{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:27MMMTCTLFVJSJV553B4LHOUL5","short_pith_number":"pith:27MMMTCT","schema_version":"1.0","canonical_sha256":"d7d8c64c53596a9926bdeec3c59dd45f5e83f327f3d9b3583229ea126d70d117","source":{"kind":"arxiv","id":"2506.08134","version":4},"attestation_state":"computed","paper":{"title":"Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.AI","authors_text":"Jing Yang, Markus Wulfmeier, Mihaela van der Schaar, Qiyao Wei, Samuel Holt","submitted_at":"2025-06-09T18:37:14Z","abstract_excerpt":"Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale. Exponential growth in manuscript submissions to premier ML venues such as NeurIPS, ICML, and ICLR is outpacing the finite capacity of qualified reviewers, leading to concerns about review quality, consistency, and reviewer fatigue. This position paper argues that AI-assisted peer review must become an urgent research and infrastructure priority. We advocate for a comprehensive AI-augmented ecosystem, leveraging Large Language Models (LLMs) not as replacements for human judgment, but a"},"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":"2506.08134","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-09T18:37:14Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"32e37a15d41bbc25f83e73707c7027c70b12c7ca0eaa763ce8420643d53d12a8","abstract_canon_sha256":"6a02061fde13a4224d3aafd6b47077a3ec0d8025dc8a3a02263cd858959bf66b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:08:28.129200Z","signature_b64":"TRVEgD+IuxdNJs1kR2B0fahXn7wPnmjlkwKWhXPVB/gRXWrRXqcB2wZW1oInv0Yki/K64LY4oI6nCsXek+55DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7d8c64c53596a9926bdeec3c59dd45f5e83f327f3d9b3583229ea126d70d117","last_reissued_at":"2026-06-10T01:08:28.128138Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:08:28.128138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.AI","authors_text":"Jing Yang, Markus Wulfmeier, Mihaela van der Schaar, Qiyao Wei, Samuel Holt","submitted_at":"2025-06-09T18:37:14Z","abstract_excerpt":"Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale. Exponential growth in manuscript submissions to premier ML venues such as NeurIPS, ICML, and ICLR is outpacing the finite capacity of qualified reviewers, leading to concerns about review quality, consistency, and reviewer fatigue. This position paper argues that AI-assisted peer review must become an urgent research and infrastructure priority. We advocate for a comprehensive AI-augmented ecosystem, leveraging Large Language Models (LLMs) not as replacements for human judgment, but a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.08134","kind":"arxiv","version":4},"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/2506.08134/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":"2506.08134","created_at":"2026-06-10T01:08:28.128296+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.08134v4","created_at":"2026-06-10T01:08:28.128296+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.08134","created_at":"2026-06-10T01:08:28.128296+00:00"},{"alias_kind":"pith_short_12","alias_value":"27MMMTCTLFVJ","created_at":"2026-06-10T01:08:28.128296+00:00"},{"alias_kind":"pith_short_16","alias_value":"27MMMTCTLFVJSJV5","created_at":"2026-06-10T01:08:28.128296+00:00"},{"alias_kind":"pith_short_8","alias_value":"27MMMTCT","created_at":"2026-06-10T01:08:28.128296+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2605.02651","citing_title":"ARA: Agentic Reproducibility Assessment For Scalable Support Of Scientific Peer-Review","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10425","citing_title":"Toward an Engineering of Science: Rebalancing Generation and Verification in the Age of AI","ref_index":40,"is_internal_anchor":true},{"citing_arxiv_id":"2605.02651","citing_title":"ARA: Agentic Reproducibility Assessment For Scalable Support Of Scientific Peer-Review","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/27MMMTCTLFVJSJV553B4LHOUL5","json":"https://pith.science/pith/27MMMTCTLFVJSJV553B4LHOUL5.json","graph_json":"https://pith.science/api/pith-number/27MMMTCTLFVJSJV553B4LHOUL5/graph.json","events_json":"https://pith.science/api/pith-number/27MMMTCTLFVJSJV553B4LHOUL5/events.json","paper":"https://pith.science/paper/27MMMTCT"},"agent_actions":{"view_html":"https://pith.science/pith/27MMMTCTLFVJSJV553B4LHOUL5","download_json":"https://pith.science/pith/27MMMTCTLFVJSJV553B4LHOUL5.json","view_paper":"https://pith.science/paper/27MMMTCT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.08134&json=true","fetch_graph":"https://pith.science/api/pith-number/27MMMTCTLFVJSJV553B4LHOUL5/graph.json","fetch_events":"https://pith.science/api/pith-number/27MMMTCTLFVJSJV553B4LHOUL5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/27MMMTCTLFVJSJV553B4LHOUL5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/27MMMTCTLFVJSJV553B4LHOUL5/action/storage_attestation","attest_author":"https://pith.science/pith/27MMMTCTLFVJSJV553B4LHOUL5/action/author_attestation","sign_citation":"https://pith.science/pith/27MMMTCTLFVJSJV553B4LHOUL5/action/citation_signature","submit_replication":"https://pith.science/pith/27MMMTCTLFVJSJV553B4LHOUL5/action/replication_record"}},"created_at":"2026-06-10T01:08:28.128296+00:00","updated_at":"2026-06-10T01:08:28.128296+00:00"}