{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:6VMRX7VII7OKFWMEQZEN23LJAP","short_pith_number":"pith:6VMRX7VI","schema_version":"1.0","canonical_sha256":"f5591bfea847dca2d9848648dd6d6903fe574f504c3be9a9bfc1cba3c4352b0f","source":{"kind":"arxiv","id":"2606.23233","version":1},"attestation_state":"computed","paper":{"title":"Judgment-Grounded Expansion for Peer Review Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Iryna Gurevych, Lizhen Qu, Sheng Lu","submitted_at":"2026-06-22T12:20:11Z","abstract_excerpt":"Automatic review generation is a promising direction for accelerating scientific progress. While most work adopts an end-to-end setup, its fully automated nature makes it less suitable for settings that demand accountability. To better balance automation and accountability, we formalize judgment-grounded expansion, a human-AI collaboration mode where a reviewer provides an evaluative claim and the system expands it into review comment candidate(s). We model it as a structured generate-check-refine process and conduct a user study to collect human-model interaction data. We study two practical "},"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":"2606.23233","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-22T12:20:11Z","cross_cats_sorted":[],"title_canon_sha256":"07d18b0aa6a4d010de3f8d13e0d31606a66d56364f66612281fb27b14fc3b2a6","abstract_canon_sha256":"c7260dec0e4233712b9c3910b5ce63b6784efaa31a308bec99f54e704ca1a5f8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T03:14:14.486942Z","signature_b64":"TDiIuZ248YhmUgxc4JA97eMMcdc0C/7C+pJMHYQotdr6z6piMOjNoiGUgqbDqA63hPXOuwO9TkFIMB2QAIeOBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f5591bfea847dca2d9848648dd6d6903fe574f504c3be9a9bfc1cba3c4352b0f","last_reissued_at":"2026-06-23T03:14:14.486544Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T03:14:14.486544Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Judgment-Grounded Expansion for Peer Review Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Iryna Gurevych, Lizhen Qu, Sheng Lu","submitted_at":"2026-06-22T12:20:11Z","abstract_excerpt":"Automatic review generation is a promising direction for accelerating scientific progress. While most work adopts an end-to-end setup, its fully automated nature makes it less suitable for settings that demand accountability. To better balance automation and accountability, we formalize judgment-grounded expansion, a human-AI collaboration mode where a reviewer provides an evaluative claim and the system expands it into review comment candidate(s). We model it as a structured generate-check-refine process and conduct a user study to collect human-model interaction data. We study two practical "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.23233","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/2606.23233/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":"2606.23233","created_at":"2026-06-23T03:14:14.486602+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.23233v1","created_at":"2026-06-23T03:14:14.486602+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.23233","created_at":"2026-06-23T03:14:14.486602+00:00"},{"alias_kind":"pith_short_12","alias_value":"6VMRX7VII7OK","created_at":"2026-06-23T03:14:14.486602+00:00"},{"alias_kind":"pith_short_16","alias_value":"6VMRX7VII7OKFWME","created_at":"2026-06-23T03:14:14.486602+00:00"},{"alias_kind":"pith_short_8","alias_value":"6VMRX7VI","created_at":"2026-06-23T03:14:14.486602+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/6VMRX7VII7OKFWMEQZEN23LJAP","json":"https://pith.science/pith/6VMRX7VII7OKFWMEQZEN23LJAP.json","graph_json":"https://pith.science/api/pith-number/6VMRX7VII7OKFWMEQZEN23LJAP/graph.json","events_json":"https://pith.science/api/pith-number/6VMRX7VII7OKFWMEQZEN23LJAP/events.json","paper":"https://pith.science/paper/6VMRX7VI"},"agent_actions":{"view_html":"https://pith.science/pith/6VMRX7VII7OKFWMEQZEN23LJAP","download_json":"https://pith.science/pith/6VMRX7VII7OKFWMEQZEN23LJAP.json","view_paper":"https://pith.science/paper/6VMRX7VI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.23233&json=true","fetch_graph":"https://pith.science/api/pith-number/6VMRX7VII7OKFWMEQZEN23LJAP/graph.json","fetch_events":"https://pith.science/api/pith-number/6VMRX7VII7OKFWMEQZEN23LJAP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6VMRX7VII7OKFWMEQZEN23LJAP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6VMRX7VII7OKFWMEQZEN23LJAP/action/storage_attestation","attest_author":"https://pith.science/pith/6VMRX7VII7OKFWMEQZEN23LJAP/action/author_attestation","sign_citation":"https://pith.science/pith/6VMRX7VII7OKFWMEQZEN23LJAP/action/citation_signature","submit_replication":"https://pith.science/pith/6VMRX7VII7OKFWMEQZEN23LJAP/action/replication_record"}},"created_at":"2026-06-23T03:14:14.486602+00:00","updated_at":"2026-06-23T03:14:14.486602+00:00"}