Unmask the State: When Does State Adaptation Matter for Masked Diffusion Language Models

Injin Kong; Sunghwan Choi; Yohan Jo

cs.AISep 27, 2026

Unmask the State: When Does State Adaptation Matter for Masked Diffusion Language Models

Authors: Injin Kong, Sunghwan Choi, Yohan Jo

Organizations: Graduate School of Data Science, Seoul National University

Abstract

Masked diffusion language models (MDMs) admit flexible generation orders, making the unmasking strategy an inference decision. Existing methods vary in how they prioritize positions, control parallelism, restrict selection regions, revise predictions, or plan future denoising, yet it remains unclear when these choices should change during generation. We study this question through strategy reversals, where an alternative action becomes preferable to a fixed choice. We organize MDM inference into five axes--score, cardinality, region, commitment, and planning--and define adaptation opportunity as the one-step utility advantage of the best candidate action over a validation-selected fixed action. This view shows that adaptation value depends on both the frequency and magnitude of such reversals. Across three MDMs and ten tasks, adaptation opportunities are highly heterogeneous, with some regimes exhibiting concentrated and predictable one-step gains. This motivates selective adaptation: lightweight detectors calibrated on validation prompts identify high-opportunity states, capturing, for example, 56.9 percent of the candidate-set oracle opportunity by adapting only the top 10 percent of states on LLaDA-8B constrained JSON filling. Our transition-level results suggest that state adaptation is most useful when applied selectively rather than uniformly.

Figures & tables

Figure 1: Selective state adaptation. An axis-wise diagnostic proposes an alternative action, while a validation-calibrated opportunity detector decides whether to deviate from the fixed action.

Method	Score	Card.	Region	Commit.	Planning
LLaDA ( Nie et al., 2025b )	Conf.	Scheduled	Full	Irrev.	–
Fast-dLLM ( Wu et al., 2026 )	Conf.	Thresh. adapt.	Block	Irrev.	–
KLASS ( Kim et al., 2025b )	KL stab. + conf.	Multi-token	Full	Irrev.	–
DUS ( Luxembourg et al., 2026 )	Joint entropy	Grouped	Dilated	Irrev.	–
ReMDM ( Wang et al., 2025 )	Conf. / remask	Sched. dep.	Full	Remask.	–
WINO ( Hong et al., 2026c )	Draft verif.	Parallel draft	Active	Remask.	Draft–verify

Table 1: Representative methods under our five-axis taxonomy. Entries are abbreviated; full terms and an extended mapping are provided in Appendix A . “–” denotes no explicit mechanism beyond the underlying sampler.

Task	Axis	Positive states	Bidir. mass
CSV Missing Cells	region	0.0%	0.0025
Multi-Span Cloze	region	9.2%	0.0401
Constrained JSON Fill	region	13.6%	0.0225
TD07 Sparse Mask	region	4.5%	0.0238
JSON Mode Eval	region	7.0%	0.0418
Carry RTL	region	11.4%	0.0200

Table 2: Opportunity frequency and bidirectional reversal mass on LLaDA-1.5. Positive states are held-out states with positive candidate-set opportunity relative to the validation-selected fixed action. Bidirectional reversal mass is utility-weighted and is not a percentage.

Model	Task	Axis	Pos.	$C^{\star}_{5}$	$C^{\star}_{10}$	$C^{\star}_{20}$	$C^{\rm det}_{5}$	$C^{\rm det}_{10}$	$C^{\rm det}_{20}$
LLaDA-8B	Constrained JSON Fill	region	8.0%	62.7%	100.0%	100.0%	35.3%	56.9%	84.3%
Dream	Carry RTL	cardinality	4.2%	100.0%	100.0%	100.0%	42.9%	64.3%	71.4%
Dream	HTML Close Tags	region	4.7%	100.0%	100.0%	100.0%	16.7%	36.7%	66.7%
LLaDA-1.5	Carry RTL	region	11.4%	53.9%	89.9%	100.0%	4.5%	9.0%	31.5%

Table 3: Opportunity concentration versus detectability.

