{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QVHSIWFP4USTGT7G2XVP2CMYGT","short_pith_number":"pith:QVHSIWFP","schema_version":"1.0","canonical_sha256":"854f2458afe525334fe6d5eafd099834fc64505bab480ba1bc2bd6e935128b17","source":{"kind":"arxiv","id":"2508.18648","version":2},"attestation_state":"computed","paper":{"title":"Thinking Before You Speak: A Proactive Test-time Scaling Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Cong Liu, Hejun Wu, Liang Lin, Pengxu Wei, Wenchang Chai, Yan Pan","submitted_at":"2025-08-26T03:43:32Z","abstract_excerpt":"Large Language Models (LLMs) often exhibit deficiencies with complex reasoning tasks, such as maths, which we attribute to the discrepancy between human reasoning patterns and those presented in the LLMs' training data. When dealing with complex problems, humans tend to think carefully before expressing solutions. However, they often do not articulate their inner thoughts, including their intentions and chosen methodologies. Consequently, critical insights essential for bridging reasoning steps may be absent in training data collected from human sources. To bridge this gap, we proposes inserti"},"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":"2508.18648","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-26T03:43:32Z","cross_cats_sorted":[],"title_canon_sha256":"cd7a4a8d1be111785b13a586b1dcb0a7310ab86921da34548c9f53da0d783ccc","abstract_canon_sha256":"c624c2b8488a2abf0af8147fa35c5388f4da5a10670b6aa1f4f603bb6d1a6027"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:00:10.366349Z","signature_b64":"vyO8Ejz6Jsh3RPiP/9jqmoVW5X1DEcU01P9pQFcFnTxLfJMesQPdWEBmMN8OILTzRHyLieBPmwMsGlzpmoYPBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"854f2458afe525334fe6d5eafd099834fc64505bab480ba1bc2bd6e935128b17","last_reissued_at":"2026-07-05T12:00:10.365828Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:00:10.365828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Thinking Before You Speak: A Proactive Test-time Scaling Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Cong Liu, Hejun Wu, Liang Lin, Pengxu Wei, Wenchang Chai, Yan Pan","submitted_at":"2025-08-26T03:43:32Z","abstract_excerpt":"Large Language Models (LLMs) often exhibit deficiencies with complex reasoning tasks, such as maths, which we attribute to the discrepancy between human reasoning patterns and those presented in the LLMs' training data. When dealing with complex problems, humans tend to think carefully before expressing solutions. However, they often do not articulate their inner thoughts, including their intentions and chosen methodologies. Consequently, critical insights essential for bridging reasoning steps may be absent in training data collected from human sources. To bridge this gap, we proposes inserti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.18648","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/2508.18648/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":"2508.18648","created_at":"2026-07-05T12:00:10.365895+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.18648v2","created_at":"2026-07-05T12:00:10.365895+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.18648","created_at":"2026-07-05T12:00:10.365895+00:00"},{"alias_kind":"pith_short_12","alias_value":"QVHSIWFP4UST","created_at":"2026-07-05T12:00:10.365895+00:00"},{"alias_kind":"pith_short_16","alias_value":"QVHSIWFP4USTGT7G","created_at":"2026-07-05T12:00:10.365895+00:00"},{"alias_kind":"pith_short_8","alias_value":"QVHSIWFP","created_at":"2026-07-05T12:00:10.365895+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/QVHSIWFP4USTGT7G2XVP2CMYGT","json":"https://pith.science/pith/QVHSIWFP4USTGT7G2XVP2CMYGT.json","graph_json":"https://pith.science/api/pith-number/QVHSIWFP4USTGT7G2XVP2CMYGT/graph.json","events_json":"https://pith.science/api/pith-number/QVHSIWFP4USTGT7G2XVP2CMYGT/events.json","paper":"https://pith.science/paper/QVHSIWFP"},"agent_actions":{"view_html":"https://pith.science/pith/QVHSIWFP4USTGT7G2XVP2CMYGT","download_json":"https://pith.science/pith/QVHSIWFP4USTGT7G2XVP2CMYGT.json","view_paper":"https://pith.science/paper/QVHSIWFP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.18648&json=true","fetch_graph":"https://pith.science/api/pith-number/QVHSIWFP4USTGT7G2XVP2CMYGT/graph.json","fetch_events":"https://pith.science/api/pith-number/QVHSIWFP4USTGT7G2XVP2CMYGT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QVHSIWFP4USTGT7G2XVP2CMYGT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QVHSIWFP4USTGT7G2XVP2CMYGT/action/storage_attestation","attest_author":"https://pith.science/pith/QVHSIWFP4USTGT7G2XVP2CMYGT/action/author_attestation","sign_citation":"https://pith.science/pith/QVHSIWFP4USTGT7G2XVP2CMYGT/action/citation_signature","submit_replication":"https://pith.science/pith/QVHSIWFP4USTGT7G2XVP2CMYGT/action/replication_record"}},"created_at":"2026-07-05T12:00:10.365895+00:00","updated_at":"2026-07-05T12:00:10.365895+00:00"}