{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EYXDT3HHO25CPEUKWCKKKVCM7O","short_pith_number":"pith:EYXDT3HH","schema_version":"1.0","canonical_sha256":"262e39ece776ba27928ab094a5544cfbb2d64af14a30abd5b8c6931fdb5cccd7","source":{"kind":"arxiv","id":"2511.01043","version":2},"attestation_state":"computed","paper":{"title":"DPO-F+: Aligning Code Repair Feedback with Developers' Preferences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Kevin Leach, Yifan Zhang, Yueke Zhang, Yu Huang, Zihan Fang","submitted_at":"2025-11-02T18:39:41Z","abstract_excerpt":"Large Language Models (LLMs) are increasingly used in software engineering tasks, especially code repair. However, developers often struggle to interpret model outputs, limiting effective human--AI teaming, where humans and AI work toward a shared objective. Prior work mainly optimizes generated code, giving less attention to natural-language feedback that supports comprehension and iterative improvement. We present \\textsc{DPO-f+}, a framework that aligns code-repair feedback with the needs of different developer groups, including novices and proficient developers. It (1) defines feedback-ali"},"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":"2511.01043","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-11-02T18:39:41Z","cross_cats_sorted":[],"title_canon_sha256":"5ce05858d937ded3edf12381d3ab934420475faa037ae0ea272a39e8b7e489f0","abstract_canon_sha256":"d1da46c66c2d1a622d36731e4feb9dcc02d16fb1fbf9ef197a854b051a127e7e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-09T00:19:11.606677Z","signature_b64":"L5uvqF+PsaNukdprQkOKe85RGolX9jpATJo5T/fnofefCsW6nDjj7hlKjVEoc0m9pWiIK/R/UZuCBKDzI2YZAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"262e39ece776ba27928ab094a5544cfbb2d64af14a30abd5b8c6931fdb5cccd7","last_reissued_at":"2026-07-09T00:19:11.605718Z","signature_status":"signed_v1","first_computed_at":"2026-07-09T00:19:11.605718Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DPO-F+: Aligning Code Repair Feedback with Developers' Preferences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Kevin Leach, Yifan Zhang, Yueke Zhang, Yu Huang, Zihan Fang","submitted_at":"2025-11-02T18:39:41Z","abstract_excerpt":"Large Language Models (LLMs) are increasingly used in software engineering tasks, especially code repair. However, developers often struggle to interpret model outputs, limiting effective human--AI teaming, where humans and AI work toward a shared objective. Prior work mainly optimizes generated code, giving less attention to natural-language feedback that supports comprehension and iterative improvement. We present \\textsc{DPO-f+}, a framework that aligns code-repair feedback with the needs of different developer groups, including novices and proficient developers. It (1) defines feedback-ali"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.01043","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/2511.01043/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":"2511.01043","created_at":"2026-07-09T00:19:11.605853+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.01043v2","created_at":"2026-07-09T00:19:11.605853+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.01043","created_at":"2026-07-09T00:19:11.605853+00:00"},{"alias_kind":"pith_short_12","alias_value":"EYXDT3HHO25C","created_at":"2026-07-09T00:19:11.605853+00:00"},{"alias_kind":"pith_short_16","alias_value":"EYXDT3HHO25CPEUK","created_at":"2026-07-09T00:19:11.605853+00:00"},{"alias_kind":"pith_short_8","alias_value":"EYXDT3HH","created_at":"2026-07-09T00:19:11.605853+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2607.05677","citing_title":"From Conversation to Contribution: Characterizing Coding Agent in Open-Source Software","ref_index":19,"is_internal_anchor":true},{"citing_arxiv_id":"2604.17184","citing_title":"SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair","ref_index":95,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EYXDT3HHO25CPEUKWCKKKVCM7O","json":"https://pith.science/pith/EYXDT3HHO25CPEUKWCKKKVCM7O.json","graph_json":"https://pith.science/api/pith-number/EYXDT3HHO25CPEUKWCKKKVCM7O/graph.json","events_json":"https://pith.science/api/pith-number/EYXDT3HHO25CPEUKWCKKKVCM7O/events.json","paper":"https://pith.science/paper/EYXDT3HH"},"agent_actions":{"view_html":"https://pith.science/pith/EYXDT3HHO25CPEUKWCKKKVCM7O","download_json":"https://pith.science/pith/EYXDT3HHO25CPEUKWCKKKVCM7O.json","view_paper":"https://pith.science/paper/EYXDT3HH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.01043&json=true","fetch_graph":"https://pith.science/api/pith-number/EYXDT3HHO25CPEUKWCKKKVCM7O/graph.json","fetch_events":"https://pith.science/api/pith-number/EYXDT3HHO25CPEUKWCKKKVCM7O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EYXDT3HHO25CPEUKWCKKKVCM7O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EYXDT3HHO25CPEUKWCKKKVCM7O/action/storage_attestation","attest_author":"https://pith.science/pith/EYXDT3HHO25CPEUKWCKKKVCM7O/action/author_attestation","sign_citation":"https://pith.science/pith/EYXDT3HHO25CPEUKWCKKKVCM7O/action/citation_signature","submit_replication":"https://pith.science/pith/EYXDT3HHO25CPEUKWCKKKVCM7O/action/replication_record"}},"created_at":"2026-07-09T00:19:11.605853+00:00","updated_at":"2026-07-09T00:19:11.605853+00:00"}