{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SWEY5UQQ6TB4UGQVPEVB44NIT5","short_pith_number":"pith:SWEY5UQQ","schema_version":"1.0","canonical_sha256":"95898ed210f4c3ca1a15792a1e71a89f4418e464c7078e23faa5cd24e1c90b11","source":{"kind":"arxiv","id":"2503.13288","version":1},"attestation_state":"computed","paper":{"title":"$\\phi$-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Chang Ma, Fangzhi Xu, Haiteng Zhao, Hang Yan, Jun Liu, Qika Lin, Zhiyong Wu","submitted_at":"2025-03-17T15:38:33Z","abstract_excerpt":"Inference-time optimization scales computation to derive deliberate reasoning steps for effective performance. While previous search-based strategies address the short-sightedness of auto-regressive generation, the vast search space leads to excessive exploration and insufficient exploitation. To strike an efficient balance to derive the optimal step, we frame the decoding strategy as foresight sampling, leveraging simulated future steps to obtain globally optimal step estimation. Built on it, we propose a novel decoding strategy, named $\\phi$-Decoding. To provide a precise and expressive esti"},"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":"2503.13288","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-17T15:38:33Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"b01a460858de20c5aa8381cc1fcf8834e1b981bf124c3092c99b5ca3ba781aa7","abstract_canon_sha256":"b963624f5e99014d467bc0cc903f2974555003e25655bee7c8e67451823bc905"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:32:57.644100Z","signature_b64":"+CVnUkSXoBcmnzEoX7paBvpavBbIIkDz8tL0v009DS6ByCniXRjK52HiRZ8H43GlTLvm9SDaiHosyM3E8VBVBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"95898ed210f4c3ca1a15792a1e71a89f4418e464c7078e23faa5cd24e1c90b11","last_reissued_at":"2026-07-05T10:32:57.643184Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:32:57.643184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"$\\phi$-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Chang Ma, Fangzhi Xu, Haiteng Zhao, Hang Yan, Jun Liu, Qika Lin, Zhiyong Wu","submitted_at":"2025-03-17T15:38:33Z","abstract_excerpt":"Inference-time optimization scales computation to derive deliberate reasoning steps for effective performance. While previous search-based strategies address the short-sightedness of auto-regressive generation, the vast search space leads to excessive exploration and insufficient exploitation. To strike an efficient balance to derive the optimal step, we frame the decoding strategy as foresight sampling, leveraging simulated future steps to obtain globally optimal step estimation. Built on it, we propose a novel decoding strategy, named $\\phi$-Decoding. To provide a precise and expressive esti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.13288","kind":"arxiv","version":1},"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/2503.13288/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":"2503.13288","created_at":"2026-07-05T10:32:57.643304+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.13288v1","created_at":"2026-07-05T10:32:57.643304+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.13288","created_at":"2026-07-05T10:32:57.643304+00:00"},{"alias_kind":"pith_short_12","alias_value":"SWEY5UQQ6TB4","created_at":"2026-07-05T10:32:57.643304+00:00"},{"alias_kind":"pith_short_16","alias_value":"SWEY5UQQ6TB4UGQV","created_at":"2026-07-05T10:32:57.643304+00:00"},{"alias_kind":"pith_short_8","alias_value":"SWEY5UQQ","created_at":"2026-07-05T10:32:57.643304+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.22602","citing_title":"DeepLook: Deeper Thinking with Lookahead","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SWEY5UQQ6TB4UGQVPEVB44NIT5","json":"https://pith.science/pith/SWEY5UQQ6TB4UGQVPEVB44NIT5.json","graph_json":"https://pith.science/api/pith-number/SWEY5UQQ6TB4UGQVPEVB44NIT5/graph.json","events_json":"https://pith.science/api/pith-number/SWEY5UQQ6TB4UGQVPEVB44NIT5/events.json","paper":"https://pith.science/paper/SWEY5UQQ"},"agent_actions":{"view_html":"https://pith.science/pith/SWEY5UQQ6TB4UGQVPEVB44NIT5","download_json":"https://pith.science/pith/SWEY5UQQ6TB4UGQVPEVB44NIT5.json","view_paper":"https://pith.science/paper/SWEY5UQQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.13288&json=true","fetch_graph":"https://pith.science/api/pith-number/SWEY5UQQ6TB4UGQVPEVB44NIT5/graph.json","fetch_events":"https://pith.science/api/pith-number/SWEY5UQQ6TB4UGQVPEVB44NIT5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SWEY5UQQ6TB4UGQVPEVB44NIT5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SWEY5UQQ6TB4UGQVPEVB44NIT5/action/storage_attestation","attest_author":"https://pith.science/pith/SWEY5UQQ6TB4UGQVPEVB44NIT5/action/author_attestation","sign_citation":"https://pith.science/pith/SWEY5UQQ6TB4UGQVPEVB44NIT5/action/citation_signature","submit_replication":"https://pith.science/pith/SWEY5UQQ6TB4UGQVPEVB44NIT5/action/replication_record"}},"created_at":"2026-07-05T10:32:57.643304+00:00","updated_at":"2026-07-05T10:32:57.643304+00:00"}