWZFED3ST63O7","target":"record","payload":{"canonical_record":{"source":{"id":"2506.10408","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-12T07:01:56Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"b7657a3dbbab46c2917fdfc6ecc61dab0e7ca9a3a83c3ff6f9443b04d88e259e","abstract_canon_sha256":"9785ec28b3b48e42b8ea49d5c842251c2a9c3cae2a749233682c8ad28b302564"},"schema_version":"1.0"},"canonical_sha256":"6e079faa102ae5b186d92907b94fdb77e3ca7e54b808f5f5c0a612c9fafae1df","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:20:22.943847Z","signature_b64":"Uz8eafPZtLrfjrA7HdtyeXBuK63i0SIcom9uzr+WnDJ+legbfhQh405PbGSqS85hdJFzVjhC1atbQMBK1kBqBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e079faa102ae5b186d92907b94fdb77e3ca7e54b808f5f5c0a612c9fafae1df","last_reissued_at":"2026-07-05T11:20:22.943302Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:20:22.943302Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.10408","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":"2026-07-05T11:20:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"had7FmjM2x0A/MJJag/PW2SO5854R40khfgzdF5jgaR976D/x+OSdeJFiBHd5udbR4saMRODWJFnM6T/EtNXDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:29:04.883701Z"},"content_sha256":"441f963451b85f83717b7c1d406eab36446004b0e96c53fcb51f5724c7894c56","schema_version":"1.0","event_id":"sha256:441f963451b85f83717b7c1d406eab36446004b0e96c53fcb51f5724c7894c56"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:NYDZ7KQQFLS3DBWZFED3ST63O7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.AI","authors_text":"Gang Su, Huifeng Lin, Jintao Liang, Rui Zhao, You Wu, Ziyue Li","submitted_at":"2025-06-12T07:01:56Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to overcome the knowledge limitations of Large Language Models (LLMs) by integrating external retrieval with language generation. While early RAG systems based on static pipelines have shown effectiveness in well-structured tasks, they struggle in real-world scenarios requiring complex reasoning, dynamic retrieval, and multi-modal integration. To address these challenges, the field has shifted toward Reasoning Agentic RAG, a paradigm that embeds decision-making and adaptive tool use directly into the retrieval process. In"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.10408","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/2506.10408/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":"2026-07-05T11:20:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kBUXAXpYZ0l8uW05VDvlxxkIvmNB87+ltOMtdTQN1m3RzOp0RfNZPKeBtZKEhSOMy9JbqKhF/ugO6oYboaWWDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:29:04.884192Z"},"content_sha256":"caa80fdf14ee3756345e7db1fa84ccaf3fb4473765e9d1f36722d801835453f5","schema_version":"1.0","event_id":"sha256:caa80fdf14ee3756345e7db1fa84ccaf3fb4473765e9d1f36722d801835453f5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NYDZ7KQQFLS3DBWZFED3ST63O7/bundle.json","state_url":"https://pith.science/pith/NYDZ7KQQFLS3DBWZFED3ST63O7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NYDZ7KQQFLS3DBWZFED3ST63O7/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":"2026-08-09T05:29:04Z","links":{"resolver":"https://pith.science/pith/NYDZ7KQQFLS3DBWZFED3ST63O7","bundle":"https://pith.science/pith/NYDZ7KQQFLS3DBWZFED3ST63O7/bundle.json","state":"https://pith.science/pith/NYDZ7KQQFLS3DBWZFED3ST63O7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NYDZ7KQQFLS3DBWZFED3ST63O7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NYDZ7KQQFLS3DBWZFED3ST63O7","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":"9785ec28b3b48e42b8ea49d5c842251c2a9c3cae2a749233682c8ad28b302564","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-12T07:01:56Z","title_canon_sha256":"b7657a3dbbab46c2917fdfc6ecc61dab0e7ca9a3a83c3ff6f9443b04d88e259e"},"schema_version":"1.0","source":{"id":"2506.10408","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.10408","created_at":"2026-07-05T11:20:22Z"},{"alias_kind":"arxiv_version","alias_value":"2506.10408v1","created_at":"2026-07-05T11:20:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.10408","created_at":"2026-07-05T11:20:22Z"},{"alias_kind":"pith_short_12","alias_value":"NYDZ7KQQFLS3","created_at":"2026-07-05T11:20:22Z"},{"alias_kind":"pith_short_16","alias_value":"NYDZ7KQQFLS3DBWZ","created_at":"2026-07-05T11:20:22Z"},{"alias_kind":"pith_short_8","alias_value":"NYDZ7KQQ","created_at":"2026-07-05T11:20:22Z"}],"graph_snapshots":[{"event_id":"sha256:caa80fdf14ee3756345e7db1fa84ccaf3fb4473765e9d1f36722d801835453f5","target":"graph","created_at":"2026-07-05T11:20:22Z","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/2506.10408/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to overcome the knowledge limitations of Large Language Models (LLMs) by integrating external retrieval with language generation. While early RAG systems based on static pipelines have shown effectiveness in well-structured tasks, they struggle in real-world scenarios requiring complex reasoning, dynamic retrieval, and multi-modal integration. To address these challenges, the field has shifted toward Reasoning Agentic RAG, a paradigm that embeds decision-making and adaptive tool use directly into the retrieval process. In","authors_text":"Gang Su, Huifeng Lin, Jintao Liang, Rui Zhao, You Wu, Ziyue Li","cross_cats":["cs.IR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-12T07:01:56Z","title":"Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.10408","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":"sha256:441f963451b85f83717b7c1d406eab36446004b0e96c53fcb51f5724c7894c56","target":"record","created_at":"2026-07-05T11:20:22Z","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":"9785ec28b3b48e42b8ea49d5c842251c2a9c3cae2a749233682c8ad28b302564","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-12T07:01:56Z","title_canon_sha256":"b7657a3dbbab46c2917fdfc6ecc61dab0e7ca9a3a83c3ff6f9443b04d88e259e"},"schema_version":"1.0","source":{"id":"2506.10408","kind":"arxiv","version":1}},"canonical_sha256":"6e079faa102ae5b186d92907b94fdb77e3ca7e54b808f5f5c0a612c9fafae1df","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6e079faa102ae5b186d92907b94fdb77e3ca7e54b808f5f5c0a612c9fafae1df","first_computed_at":"2026-07-05T11:20:22.943302Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:20:22.943302Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Uz8eafPZtLrfjrA7HdtyeXBuK63i0SIcom9uzr+WnDJ+legbfhQh405PbGSqS85hdJFzVjhC1atbQMBK1kBqBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:20:22.943847Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.10408","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:441f963451b85f83717b7c1d406eab36446004b0e96c53fcb51f5724c7894c56","sha256:caa80fdf14ee3756345e7db1fa84ccaf3fb4473765e9d1f36722d801835453f5"],"state_sha256":"56c59e0d63bbd5ea658dffcf0ae37d4d578fb8f84be26807e470557a8b6db6fd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YeB14iUj61LNzHIIb++RMNqJOuKk+mm1P7dV6tjsNK7yqnimWu/uLT/fxGLeWylCAnMS9p1LTjmaJwmwUnBHCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T05:29:04.888524Z","bundle_sha256":"b0a9e45fa20b6a038ab6526fea2a76e587818d507ed8dbb677acba4b57955cda"}}