{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KJBXNX3TRNZT3R3HCSX6JHI52X","short_pith_number":"pith:KJBXNX3T","schema_version":"1.0","canonical_sha256":"524376df738b733dc76714afe49d1dd5c8c8aacc97addd586753353ce79a9a46","source":{"kind":"arxiv","id":"2409.13712","version":1},"attestation_state":"computed","paper":{"title":"Good Idea or Not, Representation of LLM Could Tell","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bo Xue, Cheng Deng, Chenghu Zhou, Jiaxin Ding, Luoyi Fu, Shuqian Sheng, Xinbing Wang, Yi Xu, Zanwei Shen","submitted_at":"2024-09-07T02:07:22Z","abstract_excerpt":"In the ever-expanding landscape of academic research, the proliferation of ideas presents a significant challenge for researchers: discerning valuable ideas from the less impactful ones. The ability to efficiently evaluate the potential of these ideas is crucial for the advancement of science and paper review. In this work, we focus on idea assessment, which aims to leverage the knowledge of large language models to assess the merit of scientific ideas. First, we investigate existing text evaluation research and define the problem of quantitative evaluation of ideas. Second, we curate and rele"},"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":"2409.13712","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-07T02:07:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c74c744e4616ad497192d1e69516cec192c4988ef8b8e0bf1ad9732592a8c4af","abstract_canon_sha256":"f62041d70c51c0becc3523d42df83052d97b83d0ba2827a2b0d33165f49a166e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:10:05.843561Z","signature_b64":"3SHcscs43apqn+kxUO+1dK/zte4aQwLXDk57TxkndKMAxmxBT0gLQRSsVYIzHgIjEJUnKmIAvRhsTqGi9WmOCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"524376df738b733dc76714afe49d1dd5c8c8aacc97addd586753353ce79a9a46","last_reissued_at":"2026-07-05T09:10:05.843075Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:10:05.843075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Good Idea or Not, Representation of LLM Could Tell","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bo Xue, Cheng Deng, Chenghu Zhou, Jiaxin Ding, Luoyi Fu, Shuqian Sheng, Xinbing Wang, Yi Xu, Zanwei Shen","submitted_at":"2024-09-07T02:07:22Z","abstract_excerpt":"In the ever-expanding landscape of academic research, the proliferation of ideas presents a significant challenge for researchers: discerning valuable ideas from the less impactful ones. The ability to efficiently evaluate the potential of these ideas is crucial for the advancement of science and paper review. In this work, we focus on idea assessment, which aims to leverage the knowledge of large language models to assess the merit of scientific ideas. First, we investigate existing text evaluation research and define the problem of quantitative evaluation of ideas. Second, we curate and rele"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.13712","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/2409.13712/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":"2409.13712","created_at":"2026-07-05T09:10:05.843133+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.13712v1","created_at":"2026-07-05T09:10:05.843133+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.13712","created_at":"2026-07-05T09:10:05.843133+00:00"},{"alias_kind":"pith_short_12","alias_value":"KJBXNX3TRNZT","created_at":"2026-07-05T09:10:05.843133+00:00"},{"alias_kind":"pith_short_16","alias_value":"KJBXNX3TRNZT3R3H","created_at":"2026-07-05T09:10:05.843133+00:00"},{"alias_kind":"pith_short_8","alias_value":"KJBXNX3T","created_at":"2026-07-05T09:10:05.843133+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.05242","citing_title":"Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoring","ref_index":80,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KJBXNX3TRNZT3R3HCSX6JHI52X","json":"https://pith.science/pith/KJBXNX3TRNZT3R3HCSX6JHI52X.json","graph_json":"https://pith.science/api/pith-number/KJBXNX3TRNZT3R3HCSX6JHI52X/graph.json","events_json":"https://pith.science/api/pith-number/KJBXNX3TRNZT3R3HCSX6JHI52X/events.json","paper":"https://pith.science/paper/KJBXNX3T"},"agent_actions":{"view_html":"https://pith.science/pith/KJBXNX3TRNZT3R3HCSX6JHI52X","download_json":"https://pith.science/pith/KJBXNX3TRNZT3R3HCSX6JHI52X.json","view_paper":"https://pith.science/paper/KJBXNX3T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.13712&json=true","fetch_graph":"https://pith.science/api/pith-number/KJBXNX3TRNZT3R3HCSX6JHI52X/graph.json","fetch_events":"https://pith.science/api/pith-number/KJBXNX3TRNZT3R3HCSX6JHI52X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KJBXNX3TRNZT3R3HCSX6JHI52X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KJBXNX3TRNZT3R3HCSX6JHI52X/action/storage_attestation","attest_author":"https://pith.science/pith/KJBXNX3TRNZT3R3HCSX6JHI52X/action/author_attestation","sign_citation":"https://pith.science/pith/KJBXNX3TRNZT3R3HCSX6JHI52X/action/citation_signature","submit_replication":"https://pith.science/pith/KJBXNX3TRNZT3R3HCSX6JHI52X/action/replication_record"}},"created_at":"2026-07-05T09:10:05.843133+00:00","updated_at":"2026-07-05T09:10:05.843133+00:00"}