{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:22M7G2M77TJYZ54GNA7LAS4IVM","short_pith_number":"pith:22M7G2M7","schema_version":"1.0","canonical_sha256":"d699f3699ffcd38cf786683eb04b88ab1bc1f0887ada02c9ea72b2829035f85e","source":{"kind":"arxiv","id":"2606.06025","version":1},"attestation_state":"computed","paper":{"title":"EGTR-Review: Efficient Evidence-Grounded Scientific Peer Review Generation via Multi-Agent Teacher Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jimin Wang, Wang Yihu, Xiaochen Wang, Xinpeng Qiu, Zhifeng Liu","submitted_at":"2026-06-04T11:17:40Z","abstract_excerpt":"Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback. However, existing Large Language Model (LLM)-based methods often produce generic comments with insufficient evidence support and weak source traceability, while complex multi-agent systems incur high inference costs. To address these challenges, we propose EGTR-Review, an Evidence-Grounded and Traceable Review Generation framework via Multi-Agent Teacher Distillation. EGTR-Review first constructs a multi-agent teacher that performs structure-aware paper decompositi"},"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":"2606.06025","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-06-04T11:17:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"841a556d00bca58e80ff6003b2ac99b7a0f3c7de0f850a475fe958987d2335c8","abstract_canon_sha256":"ddd12f7a2fc07c2437d0b7823f26cb1f90f0a7b454e8178cb6ce578d06dd00f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:15:30.720306Z","signature_b64":"xkol4MTcihU+HTlkrtcmcNqDcRGEJ/z7IzjU33vHeY/eTUiEMYOvEGub9lSeYCZYuEnt32FNVGI+edHly095Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d699f3699ffcd38cf786683eb04b88ab1bc1f0887ada02c9ea72b2829035f85e","last_reissued_at":"2026-06-05T01:15:30.719863Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:15:30.719863Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EGTR-Review: Efficient Evidence-Grounded Scientific Peer Review Generation via Multi-Agent Teacher Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jimin Wang, Wang Yihu, Xiaochen Wang, Xinpeng Qiu, Zhifeng Liu","submitted_at":"2026-06-04T11:17:40Z","abstract_excerpt":"Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback. However, existing Large Language Model (LLM)-based methods often produce generic comments with insufficient evidence support and weak source traceability, while complex multi-agent systems incur high inference costs. To address these challenges, we propose EGTR-Review, an Evidence-Grounded and Traceable Review Generation framework via Multi-Agent Teacher Distillation. EGTR-Review first constructs a multi-agent teacher that performs structure-aware paper decompositi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06025","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/2606.06025/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":"2606.06025","created_at":"2026-06-05T01:15:30.719921+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.06025v1","created_at":"2026-06-05T01:15:30.719921+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06025","created_at":"2026-06-05T01:15:30.719921+00:00"},{"alias_kind":"pith_short_12","alias_value":"22M7G2M77TJY","created_at":"2026-06-05T01:15:30.719921+00:00"},{"alias_kind":"pith_short_16","alias_value":"22M7G2M77TJYZ54G","created_at":"2026-06-05T01:15:30.719921+00:00"},{"alias_kind":"pith_short_8","alias_value":"22M7G2M7","created_at":"2026-06-05T01:15:30.719921+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/22M7G2M77TJYZ54GNA7LAS4IVM","json":"https://pith.science/pith/22M7G2M77TJYZ54GNA7LAS4IVM.json","graph_json":"https://pith.science/api/pith-number/22M7G2M77TJYZ54GNA7LAS4IVM/graph.json","events_json":"https://pith.science/api/pith-number/22M7G2M77TJYZ54GNA7LAS4IVM/events.json","paper":"https://pith.science/paper/22M7G2M7"},"agent_actions":{"view_html":"https://pith.science/pith/22M7G2M77TJYZ54GNA7LAS4IVM","download_json":"https://pith.science/pith/22M7G2M77TJYZ54GNA7LAS4IVM.json","view_paper":"https://pith.science/paper/22M7G2M7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.06025&json=true","fetch_graph":"https://pith.science/api/pith-number/22M7G2M77TJYZ54GNA7LAS4IVM/graph.json","fetch_events":"https://pith.science/api/pith-number/22M7G2M77TJYZ54GNA7LAS4IVM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/22M7G2M77TJYZ54GNA7LAS4IVM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/22M7G2M77TJYZ54GNA7LAS4IVM/action/storage_attestation","attest_author":"https://pith.science/pith/22M7G2M77TJYZ54GNA7LAS4IVM/action/author_attestation","sign_citation":"https://pith.science/pith/22M7G2M77TJYZ54GNA7LAS4IVM/action/citation_signature","submit_replication":"https://pith.science/pith/22M7G2M77TJYZ54GNA7LAS4IVM/action/replication_record"}},"created_at":"2026-06-05T01:15:30.719921+00:00","updated_at":"2026-06-05T01:15:30.719921+00:00"}