{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2D3NRF7SBZVPQIE6JDZVSFA5JT","short_pith_number":"pith:2D3NRF7S","schema_version":"1.0","canonical_sha256":"d0f6d897f20e6af8209e48f359141d4ccbc0f042351d54e7099f5f65ac61248c","source":{"kind":"arxiv","id":"2607.15655","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Multi-Step Lookahead Decoding for Diffusion Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Charu C. Aggarwal, Hang Li, Hui Liu, Lantao Mei, Wei Deng, Yingqian Cui, Yue Xing","submitted_at":"2026-07-17T06:04:04Z","abstract_excerpt":"Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternative to autoregressive decoding. Recent lookahead-based decoding methods improve the accuracy--efficiency trade-off by exploring future decoding states before committing token updates. However, existing approaches mainly rely on shallow one-step lookahead, which optimizes immediate information gain but can be suboptimal for longer-horizon decoding trajectories. Meanwhile, we find that a naive extension for deeper lookahead is also ineffective, as fixed-dept"},"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":"2607.15655","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-17T06:04:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ce84bc753a63e5fed5ebb087a5767625f9c652c6f11b339c418f5bf9b8db447e","abstract_canon_sha256":"8f7e5b6f1540c4de722f0a50de0dd281fc841149d2abb79f56d6e86df9e50048"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-20T01:19:02.455685Z","signature_b64":"HPnnLs3bLpHu4KOzxzqr+tkF0Gzdj+VBGDnquSxdSpvRQ1qd47XuxsD25M8YEmfT/UugR386Qqe5zkYBsVA9Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0f6d897f20e6af8209e48f359141d4ccbc0f042351d54e7099f5f65ac61248c","last_reissued_at":"2026-07-20T01:19:02.454804Z","signature_status":"signed_v1","first_computed_at":"2026-07-20T01:19:02.454804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Multi-Step Lookahead Decoding for Diffusion Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Charu C. Aggarwal, Hang Li, Hui Liu, Lantao Mei, Wei Deng, Yingqian Cui, Yue Xing","submitted_at":"2026-07-17T06:04:04Z","abstract_excerpt":"Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternative to autoregressive decoding. Recent lookahead-based decoding methods improve the accuracy--efficiency trade-off by exploring future decoding states before committing token updates. However, existing approaches mainly rely on shallow one-step lookahead, which optimizes immediate information gain but can be suboptimal for longer-horizon decoding trajectories. Meanwhile, we find that a naive extension for deeper lookahead is also ineffective, as fixed-dept"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.15655","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/2607.15655/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":"2607.15655","created_at":"2026-07-20T01:19:02.455245+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.15655v1","created_at":"2026-07-20T01:19:02.455245+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.15655","created_at":"2026-07-20T01:19:02.455245+00:00"},{"alias_kind":"pith_short_12","alias_value":"2D3NRF7SBZVP","created_at":"2026-07-20T01:19:02.455245+00:00"},{"alias_kind":"pith_short_16","alias_value":"2D3NRF7SBZVPQIE6","created_at":"2026-07-20T01:19:02.455245+00:00"},{"alias_kind":"pith_short_8","alias_value":"2D3NRF7S","created_at":"2026-07-20T01:19:02.455245+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/2D3NRF7SBZVPQIE6JDZVSFA5JT","json":"https://pith.science/pith/2D3NRF7SBZVPQIE6JDZVSFA5JT.json","graph_json":"https://pith.science/api/pith-number/2D3NRF7SBZVPQIE6JDZVSFA5JT/graph.json","events_json":"https://pith.science/api/pith-number/2D3NRF7SBZVPQIE6JDZVSFA5JT/events.json","paper":"https://pith.science/paper/2D3NRF7S"},"agent_actions":{"view_html":"https://pith.science/pith/2D3NRF7SBZVPQIE6JDZVSFA5JT","download_json":"https://pith.science/pith/2D3NRF7SBZVPQIE6JDZVSFA5JT.json","view_paper":"https://pith.science/paper/2D3NRF7S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.15655&json=true","fetch_graph":"https://pith.science/api/pith-number/2D3NRF7SBZVPQIE6JDZVSFA5JT/graph.json","fetch_events":"https://pith.science/api/pith-number/2D3NRF7SBZVPQIE6JDZVSFA5JT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2D3NRF7SBZVPQIE6JDZVSFA5JT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2D3NRF7SBZVPQIE6JDZVSFA5JT/action/storage_attestation","attest_author":"https://pith.science/pith/2D3NRF7SBZVPQIE6JDZVSFA5JT/action/author_attestation","sign_citation":"https://pith.science/pith/2D3NRF7SBZVPQIE6JDZVSFA5JT/action/citation_signature","submit_replication":"https://pith.science/pith/2D3NRF7SBZVPQIE6JDZVSFA5JT/action/replication_record"}},"created_at":"2026-07-20T01:19:02.455245+00:00","updated_at":"2026-07-20T01:19:02.455245+00:00"}