C^{\star}_{c}

denotes candidate-set oracle opportunity captured by top-

c

states, while

C^{\mathrm{det}}_{c}

denotes opportunity captured by detector-ranked states.

Model	Task	Axis	AUROC	Spearman	Pos. states	$C_{10}$	$L_{10}$
LLaDA-8B	Constrained JSON Fill	region	.854	.381	8.0%	56.9%	+.0453
Dream	Carry RTL	cardinality	.802	.368	4.2%	64.3%	+.0089
Dream	HTML Close Tags	region	.804	.261	4.7%	36.7%	+.0094
LLaDA-1.5	Carry RTL	region	.689	.242	11.4%	9.0%	+.0036
Dream	JSON Mode Eval	score	.891	.136	0.5%	0.0%	+.0000
LLaDA-1.5	JSON Mode Eval	region	.542	.051	7.0%	14.1%	+.0000

Table 4: Held-out opportunity detection.

C_{10}

is detector-captured candidate-set oracle opportunity at

10\%

coverage;

L_{10}

is realized one-step lift over the validation-selected fixed action. Prompt-cluster bootstrap 95% confidence intervals are reported in Appendix I .

Coverage	LLaDA-8B: Constrained JSON / region	Dream: Carry / cardinality	Dream: HTML / region
5%	35.3% / +.0281	42.9% / +.0057	16.7% / +.0016
10%	56.9% / +.0453	64.3% / +.0089	36.7% / +.0094
20%	84.3% / +.0594	71.4% / +.0099	66.7% / +.0188
50%	88.2% / +.0594	82.1% / +.0099	90.0% / +.0297
100%	100% / +.0594	100% / +.0099	100% / +.0297

Table 5: Coverage-dependent selective utility for representative successful regimes. Each entry reports the fraction of total candidate-set oracle opportunity captured / realized average one-step utility lift over the validation-selected fixed action.

Model	Task	Axis	Random $C_{10}$	Detector $C_{10}$	Oracle-gate $C_{10}$	Detector lift	Oracle-gate diagnostic lift
LLaDA-8B	Constrained JSON Fill	region	10.0%	56.9%	100.0%	+0.0453	+0.0625
Dream	Carry RTL	cardinality	10.0%	64.3%	100.0%	+0.0089	+0.0099
Dream	HTML Close Tags	region	10.1%	36.7%	100.0%	+0.0094	+0.0375

Table 6: Coverage controls at

10\%

coverage. Random

C_{10}

averages opportunity capture over 10,000 uniform state selections. Oracle-gate

C_{10}

ranks states by held-out candidate-set opportunity, while oracle-gate lift applies the same diagnostic action only to those oracle-selected states.

Model	Task	Axis	Ref.	Fixed	Stage	Always	Selective	$\Delta$	Coverage
LLaDA-8B	Unique List Commit	region	0.260	0.365	0.353	0.160	0.570	+0.205	54.2%
LLaDA-1.5	JSON Mode Eval	region	0.423	0.423	0.435	0.384	0.577	+0.154	19.6%
Dream	Carry RTL	commitment	0.433	0.467	0.467	0.367	0.567	+0.100	56.3%

Table 7: End-to-end selective decoding on illustrative settings. Terminal task utility under five decoding policies.

\Delta

denotes Selective

-

Fixed, and Coverage is the fraction of states where selective adaptation is activated. Full ten-task results are in Appendix Table 18 .

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Method	Score	Cardinality	Region	Commitment	Planning
LLaDA ( Nie et al., 2025b )	Confidence	Scheduled	Full	Irreversible	–
Fast-dLLM ( Wu et al., 2026 )	Confidence	Threshold-adaptive	Block	Irreversible	–
KLASS ( Kim et al., 2025b )	KL stability + confidence	Adaptive multi-token	Full	Irreversible	–
EB-Sampler ( Ben-Hamu et al., 2025 )	Entropy bound	Adaptive	Full	Irreversible	–
DOS ( Zhou et al., 2026 )	Attention dependency	Base sampler	Full	Irreversible	–
DEMASK † ( Ringel et al., 2026 )	Learned pairwise dependency	Dependency-bounded	Full	Irreversible	–

Appendix

Table 8: Extended mapping of masked diffusion inference methods under our five-axis taxonomy. Entries reflect our interpretation under the proposed taxonomy rather than terminology necessarily used by the original authors. “–” denotes no explicit additional mechanism, and

\dagger

denotes methods requiring an additional learned component.

