{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:I354ISSLRZ3EHMKIEOSBDXKJLZ","short_pith_number":"pith:I354ISSL","schema_version":"1.0","canonical_sha256":"46fbc44a4b8e7643b14823a411dd495e76d3cb3825709c4a8782a0af54c726f4","source":{"kind":"arxiv","id":"2510.16609","version":3},"attestation_state":"computed","paper":{"title":"Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CC","cs.DS"],"primary_cat":"cs.LG","authors_text":"Avrim Blum, Cyrus Rashtchian, Daniel Hsu, Donya Saless","submitted_at":"2025-10-18T18:17:25Z","abstract_excerpt":"Test-time augmentation, such as Retrieval-Augmented Generation (RAG) or tool use, critically depends on an interplay between a model's parametric knowledge and externally retrieved information. However, the theoretical underpinnings of this relationship remain poorly understood. Specifically, it is not clear how much pre-training knowledge is required to answer queries with a small number of augmentation steps, which is a desirable property in practice. To address this question, we formulate multi-step reasoning as an $s$-$t$ connectivity problem on a knowledge graph. We represent a model's pr"},"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":"2510.16609","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-18T18:17:25Z","cross_cats_sorted":["cs.AI","cs.CC","cs.DS"],"title_canon_sha256":"a81935a22508f6586ab141800582c945af7fd6c7722b3df5ef0e34fd894c9718","abstract_canon_sha256":"3cd1e96e3679e117dfdf47e0e0b563d01579f6c3e817c78132db06f19f8447c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:15.038776Z","signature_b64":"F2+U/gyVm4u1lsM09G11kJrxRb9OGwKjYIyY+K47v9VtWCZCbSRw0XPgTDiPE1Kj/1DMITSU/mEwbTtgEHvbAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46fbc44a4b8e7643b14823a411dd495e76d3cb3825709c4a8782a0af54c726f4","last_reissued_at":"2026-05-20T00:04:15.038166Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:15.038166Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CC","cs.DS"],"primary_cat":"cs.LG","authors_text":"Avrim Blum, Cyrus Rashtchian, Daniel Hsu, Donya Saless","submitted_at":"2025-10-18T18:17:25Z","abstract_excerpt":"Test-time augmentation, such as Retrieval-Augmented Generation (RAG) or tool use, critically depends on an interplay between a model's parametric knowledge and externally retrieved information. However, the theoretical underpinnings of this relationship remain poorly understood. Specifically, it is not clear how much pre-training knowledge is required to answer queries with a small number of augmentation steps, which is a desirable property in practice. To address this question, we formulate multi-step reasoning as an $s$-$t$ connectivity problem on a knowledge graph. We represent a model's pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.16609","kind":"arxiv","version":3},"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/2510.16609/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":"2510.16609","created_at":"2026-05-20T00:04:15.038278+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.16609v3","created_at":"2026-05-20T00:04:15.038278+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.16609","created_at":"2026-05-20T00:04:15.038278+00:00"},{"alias_kind":"pith_short_12","alias_value":"I354ISSLRZ3E","created_at":"2026-05-20T00:04:15.038278+00:00"},{"alias_kind":"pith_short_16","alias_value":"I354ISSLRZ3EHMKI","created_at":"2026-05-20T00:04:15.038278+00:00"},{"alias_kind":"pith_short_8","alias_value":"I354ISSL","created_at":"2026-05-20T00:04:15.038278+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/I354ISSLRZ3EHMKIEOSBDXKJLZ","json":"https://pith.science/pith/I354ISSLRZ3EHMKIEOSBDXKJLZ.json","graph_json":"https://pith.science/api/pith-number/I354ISSLRZ3EHMKIEOSBDXKJLZ/graph.json","events_json":"https://pith.science/api/pith-number/I354ISSLRZ3EHMKIEOSBDXKJLZ/events.json","paper":"https://pith.science/paper/I354ISSL"},"agent_actions":{"view_html":"https://pith.science/pith/I354ISSLRZ3EHMKIEOSBDXKJLZ","download_json":"https://pith.science/pith/I354ISSLRZ3EHMKIEOSBDXKJLZ.json","view_paper":"https://pith.science/paper/I354ISSL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.16609&json=true","fetch_graph":"https://pith.science/api/pith-number/I354ISSLRZ3EHMKIEOSBDXKJLZ/graph.json","fetch_events":"https://pith.science/api/pith-number/I354ISSLRZ3EHMKIEOSBDXKJLZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I354ISSLRZ3EHMKIEOSBDXKJLZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I354ISSLRZ3EHMKIEOSBDXKJLZ/action/storage_attestation","attest_author":"https://pith.science/pith/I354ISSLRZ3EHMKIEOSBDXKJLZ/action/author_attestation","sign_citation":"https://pith.science/pith/I354ISSLRZ3EHMKIEOSBDXKJLZ/action/citation_signature","submit_replication":"https://pith.science/pith/I354ISSLRZ3EHMKIEOSBDXKJLZ/action/replication_record"}},"created_at":"2026-05-20T00:04:15.038278+00:00","updated_at":"2026-05-20T00:04:15.038278+00:00"}