{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:K46HCG4Q3H4H6ESATFJHMI2WQE","short_pith_number":"pith:K46HCG4Q","schema_version":"1.0","canonical_sha256":"573c711b90d9f87f124099527623568100c0d5f21fb21ef5ac31ab7a07f18af7","source":{"kind":"arxiv","id":"2504.10893","version":2},"attestation_state":"computed","paper":{"title":"ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Chaochao Lu, Hongyu Lin, Kun Wang, Le Sun, Sirui Chen, Tianshu Wang, Xianpei Han, Xingyu Zeng, Yize Zhang","submitted_at":"2025-04-15T06:06:50Z","abstract_excerpt":"Large language models (LLMs) have demonstrated impressive capabilities and are receiving increasing attention to enhance their reasoning through scaling test--time compute. However, their application in open--ended, knowledge--intensive, complex reasoning scenarios is still limited. Reasoning--oriented methods struggle to generalize to open--ended scenarios due to implicit assumptions of complete world knowledge. Meanwhile, knowledge--augmented reasoning (KAR) methods fail to address two core challenges: 1) error propagation, where errors in early steps cascade through the chain, and 2) verifi"},"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":"2504.10893","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-04-15T06:06:50Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"e97a5058363392f0e795dc0be98f02a71074045be9766761024a2163ec647339","abstract_canon_sha256":"196164319022a764cade5d8723cd26a5a97181e1cdbf4b9cbd7d9e76822349cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:09:11.413567Z","signature_b64":"tqMBC6Fq4v6uzMaBmvpHGG42ZexT4WMgYNy5LhT27foUjIHm5wQgZbqqV5ey3uHgmt5cdmgFx8T0khn1ZdU2Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"573c711b90d9f87f124099527623568100c0d5f21fb21ef5ac31ab7a07f18af7","last_reissued_at":"2026-07-05T11:09:11.413083Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:09:11.413083Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Chaochao Lu, Hongyu Lin, Kun Wang, Le Sun, Sirui Chen, Tianshu Wang, Xianpei Han, Xingyu Zeng, Yize Zhang","submitted_at":"2025-04-15T06:06:50Z","abstract_excerpt":"Large language models (LLMs) have demonstrated impressive capabilities and are receiving increasing attention to enhance their reasoning through scaling test--time compute. However, their application in open--ended, knowledge--intensive, complex reasoning scenarios is still limited. Reasoning--oriented methods struggle to generalize to open--ended scenarios due to implicit assumptions of complete world knowledge. Meanwhile, knowledge--augmented reasoning (KAR) methods fail to address two core challenges: 1) error propagation, where errors in early steps cascade through the chain, and 2) verifi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.10893","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/2504.10893/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":"2504.10893","created_at":"2026-07-05T11:09:11.413141+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.10893v2","created_at":"2026-07-05T11:09:11.413141+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.10893","created_at":"2026-07-05T11:09:11.413141+00:00"},{"alias_kind":"pith_short_12","alias_value":"K46HCG4Q3H4H","created_at":"2026-07-05T11:09:11.413141+00:00"},{"alias_kind":"pith_short_16","alias_value":"K46HCG4Q3H4H6ESA","created_at":"2026-07-05T11:09:11.413141+00:00"},{"alias_kind":"pith_short_8","alias_value":"K46HCG4Q","created_at":"2026-07-05T11:09:11.413141+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.09666","citing_title":"Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems","ref_index":43,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K46HCG4Q3H4H6ESATFJHMI2WQE","json":"https://pith.science/pith/K46HCG4Q3H4H6ESATFJHMI2WQE.json","graph_json":"https://pith.science/api/pith-number/K46HCG4Q3H4H6ESATFJHMI2WQE/graph.json","events_json":"https://pith.science/api/pith-number/K46HCG4Q3H4H6ESATFJHMI2WQE/events.json","paper":"https://pith.science/paper/K46HCG4Q"},"agent_actions":{"view_html":"https://pith.science/pith/K46HCG4Q3H4H6ESATFJHMI2WQE","download_json":"https://pith.science/pith/K46HCG4Q3H4H6ESATFJHMI2WQE.json","view_paper":"https://pith.science/paper/K46HCG4Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.10893&json=true","fetch_graph":"https://pith.science/api/pith-number/K46HCG4Q3H4H6ESATFJHMI2WQE/graph.json","fetch_events":"https://pith.science/api/pith-number/K46HCG4Q3H4H6ESATFJHMI2WQE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K46HCG4Q3H4H6ESATFJHMI2WQE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K46HCG4Q3H4H6ESATFJHMI2WQE/action/storage_attestation","attest_author":"https://pith.science/pith/K46HCG4Q3H4H6ESATFJHMI2WQE/action/author_attestation","sign_citation":"https://pith.science/pith/K46HCG4Q3H4H6ESATFJHMI2WQE/action/citation_signature","submit_replication":"https://pith.science/pith/K46HCG4Q3H4H6ESATFJHMI2WQE/action/replication_record"}},"created_at":"2026-07-05T11:09:11.413141+00:00","updated_at":"2026-07-05T11:09:11.413141+00:00"}