[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82818-en":3,"doc-seo-82818-105":29,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82818,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Don’t Commit Alone: Joint Token Commitment in Diffusion Large Language Models","Diffusion large language models commit multiple tokens in parallel at each denoising step, decoding each selected position independently and thereby introducing factorization error when committed positions are dependent. This gap is characterized by conditional total correlation, which confidence-based selection cannot detect from marginals alone. The COCOMMIT method adds a marker-gated coordination pass: it briefly defers commitment, re-applies the backbone to marked positions, and then performs greedy argmax, achieving joint-mode decoding using existing weights plus one extra partial forward pass.","arXiv :2607 .04469v 1 [ cs .CL] 5 Jul 2026  \nDon’t Commit Alone: Joint Token Commitment in Diffusion Large Language Models  \nLin Yao 1 ,2  \n1 School of Computer Science, Shanghai Jiao Tong University, Shanghai, 200240, China  \n2Zhongguancun Academy, Beijing, 100097, China  \n[lin.yao@sjtu.edu.cn](lin.yao@sjtu.edu.cn)  \nAbstract  \nDiffusion large language models (dLLMs) commit multiple tokens per denoising step by decoding each selected position independently from the shared context; when those positions are dependent, the resulting factorization error is captured by conditional total correlation, which confidence-based selection cannot observe from marginals alone. We propose COCOMMIT, a marker-gated coordination pass that briefly defers commitment: after the usual bundle selection, a learned marker announces the commit set and the backbone’s last-n layers are re-applied so marked positions coordinate—approximating joint-mode decoding—before greedy argmax writes tokens. The method reuses existing weights with one extra partial forward pass and no auxiliary model. On LLaDA2.1-mini with LoRA adapters and matched greedy inference, joint commitment improves accuracy on all six benchmarks we evaluate, with the largest gains on reasoning and exact-answer tasks.  \n1 Introduction  \nDiffusion large language models (dLLMs) generate text by iteratively revealing tokens from a masked sequence, committing multiple positions in parallel at each denoising step (2, 22, 24, 27, 35) . Parallelism is the source of their speed advantage over autoregressive decoding, and also the source of a characteristic error: tokens committed in the same step are predicted from the same context but not from each other, so each is drawn from its own marginal distribution while the tuple they form may belong to no coherent joint completion (17) .  \nThis failure is usually described operationally—“parallel tokens can be mutually inconsistent”—and treated downstream, by editing or remasking committed tokens after the fact (5, 30, 33) . We instead start from what the error is. For a commit set S and context ctx, factorized commitment replaces the joint posterior p (xS | ctx) with the product of marginals Qi∈S p (xi | ctx) . The gap between the two is the conditional total correlation TC(xS | ctx), and it vanishes if and only if the committed positions are conditionally independent given the context. The entire quality–speed trade-off of parallel decoding is governed by this single quantity: autoregressive decoding pays for exactness with sequentiality (|S| = 1, TC ≡ 0); parallel commitment in adLLM pays TC(xS | ctx) per step for speed.  \nThis view exposes a structural blind spot in how current samplers choose which positions to commit. Confidence-based selection—committing the positions whose marginals are most peaked—measures marginal entropy, not dependence. Marginals are averages over the joint modes of the posterior: in a context compatible with both “New York City” and “San Francisco”, the first position assigns high probability to both New and San, and each position’s confidence can be inherited from a different mode. High marginal confidence therefore does not imply low total correlation, and no selection rule that reads only marginals can bound the commitment error. The failure is not that confidence heuristics are poorly tuned; it is that the information they would need is absent from the quantities they observe.  \nIf selection cannot avoid dependent bundles, commitment must handle them. Our key observation is that the information required for coordination is already computed—the backbone’s hidden states encode the joint  \nstructure of the block—but the output interface discards it: a factorized readout head projects each position onto the vocabulary independently, at exactly the moment the positions’ fates are being decided. What is missing is not capacity but a communication channel at decision time.  \nCOCOMMIT adds that channel with minimal machinery (Fi","cbCaietspeyHr6Hg","https://ap.wps.com/l/cbCaietspeyHr6Hg","pdf",1344101,1,10,"English","en",105,"# Abstract\n# Introduction\n## Parallel commitment and factorization error\n## Conditional total correlation as the governing quantity\n## Blind spot of confidence-based position selection\n## COCOMMIT: marker-gated coordination pass","[{\"question\":\"How does COCOMMIT improve joint decoding without an auxiliary model?\",\"answer\":\"COCOMMIT uses a learned marker to announce the commit set, runs a coordination pass by re-applying the last-n layers so marked positions attend to each other, and then commits tokens via greedy argmax, approximating joint-mode decoding with one extra partial forward pass.\"}]",1784183166,25,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":27},"dont-commit-alone-joint-token-commitment-in-diffusion-large-language-models","",{"@graph":35,"@context":77},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/dont-commit-alone-joint-token-commitment-in-diffusion-large-language-models/82818/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How does COCOMMIT improve joint decoding without an auxiliary model?","Question",{"text":75,"@type":76},"COCOMMIT uses a learned marker to announce the commit set, runs a coordination pass by re-applying the last-n layers so marked positions attend to each other, and then commits tokens via greedy argmax, approximating joint-mode decoding with one extra partial forward pass.","Answer","https://schema.org",{"og:url":51,"og:type":79,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":81,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,126],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":45,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":45,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":45,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":45,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":21,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]