{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:MEUNIJRARSHWTQLIGXJUCRZ4JL","short_pith_number":"pith:MEUNIJRA","schema_version":"1.0","canonical_sha256":"6128d426208c8f69c16835d341473c4aebc0c56eb454dc380b25a5a2bfdcdcae","source":{"kind":"arxiv","id":"1311.3623","version":2},"attestation_state":"computed","paper":{"title":"On the Adaptivity Gap of Stochastic Orienteering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Nikhil Bansal, Viswanath Nagarajan","submitted_at":"2013-11-14T19:38:00Z","abstract_excerpt":"The input to the stochastic orienteering problem consists of a budget $B$ and metric $(V,d)$ where each vertex $v$ has a job with deterministic reward and random processing time (drawn from a known distribution). The processing times are independent across vertices. The goal is to obtain a non-anticipatory policy to run jobs at different vertices, that maximizes expected reward, subject to the total distance traveled plus processing times being at most $B$. An adaptive policy is one that can choose the next vertex to visit based on observed random instantiations. Whereas, a non-adaptive policy"},"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":"1311.3623","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2013-11-14T19:38:00Z","cross_cats_sorted":[],"title_canon_sha256":"011ac0869a34e628db7f36baaf27154654775bb8d8dba77bd2b1e596d5d67d45","abstract_canon_sha256":"0360f7debdfecd2c3f04775dcd95c911087932d24cf0383a48a95657f6236722"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:52:14.226931Z","signature_b64":"iaIJCJZxeiv44HlJFpZ3NubZ7aLbNBr9hCmJ3TtyK8hrCz9UdgCLsNGCr1HgW/s9FYtUL99zV7UBApFFFghyAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6128d426208c8f69c16835d341473c4aebc0c56eb454dc380b25a5a2bfdcdcae","last_reissued_at":"2026-05-18T02:52:14.226222Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:52:14.226222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Adaptivity Gap of Stochastic Orienteering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Nikhil Bansal, Viswanath Nagarajan","submitted_at":"2013-11-14T19:38:00Z","abstract_excerpt":"The input to the stochastic orienteering problem consists of a budget $B$ and metric $(V,d)$ where each vertex $v$ has a job with deterministic reward and random processing time (drawn from a known distribution). The processing times are independent across vertices. The goal is to obtain a non-anticipatory policy to run jobs at different vertices, that maximizes expected reward, subject to the total distance traveled plus processing times being at most $B$. An adaptive policy is one that can choose the next vertex to visit based on observed random instantiations. Whereas, a non-adaptive policy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.3623","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":""},"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":"1311.3623","created_at":"2026-05-18T02:52:14.226331+00:00"},{"alias_kind":"arxiv_version","alias_value":"1311.3623v2","created_at":"2026-05-18T02:52:14.226331+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.3623","created_at":"2026-05-18T02:52:14.226331+00:00"},{"alias_kind":"pith_short_12","alias_value":"MEUNIJRARSHW","created_at":"2026-05-18T12:27:52.871228+00:00"},{"alias_kind":"pith_short_16","alias_value":"MEUNIJRARSHWTQLI","created_at":"2026-05-18T12:27:52.871228+00:00"},{"alias_kind":"pith_short_8","alias_value":"MEUNIJRA","created_at":"2026-05-18T12:27:52.871228+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/MEUNIJRARSHWTQLIGXJUCRZ4JL","json":"https://pith.science/pith/MEUNIJRARSHWTQLIGXJUCRZ4JL.json","graph_json":"https://pith.science/api/pith-number/MEUNIJRARSHWTQLIGXJUCRZ4JL/graph.json","events_json":"https://pith.science/api/pith-number/MEUNIJRARSHWTQLIGXJUCRZ4JL/events.json","paper":"https://pith.science/paper/MEUNIJRA"},"agent_actions":{"view_html":"https://pith.science/pith/MEUNIJRARSHWTQLIGXJUCRZ4JL","download_json":"https://pith.science/pith/MEUNIJRARSHWTQLIGXJUCRZ4JL.json","view_paper":"https://pith.science/paper/MEUNIJRA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1311.3623&json=true","fetch_graph":"https://pith.science/api/pith-number/MEUNIJRARSHWTQLIGXJUCRZ4JL/graph.json","fetch_events":"https://pith.science/api/pith-number/MEUNIJRARSHWTQLIGXJUCRZ4JL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MEUNIJRARSHWTQLIGXJUCRZ4JL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MEUNIJRARSHWTQLIGXJUCRZ4JL/action/storage_attestation","attest_author":"https://pith.science/pith/MEUNIJRARSHWTQLIGXJUCRZ4JL/action/author_attestation","sign_citation":"https://pith.science/pith/MEUNIJRARSHWTQLIGXJUCRZ4JL/action/citation_signature","submit_replication":"https://pith.science/pith/MEUNIJRARSHWTQLIGXJUCRZ4JL/action/replication_record"}},"created_at":"2026-05-18T02:52:14.226331+00:00","updated_at":"2026-05-18T02:52:14.226331+00:00"}