Task	Horizon	Source	Output	Utility
CSV Missing Cells	16	constructed	one cell	binary
HumanEval	256	official	Python completion	test pass
Multi-Span Cloze	64	constructed	indexed spans	partial
GSM8K	256	official	numeric answer	binary
Carry RTL	32	constructed	three carry bits	partial
TD07 Sparse Mask	48	constructed	three spans	partial

Appendix

Table 9: Task-suite details. “Partial” indicates component- or field-level credit in

[0,1]

; “binary” indicates exact task success. The generation horizon is fixed within each task across candidate actions.

Model	Task	Axis	Gap	Bidir. mass
LLaDA-8B	CSV Missing Cells	Score	0.0000 (0.0000, 0.0000)	0.0000
LLaDA-8B	CSV Missing Cells	Region	0.0025 (0.0000, 0.0075)	0.0025
LLaDA-8B	CSV Missing Cells	Cardinality	0.0012 (0.0000, 0.0025)	0.0013
LLaDA-8B	CSV Missing Cells	Commitment	0.0000 (0.0000, 0.0000)	0.0000
LLaDA-8B	HumanEval	Score	0.0000 (0.0000, 0.0000)	0.0000
LLaDA-8B	HumanEval	Region	0.0048 (0.0000, 0.0141)	0.0048

Appendix

Table 10: Complete axis-wise adaptation-opportunity results. Gap is the held-out cross-fitted candidate-set oracle gap, with prompt-cluster bootstrap 95% confidence intervals shown in parentheses. Bidir. mass denotes axis-wise bidirectional reversal mass.

Model	Task	Axis	Corr.	Sign
LLaDA-8B	CSV Missing Cells	Score	–	1.000
LLaDA-8B	CSV Missing Cells	Region	–	0.778
LLaDA-8B	CSV Missing Cells	Cardinality	–	0.998
LLaDA-8B	CSV Missing Cells	Commitment	–	1.000
LLaDA-8B	HumanEval	Score	–	–
LLaDA-8B	HumanEval	Region	–	–

Appendix

Table 11: Complete diagnostic statistics. Corr. and Sign summarize diagnostic behavior for each model–task–axis setting. Dashes denote unavailable or undefined quantities.

Model	Task	Axis	FReg	SReg	DReg	Rec.	Screen
LLaDA-8B	CSV Missing Cells	Score	0.0000	0.0000	0.0000	–	no
LLaDA-8B	CSV Missing Cells	Region	0.0000	0.0000	0.0000	–	no
LLaDA-8B	CSV Missing Cells	Cardinality	0.0016	0.0016	0.0016	0.000	no
LLaDA-8B	CSV Missing Cells	Commitment	0.0000	0.0000	0.0000	–	no
LLaDA-8B	HumanEval	Score	–	–	–	–	–
LLaDA-8B	HumanEval	Region	–	–	–	–	–

Appendix

Table 12: Complete diagnostic action-selection utility. FReg, SReg, and DReg denote regret relative to the cross-fitted candidate-set oracle for the validation-selected fixed, stage-only, and diagnostic actions, respectively. Rec. denotes candidate-set oracle-gap recovery, and Screen indicates whether the exploratory held-out viability criteria are satisfied.

