{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:6GQQ7HHIYGDXY2XZTM62FGHAGB","short_pith_number":"pith:6GQQ7HHI","canonical_record":{"source":{"id":"2502.08178","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-12T07:32:48Z","cross_cats_sorted":[],"title_canon_sha256":"caafe71dd85f477f1026627466d088ff59f06caaf8e80d72625fcb4143936d84","abstract_canon_sha256":"65b4d67a3b9309db88452d0a2c9aa7888e16a8653891f1832c1eb2231d2a365a"},"schema_version":"1.0"},"canonical_sha256":"f1a10f9ce8c1877c6af99b3da298e030523776388144b7bbc8a737c111fc29d2","source":{"kind":"arxiv","id":"2502.08178","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.08178","created_at":"2026-07-05T10:13:18Z"},{"alias_kind":"arxiv_version","alias_value":"2502.08178v1","created_at":"2026-07-05T10:13:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.08178","created_at":"2026-07-05T10:13:18Z"},{"alias_kind":"pith_short_12","alias_value":"6GQQ7HHIYGDX","created_at":"2026-07-05T10:13:18Z"},{"alias_kind":"pith_short_16","alias_value":"6GQQ7HHIYGDXY2XZ","created_at":"2026-07-05T10:13:18Z"},{"alias_kind":"pith_short_8","alias_value":"6GQQ7HHI","created_at":"2026-07-05T10:13:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:6GQQ7HHIYGDXY2XZTM62FGHAGB","target":"record","payload":{"canonical_record":{"source":{"id":"2502.08178","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-12T07:32:48Z","cross_cats_sorted":[],"title_canon_sha256":"caafe71dd85f477f1026627466d088ff59f06caaf8e80d72625fcb4143936d84","abstract_canon_sha256":"65b4d67a3b9309db88452d0a2c9aa7888e16a8653891f1832c1eb2231d2a365a"},"schema_version":"1.0"},"canonical_sha256":"f1a10f9ce8c1877c6af99b3da298e030523776388144b7bbc8a737c111fc29d2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:13:18.222542Z","signature_b64":"XwWZ+/LrzM0h0w06yJUZct4HWStGRyFYxWcwaVN83FiMqW49ZDRq5GlrqMXZ7mFRQq1/NkSFqpOAiYgM3nX4Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1a10f9ce8c1877c6af99b3da298e030523776388144b7bbc8a737c111fc29d2","last_reissued_at":"2026-07-05T10:13:18.222032Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:13:18.222032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.08178","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":"2026-07-05T10:13:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h1GCq9ewb+8NRV2AD2N+ZE7GJkqreBcUAonDVIr0rVP7kYgavHzm/mesPEGoIBQ4RkrxtUwW+YgAtLrZAPMdDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T08:22:26.454091Z"},"content_sha256":"8e9fce9816f264a7db06acf7e3f7a992e0e7dd63d831556c146040958c94cd5b","schema_version":"1.0","event_id":"sha256:8e9fce9816f264a7db06acf7e3f7a992e0e7dd63d831556c146040958c94cd5b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:6GQQ7HHIYGDXY2XZTM62FGHAGB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chenyang Tu, Neng Gao, Ruobing Yao, Shuang Song, Yifei Zhang, Yuhua Liu","submitted_at":"2025-02-12T07:32:48Z","abstract_excerpt":"While Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by incorporating external knowledge, they still face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information. We present ParetoRAG, an unsupervised framework that optimizes RAG systems through sentence-level refinement guided by the Pareto principle. By decomposing paragraphs into sentences and dynamically re-weighting core content while preserving contextual coherence, ParetoRAG achieves dual improvements in both retrieval precision and generation qual"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.08178","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/2502.08178/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":"2026-07-05T10:13:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rCXcbH0t7HAw5DsMGgzx8RYDI2uhCJwZ7sTykkE23hm9m7DDr1sal4xGoD49HFPa2RxUxcm9F4I86DZyAJbgDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T08:22:26.454608Z"},"content_sha256":"6966a4d169ebc61873e86d4d8a7a9a1329d1683a14717b082f22b5f7ad56ac89","schema_version":"1.0","event_id":"sha256:6966a4d169ebc61873e86d4d8a7a9a1329d1683a14717b082f22b5f7ad56ac89"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6GQQ7HHIYGDXY2XZTM62FGHAGB/bundle.json","state_url":"https