{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PWT453FZRJSS6YQABXD747SKMP","short_pith_number":"pith:PWT453FZ","schema_version":"1.0","canonical_sha256":"7da7ceecb98a652f62000dc7fe7e4a63cc33a87fe5005590597b101b3114231c","source":{"kind":"arxiv","id":"2501.17338","version":1},"attestation_state":"computed","paper":{"title":"Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Jianxi Gao, Mingyu Derek Ma, Wei Wang, Yanna Ding, Yizhou Sun, Zijie Huang","submitted_at":"2025-01-28T23:21:28Z","abstract_excerpt":"Generative Language Models rely on autoregressive decoding to produce the output sequence token by token. Many tasks such as preference optimization, require the model to produce task-level output consisting of multiple tokens directly by selecting candidates from a pool as predictions. Determining a task-level prediction from candidates using the ordinary token-level decoding mechanism is constrained by time-consuming decoding and interrupted gradients by discrete token selection. Existing works have been using decoding-free candidate selection methods to obtain candidate probability from ini"},"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":"2501.17338","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-28T23:21:28Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"b3a10aa5b14212817b68427c0987bc869e67565240a9e8ee306e2f08d08aa55a","abstract_canon_sha256":"58c412f96ebf60ced683e2b6da6432a4819d9817793a9e6b15b3e12499bd8b41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:06:51.491119Z","signature_b64":"5IZRr7/58I08xw7qYzR1grGUdEtC4uIVSjRP00QAXpmbdWcvT3oTAC/aF+xx/Q75cOxD4GNIVDwrD9qouWYvBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7da7ceecb98a652f62000dc7fe7e4a63cc33a87fe5005590597b101b3114231c","last_reissued_at":"2026-07-05T10:06:51.490757Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:06:51.490757Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Jianxi Gao, Mingyu Derek Ma, Wei Wang, Yanna Ding, Yizhou Sun, Zijie Huang","submitted_at":"2025-01-28T23:21:28Z","abstract_excerpt":"Generative Language Models rely on autoregressive decoding to produce the output sequence token by token. Many tasks such as preference optimization, require the model to produce task-level output consisting of multiple tokens directly by selecting candidates from a pool as predictions. Determining a task-level prediction from candidates using the ordinary token-level decoding mechanism is constrained by time-consuming decoding and interrupted gradients by discrete token selection. Existing works have been using decoding-free candidate selection methods to obtain candidate probability from ini"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.17338","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/2501.17338/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":"2501.17338","created_at":"2026-07-05T10:06:51.490813+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.17338v1","created_at":"2026-07-05T10:06:51.490813+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.17338","created_at":"2026-07-05T10:06:51.490813+00:00"},{"alias_kind":"pith_short_12","alias_value":"PWT453FZRJSS","created_at":"2026-07-05T10:06:51.490813+00:00"},{"alias_kind":"pith_short_16","alias_value":"PWT453FZRJSS6YQA","created_at":"2026-07-05T10:06:51.490813+00:00"},{"alias_kind":"pith_short_8","alias_value":"PWT453FZ","created_at":"2026-07-05T10:06:51.490813+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/PWT453FZRJSS6YQABXD747SKMP","json":"https://pith.science/pith/PWT453FZRJSS6YQABXD747SKMP.json","graph_json":"https://pith.science/api/pith-number/PWT453FZRJSS6YQABXD747SKMP/graph.json","events_json":"https://pith.science/api/pith-number/PWT453FZRJSS6YQABXD747SKMP/events.json","paper":"https://pith.science/paper/PWT453FZ"},"agent_actions":{"view_html":"https://pith.science/pith/PWT453FZRJSS6YQABXD747SKMP","download_json":"https://pith.science/pith/PWT453FZRJSS6YQABXD747SKMP.json","view_paper":"https://pith.science/paper/PWT453FZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.17338&json=true","fetch_graph":"https://pith.science/api/pith-number/PWT453FZRJSS6YQABXD747SKMP/graph.json","fetch_events":"https://pith.science/api/pith-number/PWT453FZRJSS6YQABXD747SKMP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PWT453FZRJSS6YQABXD747SKMP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PWT453FZRJSS6YQABXD747SKMP/action/storage_attestation","attest_author":"https://pith.science/pith/PWT453FZRJSS6YQABXD747SKMP/action/author_attestation","sign_citation":"https://pith.science/pith/PWT453FZRJSS6YQABXD747SKMP/action/citation_signature","submit_replication":"https://pith.science/pith/PWT453FZRJSS6YQABXD747SKMP/action/replication_record"}},"created_at":"2026-07-05T10:06:51.490813+00:00","updated_at":"2026-07-05T10:06:51.490813+00:00"}