Model	Task	Axis	Fixed	Stage	Diagnostic	Oracle	D–F	D–S
LLaDA-8B	CSV Missing Cells	Score	0.0000	0.0000	0.0000	0.0000	+0.0000	+0.0000
LLaDA-8B	CSV Missing Cells	Region	0.0000	0.0000	0.0000	0.0000	+0.0000	+0.0000
LLaDA-8B	CSV Missing Cells	Cardinality	0.0000	0.0000	0.0000	0.0016	+0.0000	+0.0000
LLaDA-8B	CSV Missing Cells	Commitment	0.0016	0.0016	0.0016	0.0016	+0.0000	+0.0000
LLaDA-8B	HumanEval	Score	–	–	–	–	–	–
LLaDA-8B	HumanEval	Region	–	–	–	–	–	–

Appendix

Table 13: Complete transition-level utility results. Fixed, Stage, Diagnostic, and Oracle denote one-step utility under the validation-selected fixed action, stage-only action, diagnostic action, and cross-fitted candidate-set oracle, respectively. D–F and D–S denote diagnostic lift over the fixed and stage-only actions.

Model	Task	Axis	AUROC	$\rho$	Pos.
LLaDA-8B	CSV Missing Cells	Score	–	–	0.0%
LLaDA-8B	CSV Missing Cells	Region	–	–	0.0%
LLaDA-8B	CSV Missing Cells	Cardinality	0.500	–	0.2%
LLaDA-8B	CSV Missing Cells	Commitment	–	–	0.0%
LLaDA-8B	HumanEval	Score	–	–	–
LLaDA-8B	HumanEval	Region	–	–	–

Appendix

Table 14: Complete held-out opportunity-detection results. AUROC measures discrimination of positive-opportunity states,

\rho

denotes Spearman rank correlation with continuous candidate-set opportunity, and Pos. is the fraction of held-out states with positive opportunity. Dashes denote unsupported or undefined quantities.

Model	Task	Axis	5%	10%	20%	50%	100%
LLaDA-8B	CSV Missing Cells	Score	–	–	–	–	–
LLaDA-8B	CSV Missing Cells	Region	–	–	–	–	–
LLaDA-8B	CSV Missing Cells	Cardinality	0.0% (+0.0000)	0.0% (+0.0000)	0.0% (+0.0000)	0.0% (+0.0000)	100.0% (+0.0000)
LLaDA-8B	CSV Missing Cells	Commitment	–	–	–	–	–
LLaDA-8B	HumanEval	Score	–	–	–	–	–
LLaDA-8B	HumanEval	Region	–	–	–	–	–

Appendix

Table 15: Complete coverage-dependent selective-utility results. At each coverage level, each cell reports the fraction of total candidate-set oracle opportunity captured by detector-ranked states, with realized average one-step utility lift over the validation-selected fixed action shown in parentheses. Dashes denote unsupported or undefined quantities.

Model	Task	Axis	AUROC	$\rho$	$C^{\rm det}_{10}$	$L^{\rm det}_{10}$
Dream	Carry RTL	Cardinality	0.802 [0.699, 0.911]	0.368 [0.242, 0.486]	64.3% [43.9, 87.0]	+0.0089 [+0.0052, +0.0130]
Dream	Carry RTL	Commitment	0.498 [0.438, 0.556]	0.001 [-0.077, 0.081]	13.4% [6.9, 20.8]	-0.0005 [-0.0078, +0.0073]
Dream	Carry RTL	Region	0.540 [0.368, 0.862]	0.020 [-0.054, 0.088]	16.7% [0.0, 33.3]	+0.0000 [+0.0000, +0.0000]
Dream	Carry RTL	Score	–	–	–	+0.0000 [+0.0000, +0.0000]
Dream	Constrained JSON Fill	Cardinality	0.500 [0.500, 0.500]	–	4.9% [0.0, 12.1]	+0.0000 [+0.0000, +0.0000]
Dream	Constrained JSON Fill	Commitment	0.402 [0.321, 0.495]	-0.069 [-0.125, -0.003]	0.0% [0.0, 10.0]	+0.0000 [+0.0000, +0.0000]

Appendix

Table 16: Bootstrap robustness of opportunity detection and selective utility.

C^{\rm det}_{10}

is the fraction of total candidate-set oracle opportunity captured by the detector-selected top

10\%

of states, and

L^{\rm det}_{10}

is the corresponding realized average one-step utility lift over the validation-selected fixed action. Each cell reports the point estimate with its 95% confidence interval.