://pith.science/pith/6GQQ7HHIYGDXY2XZTM62FGHAGB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6GQQ7HHIYGDXY2XZTM62FGHAGB/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":"2026-08-09T08:22:26Z","links":{"resolver":"https://pith.science/pith/6GQQ7HHIYGDXY2XZTM62FGHAGB","bundle":"https://pith.science/pith/6GQQ7HHIYGDXY2XZTM62FGHAGB/bundle.json","state":"https://pith.science/pith/6GQQ7HHIYGDXY2XZTM62FGHAGB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6GQQ7HHIYGDXY2XZTM62FGHAGB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6GQQ7HHIYGDXY2XZTM62FGHAGB","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":"65b4d67a3b9309db88452d0a2c9aa7888e16a8653891f1832c1eb2231d2a365a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-12T07:32:48Z","title_canon_sha256":"caafe71dd85f477f1026627466d088ff59f06caaf8e80d72625fcb4143936d84"},"schema_version":"1.0","source":{"id":"2502.08178","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.08178","created_at":"2026-07-05T10:13:18Z"},{"alias_kind":"arxiv_version","alias_value":"2502.08178v1","created_at":"2026-07-05T10:13:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.08178","created_at":"2026-07-05T10:13:18Z"},{"alias_kind":"pith_short_12","alias_value":"6GQQ7HHIYGDX","created_at":"2026-07-05T10:13:18Z"},{"alias_kind":"pith_short_16","alias_value":"6GQQ7HHIYGDXY2XZ","created_at":"2026-07-05T10:13:18Z"},{"alias_kind":"pith_short_8","alias_value":"6GQQ7HHI","created_at":"2026-07-05T10:13:18Z"}],"graph_snapshots":[{"event_id":"sha256:6966a4d169ebc61873e86d4d8a7a9a1329d1683a14717b082f22b5f7ad56ac89","target":"graph","created_at":"2026-07-05T10:13:18Z","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/2502.08178/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by incorporating external knowledge, they still face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information. We present ParetoRAG, an unsupervised framework that optimizes RAG systems through sentence-level refinement guided by the Pareto principle. By decomposing paragraphs into sentences and dynamically re-weighting core content while preserving contextual coherence, ParetoRAG achieves dual improvements in both retrieval precision and generation qual","authors_text":"Chenyang Tu, Neng Gao, Ruobing Yao, Shuang Song, Yifei Zhang, Yuhua Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-12T07:32:48Z","title":"ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.08178","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":"sha256:8e9fce9816f264a7db06acf7e3f7a992e0e7dd63d831556c146040958c94cd5b","target":"record","created_at":"2026-07-05T10:13:18Z","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":"65b4d67a3b9309db88452d0a2c9aa7888e16a8653891f1832c1eb2231d2a365a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-12T07:32:48Z","title_canon_sha256":"caafe71dd85f477f1026627466d088ff59f06caaf8e80d72625fcb4143936d84"},"schema_version":"1.0","source":{"id":"2502.08178","kind":"arxiv","version":1}},"canonical_sha256":"f1a10f9ce8c1877c6af99b3da298e030523776388144b7bbc8a737c111fc29d2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f1a10f9ce8c1877c6af99b3da298e030523776388144b7bbc8a737c111fc29d2","first_computed_at":"2026-07-05T10:13:18.222032Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:13:18.222032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XwWZ+/LrzM0h0w06yJUZct4HWStGRyFYxWcwaVN83FiMqW49ZDRq5GlrqMXZ7mFRQq1/NkSFqpOAiYgM3nX4Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:13:18.222542Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.08178","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8e9fce9816f264a7db06acf7e3f7a992e0e7dd63d831556c146040958c94cd5b","sha256:6966a4d169ebc61873e86d4d8a7a9a1329d1683a14717b082f22b5f7ad56ac89"],"state_sha256":"0b1a4e4cf31e731160b9eace6f737faeeddfd381a2a960eb79182522d596edf9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5iKvKcT5gMLasNAwjzL0JxjxTVXOFtNDbItNgr2Y/ZMOdMszISPw609oO3NRXbZR1BqBCziN3j5AgdhPCA88Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T08:22:26.461014Z","bundle_sha256":"c5275b66a0b2f168708e4af99b98c470bd40a4121532d41af5ccd622fca1de76"}}