{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:7V6BX3MHUQQB7Q2ZP2BV7UHCSO","short_pith_number":"pith:7V6BX3MH","schema_version":"1.0","canonical_sha256":"fd7c1bed87a4201fc3597e835fd0e293beb5b6cdd892ce7a92db17096ed7a058","source":{"kind":"arxiv","id":"2606.08077","version":1},"attestation_state":"computed","paper":{"title":"Support Vector Rubrics: Closing the Gap Between Self-Generated and Human Rubrics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Mengyuan Sun, Shikun Zhang, Wei Ye, Yu Li, Zhuohao Yu","submitted_at":"2026-06-06T09:55:22Z","abstract_excerpt":"Rubric-based evaluation is a promising paradigm for judging large language model (LLM) outputs, yet self-generated rubrics lag human-annotated criteria on hard instances. We argue this discriminative gap reflects an objective mismatch: self-generated rubrics describe good responses, whereas effective criteria must discriminate between close candidates. To close this gap, we introduce SVR (Support Vector Rubrics), a framework that recasts rubric construction as max-margin boundary learning over preference data. SVR mines contrastive features from preference pairs into a rubric bank, learns a pr"},"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":"2606.08077","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-06T09:55:22Z","cross_cats_sorted":[],"title_canon_sha256":"49608853a5c61dd02cc9df6560c89b36adce82103a7ea5dbd887306ae22fa673","abstract_canon_sha256":"04a5cb31dbb6634344a57dfbd8efb27f84b6505159a0778910563f8e05a435cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:25.564016Z","signature_b64":"m9ulv9kSQ7i1BBCOgWkFyP4pmRo+nPYZv0PMqPtiQbf+/uzbB9FTS3MT+kcyg/7MqvYxy6IjEbYOZJH+ToG4AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd7c1bed87a4201fc3597e835fd0e293beb5b6cdd892ce7a92db17096ed7a058","last_reissued_at":"2026-06-09T01:05:25.563577Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:25.563577Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Support Vector Rubrics: Closing the Gap Between Self-Generated and Human Rubrics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Mengyuan Sun, Shikun Zhang, Wei Ye, Yu Li, Zhuohao Yu","submitted_at":"2026-06-06T09:55:22Z","abstract_excerpt":"Rubric-based evaluation is a promising paradigm for judging large language model (LLM) outputs, yet self-generated rubrics lag human-annotated criteria on hard instances. We argue this discriminative gap reflects an objective mismatch: self-generated rubrics describe good responses, whereas effective criteria must discriminate between close candidates. To close this gap, we introduce SVR (Support Vector Rubrics), a framework that recasts rubric construction as max-margin boundary learning over preference data. SVR mines contrastive features from preference pairs into a rubric bank, learns a pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.08077","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/2606.08077/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":"2606.08077","created_at":"2026-06-09T01:05:25.563637+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.08077v1","created_at":"2026-06-09T01:05:25.563637+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.08077","created_at":"2026-06-09T01:05:25.563637+00:00"},{"alias_kind":"pith_short_12","alias_value":"7V6BX3MHUQQB","created_at":"2026-06-09T01:05:25.563637+00:00"},{"alias_kind":"pith_short_16","alias_value":"7V6BX3MHUQQB7Q2Z","created_at":"2026-06-09T01:05:25.563637+00:00"},{"alias_kind":"pith_short_8","alias_value":"7V6BX3MH","created_at":"2026-06-09T01:05:25.563637+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/7V6BX3MHUQQB7Q2ZP2BV7UHCSO","json":"https://pith.science/pith/7V6BX3MHUQQB7Q2ZP2BV7UHCSO.json","graph_json":"https://pith.science/api/pith-number/7V6BX3MHUQQB7Q2ZP2BV7UHCSO/graph.json","events_json":"https://pith.science/api/pith-number/7V6BX3MHUQQB7Q2ZP2BV7UHCSO/events.json","paper":"https://pith.science/paper/7V6BX3MH"},"agent_actions":{"view_html":"https://pith.science/pith/7V6BX3MHUQQB7Q2ZP2BV7UHCSO","download_json":"https://pith.science/pith/7V6BX3MHUQQB7Q2ZP2BV7UHCSO.json","view_paper":"https://pith.science/paper/7V6BX3MH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.08077&json=true","fetch_graph":"https://pith.science/api/pith-number/7V6BX3MHUQQB7Q2ZP2BV7UHCSO/graph.json","fetch_events":"https://pith.science/api/pith-number/7V6BX3MHUQQB7Q2ZP2BV7UHCSO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7V6BX3MHUQQB7Q2ZP2BV7UHCSO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7V6BX3MHUQQB7Q2ZP2BV7UHCSO/action/storage_attestation","attest_author":"https://pith.science/pith/7V6BX3MHUQQB7Q2ZP2BV7UHCSO/action/author_attestation","sign_citation":"https://pith.science/pith/7V6BX3MHUQQB7Q2ZP2BV7UHCSO/action/citation_signature","submit_replication":"https://pith.science/pith/7V6BX3MHUQQB7Q2ZP2BV7UHCSO/action/replication_record"}},"created_at":"2026-06-09T01:05:25.563637+00:00","updated_at":"2026-06-09T01:05:25.563637+00:00"}