Model	Task	Axis	$C^{\star}_{10}$	Oracle-gate lift	Random $C_{10}$
Dream	Carry RTL	Cardinality	100.0% [100.0, 100.0]	+0.0099 [+0.0062, +0.0141]	10.0% [0.0, 21.4]
Dream	Carry RTL	Commitment	67.9% [56.4, 82.3]	+0.0312 [+0.0208, +0.0422]	10.0% [4.5, 15.7]
Dream	Carry RTL	Region	100.0% [100.0, 100.0]	+0.0000 [+0.0000, +0.0000]	10.1% [0.0, 33.3]
Dream	Carry RTL	Score	–	+0.0000 [+0.0000, +0.0000]	–
Dream	Constrained JSON Fill	Cardinality	100.0% [100.0, 100.0]	+0.0000 [+0.0000, +0.0000]	10.0% [2.4, 19.5]
Dream	Constrained JSON Fill	Commitment	100.0% [100.0, 100.0]	+0.0000 [+0.0000, +0.0000]	9.9% [0.0, 25.0]

Appendix

Table 17: Opportunity-concentration and coverage controls at

10\%

coverage.

C^{\star}_{10}

is the maximum fraction of total candidate-set oracle opportunity captured by the oracle-ranked top

10\%

of held-out states. Oracle-gate lift applies the same diagnostic action only to those oracle-selected states, and Random

C_{10}

is the opportunity fraction captured by uniformly selecting

10\%

of held-out states. Each cell reports the point estimate with its 95% confidence interval.

Model	Task	Axis	Ref.	Fixed	Stage	Always	Selective	$\Delta$	Coverage
LLaDA-8B	Carry RTL	region	0.027	0.647	0.647	0.693	0.693	+0.046	45.8%
LLaDA-8B	Constrained JSON Fill	region	0.320	0.000	0.000	0.000	0.000	0.000	62.9%
LLaDA-8B	CSV Missing Cells	region	0.040	0.080	0.080	0.080	0.080	0.000	49.6%
LLaDA-8B	GSM8K	region	0.660	0.760	0.760	0.760	0.760	0.000	30.1%
LLaDA-8B	HTML Close Tags	commitment	0.160	0.160	0.160	0.160	0.160	0.000	50.4%
LLaDA-8B	HumanEval	commitment	0.141	0.141	0.141	0.141	0.141	0.000	0.0%

Appendix

Table 18: Full end-to-end selective-decoding results. Terminal task utility under the reference decoder, validation-selected best-fixed action, stage-only schedule, always-diagnostic adaptation, and selective adaptation.

\Delta

denotes Selective

-

Fixed, and Coverage is the fraction of states at which selective adaptation is activated. Macro rows report the unweighted average across the ten tasks for each model.

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Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models

Yusuf Sahin, Ahmed Rockey Saikia, Volkan Cevher, Paolo Favaro

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Masked Diffusion Language Models Diffusion Language Models Adaptive Sampling

Open paper AskPDF arXiv

Masked diffusion language models can reduce inference steps by revealing multiple tokens per denoising iteration, but this parallelism is fragile: positions that are individually confident may be unsafe to commit together when their predictions are coupled. Existing training-free samplers such as Top-

k

, Fast-dLLM, and EB-Sampler mainly control how many tokens to reveal, while often ranking candidates by token-wise scores that ignore interactions within the selected set. We propose ADAS, a training-free reranking rule that leaves the base sampler's stopping rule unchanged and greedily discounts each token-wise confidence score according to its attention to already selected positions, weighted by their prediction uncertainty. Across LLaDA-8B-Base and Dream-7B-Base on the reasoning benchmarks GSM8K and MATH500 and the code benchmarks HumanEval and MBPP, plugging ADAS into all three samplers improves low-NFE performance at matched denoiser evaluations by

9.11

and

10.46

percentage points on average, respectively, with

3.1\%

per-forward runtime overhead. Code is available at https://github.com/yusufsahin99/ADAS.

Abstract

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