{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:HFWUR5HSJNIURM76DDBXMBMOSJ","short_pith_number":"pith:HFWUR5HS","schema_version":"1.0","canonical_sha256":"396d48f4f24b5148b3fe18c376058e926a48ea9d392f6dd247c40e562dc66a18","source":{"kind":"arxiv","id":"2606.24834","version":1},"attestation_state":"computed","paper":{"title":"Accuracy and Satisfaction in Multi-Turn LLM Dialogues for NFR Assessment","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ali Pourghasemi Fatideh, Collin McMillan, Maria Dhakal, Sepideh Ghanavati, Wilder Baldwin","submitted_at":"2026-06-23T17:15:40Z","abstract_excerpt":"LLM-based dialogue assistants have become mainstream tools for software developers, yet current evaluation benchmarks focus exclusively on functional correctness. This leaves a critical gap in assessing the quality and accuracy of these conversations when handling Non-Functional Requirements (NFRs), which are inherently vague, context-dependent, and involve many parts of a program. Evaluating how well these systems support collaborative reasoning about NFRs requires methods that go beyond single-turn accuracy to capture both the correctness of the system's outputs and the quality of the multi-"},"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":"2606.24834","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-23T17:15:40Z","cross_cats_sorted":[],"title_canon_sha256":"8cffdc4f15d7e3acb13d0382d995e6c3d858c177884b7e1983deb92493802a25","abstract_canon_sha256":"e0f9734883bae8684c0b21e18d77b0fd6bdfac74494d66a344233a795e71c3ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:15:43.541177Z","signature_b64":"72JIQQg2U4IBGG0UJh5L5am10xRQ1pB628U+v4mDDQNoxJrdZyunDJc6Cb/FvF1t/yx9Z9CKyukesXI9rRONDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"396d48f4f24b5148b3fe18c376058e926a48ea9d392f6dd247c40e562dc66a18","last_reissued_at":"2026-06-24T01:15:43.540820Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:15:43.540820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accuracy and Satisfaction in Multi-Turn LLM Dialogues for NFR Assessment","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ali Pourghasemi Fatideh, Collin McMillan, Maria Dhakal, Sepideh Ghanavati, Wilder Baldwin","submitted_at":"2026-06-23T17:15:40Z","abstract_excerpt":"LLM-based dialogue assistants have become mainstream tools for software developers, yet current evaluation benchmarks focus exclusively on functional correctness. This leaves a critical gap in assessing the quality and accuracy of these conversations when handling Non-Functional Requirements (NFRs), which are inherently vague, context-dependent, and involve many parts of a program. Evaluating how well these systems support collaborative reasoning about NFRs requires methods that go beyond single-turn accuracy to capture both the correctness of the system's outputs and the quality of the multi-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24834","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/2606.24834/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":"2606.24834","created_at":"2026-06-24T01:15:43.540880+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.24834v1","created_at":"2026-06-24T01:15:43.540880+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24834","created_at":"2026-06-24T01:15:43.540880+00:00"},{"alias_kind":"pith_short_12","alias_value":"HFWUR5HSJNIU","created_at":"2026-06-24T01:15:43.540880+00:00"},{"alias_kind":"pith_short_16","alias_value":"HFWUR5HSJNIURM76","created_at":"2026-06-24T01:15:43.540880+00:00"},{"alias_kind":"pith_short_8","alias_value":"HFWUR5HS","created_at":"2026-06-24T01:15:43.540880+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HFWUR5HSJNIURM76DDBXMBMOSJ","json":"https://pith.science/pith/HFWUR5HSJNIURM76DDBXMBMOSJ.json","graph_json":"https://pith.science/api/pith-number/HFWUR5HSJNIURM76DDBXMBMOSJ/graph.json","events_json":"https://pith.science/api/pith-number/HFWUR5HSJNIURM76DDBXMBMOSJ/events.json","paper":"https://pith.science/paper/HFWUR5HS"},"agent_actions":{"view_html":"https://pith.science/pith/HFWUR5HSJNIURM76DDBXMBMOSJ","download_json":"https://pith.science/pith/HFWUR5HSJNIURM76DDBXMBMOSJ.json","view_paper":"https://pith.science/paper/HFWUR5HS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.24834&json=true","fetch_graph":"https://pith.science/api/pith-number/HFWUR5HSJNIURM76DDBXMBMOSJ/graph.json","fetch_events":"https://pith.science/api/pith-number/HFWUR5HSJNIURM76DDBXMBMOSJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HFWUR5HSJNIURM76DDBXMBMOSJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HFWUR5HSJNIURM76DDBXMBMOSJ/action/storage_attestation","attest_author":"https://pith.science/pith/HFWUR5HSJNIURM76DDBXMBMOSJ/action/author_attestation","sign_citation":"https://pith.science/pith/HFWUR5HSJNIURM76DDBXMBMOSJ/action/citation_signature","submit_replication":"https://pith.science/pith/HFWUR5HSJNIURM76DDBXMBMOSJ/action/replication_record"}},"created_at":"2026-06-24T01:15:43.540880+00:00","updated_at":"2026-06-24T01:15:43.540880+00:00"}