{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JMAD3JCC5EKXAOLBP2JOOBBBW2","short_pith_number":"pith:JMAD3JCC","schema_version":"1.0","canonical_sha256":"4b003da442e9157039617e92e70421b689d22542fc3b31217946aea82fb64e79","source":{"kind":"arxiv","id":"2410.10136","version":1},"attestation_state":"computed","paper":{"title":"Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Cosimo Spera, Garima Agrawal, Sashank Gummuluri","submitted_at":"2024-10-14T04:06:22Z","abstract_excerpt":"In customer contact centers, human agents often struggle with long average handling times (AHT) due to the need to manually interpret queries and retrieve relevant knowledge base (KB) articles. While retrieval augmented generation (RAG) systems using large language models (LLMs) have been widely adopted in industry to assist with such tasks, RAG faces challenges in real-time conversations, such as inaccurate query formulation and redundant retrieval of frequently asked questions (FAQs). To address these limitations, we propose a decision support system that can look beyond RAG by first identif"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.10136","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-14T04:06:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"91d4094567a84168513e913514cc4621662a0d013502d0d10c3a6abc454f9d93","abstract_canon_sha256":"7160e7e817523cdce8d1c5cd460d35f39ec9309633a55e0b7e383da3faeb4e70"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:11.757630Z","signature_b64":"iMi2v1ayY4lYWtowpDvIYpNK1+7PVKKRy/Yfai4D3Lw8oKLexzzZGk77HfoULR0z5kwycx8AoTuhXvW9edHbBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b003da442e9157039617e92e70421b689d22542fc3b31217946aea82fb64e79","last_reissued_at":"2026-07-05T09:20:11.757247Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:11.757247Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Cosimo Spera, Garima Agrawal, Sashank Gummuluri","submitted_at":"2024-10-14T04:06:22Z","abstract_excerpt":"In customer contact centers, human agents often struggle with long average handling times (AHT) due to the need to manually interpret queries and retrieve relevant knowledge base (KB) articles. While retrieval augmented generation (RAG) systems using large language models (LLMs) have been widely adopted in industry to assist with such tasks, RAG faces challenges in real-time conversations, such as inaccurate query formulation and redundant retrieval of frequently asked questions (FAQs). To address these limitations, we propose a decision support system that can look beyond RAG by first identif"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.10136","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/2410.10136/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.10136","created_at":"2026-07-05T09:20:11.757303+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.10136v1","created_at":"2026-07-05T09:20:11.757303+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.10136","created_at":"2026-07-05T09:20:11.757303+00:00"},{"alias_kind":"pith_short_12","alias_value":"JMAD3JCC5EKX","created_at":"2026-07-05T09:20:11.757303+00:00"},{"alias_kind":"pith_short_16","alias_value":"JMAD3JCC5EKXAOLB","created_at":"2026-07-05T09:20:11.757303+00:00"},{"alias_kind":"pith_short_8","alias_value":"JMAD3JCC","created_at":"2026-07-05T09:20:11.757303+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.15104","citing_title":"From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents","ref_index":286,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JMAD3JCC5EKXAOLBP2JOOBBBW2","json":"https://pith.science/pith/JMAD3JCC5EKXAOLBP2JOOBBBW2.json","graph_json":"https://pith.science/api/pith-number/JMAD3JCC5EKXAOLBP2JOOBBBW2/graph.json","events_json":"https://pith.science/api/pith-number/JMAD3JCC5EKXAOLBP2JOOBBBW2/events.json","paper":"https://pith.science/paper/JMAD3JCC"},"agent_actions":{"view_html":"https://pith.science/pith/JMAD3JCC5EKXAOLBP2JOOBBBW2","download_json":"https://pith.science/pith/JMAD3JCC5EKXAOLBP2JOOBBBW2.json","view_paper":"https://pith.science/paper/JMAD3JCC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.10136&json=true","fetch_graph":"https://pith.science/api/pith-number/JMAD3JCC5EKXAOLBP2JOOBBBW2/graph.json","fetch_events":"https://pith.science/api/pith-number/JMAD3JCC5EKXAOLBP2JOOBBBW2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JMAD3JCC5EKXAOLBP2JOOBBBW2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JMAD3JCC5EKXAOLBP2JOOBBBW2/action/storage_attestation","attest_author":"https://pith.science/pith/JMAD3JCC5EKXAOLBP2JOOBBBW2/action/author_attestation","sign_citation":"https://pith.science/pith/JMAD3JCC5EKXAOLBP2JOOBBBW2/action/citation_signature","submit_replication":"https://pith.science/pith/JMAD3JCC5EKXAOLBP2JOOBBBW2/action/replication_record"}},"created_at":"2026-07-05T09:20:11.757303+00:00","updated_at":"2026-07-05T09:20:11.757303+00:00"}