{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LQWXWF2VVBAPFTESP5E52PT2RS","short_pith_number":"pith:LQWXWF2V","schema_version":"1.0","canonical_sha256":"5c2d7b1755a840f2cc927f49dd3e7a8c8172c3c1859b50bc68a59b2e17f0e936","source":{"kind":"arxiv","id":"2410.23166","version":2},"attestation_state":"computed","paper":{"title":"SciPIP: An LLM-based Scientific Paper Idea Proposer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Binbin Lin, Chen Shen, Jieping Ye, Liang Xie, Lihui Gu, Liye Zhang, Wenxiao Wang, Xiaofei He, Yi Dai, Yunxiang Luo","submitted_at":"2024-10-30T16:18:22Z","abstract_excerpt":"The rapid advancement of large language models (LLMs) has opened new possibilities for automating the proposal of innovative scientific ideas. This process involves two key phases: literature retrieval and idea generation. However, existing approaches often fall short due to their reliance on keyword-based search tools during the retrieval phase, which neglects crucial semantic information and frequently results in incomplete retrieval outcomes. Similarly, in the idea generation phase, current methodologies tend to depend solely on the internal knowledge of LLMs or metadata from retrieved pape"},"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":"2410.23166","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-30T16:18:22Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"title_canon_sha256":"e4737458801335b466ed8345dbcffda0a9aedb930693b924750bb066826d53d1","abstract_canon_sha256":"1b03a32d8a5dd8d8a66b5bf32d2246d65f2efb75aae036d91ceb76d3cc6a62cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:15:21.158297Z","signature_b64":"2LeVumxhsIUqWecSm1MCY8ZJ0Z7VIi9kpB+A2Rb08yUMzPs0k+j8UGGBvgUV5/UV2Qz3uS1hou3cKAypRf3GBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c2d7b1755a840f2cc927f49dd3e7a8c8172c3c1859b50bc68a59b2e17f0e936","last_reissued_at":"2026-07-05T10:15:21.157766Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:15:21.157766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SciPIP: An LLM-based Scientific Paper Idea Proposer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Binbin Lin, Chen Shen, Jieping Ye, Liang Xie, Lihui Gu, Liye Zhang, Wenxiao Wang, Xiaofei He, Yi Dai, Yunxiang Luo","submitted_at":"2024-10-30T16:18:22Z","abstract_excerpt":"The rapid advancement of large language models (LLMs) has opened new possibilities for automating the proposal of innovative scientific ideas. This process involves two key phases: literature retrieval and idea generation. However, existing approaches often fall short due to their reliance on keyword-based search tools during the retrieval phase, which neglects crucial semantic information and frequently results in incomplete retrieval outcomes. Similarly, in the idea generation phase, current methodologies tend to depend solely on the internal knowledge of LLMs or metadata from retrieved pape"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23166","kind":"arxiv","version":2},"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/2410.23166/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":"2410.23166","created_at":"2026-07-05T10:15:21.157836+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.23166v2","created_at":"2026-07-05T10:15:21.157836+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23166","created_at":"2026-07-05T10:15:21.157836+00:00"},{"alias_kind":"pith_short_12","alias_value":"LQWXWF2VVBAP","created_at":"2026-07-05T10:15:21.157836+00:00"},{"alias_kind":"pith_short_16","alias_value":"LQWXWF2VVBAPFTES","created_at":"2026-07-05T10:15:21.157836+00:00"},{"alias_kind":"pith_short_8","alias_value":"LQWXWF2V","created_at":"2026-07-05T10:15:21.157836+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.31229","citing_title":"Agentic-Ideation: Sample Efficient Agentic Trajectories Synthesis for Scientific Ideation Agents","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24018","citing_title":"EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.30961","citing_title":"EvoGens: A Population-Based Heuristic Search Framework for Scientific Idea Generation","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08723","citing_title":"From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22878","citing_title":"SciAtlas: A Large-Scale Knowledge Graph for Automated Scientific Research","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18661","citing_title":"AI for Auto-Research: Roadmap & User Guide","ref_index":212,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17675","citing_title":"Bridging the Gap on AI-Assisted Scientific Software Development Through Transparency and Traceability","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04922","citing_title":"Evolving Idea Graphs with Learnable Edits-and-Commits for Multi-Agent Scientific Ideation","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LQWXWF2VVBAPFTESP5E52PT2RS","json":"https://pith.science/pith/LQWXWF2VVBAPFTESP5E52PT2RS.json","graph_json":"https://pith.science/api/pith-number/LQWXWF2VVBAPFTESP5E52PT2RS/graph.json","events_json":"https://pith.science/api/pith-number/LQWXWF2VVBAPFTESP5E52PT2RS/events.json","paper":"https://pith.science/paper/LQWXWF2V"},"agent_actions":{"view_html":"https://pith.science/pith/LQWXWF2VVBAPFTESP5E52PT2RS","download_json":"https://pith.science/pith/LQWXWF2VVBAPFTESP5E52PT2RS.json","view_paper":"https://pith.science/paper/LQWXWF2V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.23166&json=true","fetch_graph":"https://pith.science/api/pith-number/LQWXWF2VVBAPFTESP5E52PT2RS/graph.json","fetch_events":"https://pith.science/api/pith-number/LQWXWF2VVBAPFTESP5E52PT2RS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LQWXWF2VVBAPFTESP5E52PT2RS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LQWXWF2VVBAPFTESP5E52PT2RS/action/storage_attestation","attest_author":"https://pith.science/pith/LQWXWF2VVBAPFTESP5E52PT2RS/action/author_attestation","sign_citation":"https://pith.science/pith/LQWXWF2VVBAPFTESP5E52PT2RS/action/citation_signature","submit_replication":"https://pith.science/pith/LQWXWF2VVBAPFTESP5E52PT2RS/action/replication_record"}},"created_at":"2026-07-05T10:15:21.157836+00:00","updated_at":"2026-07-05T10:15:21.157836+00:00"}