Latent-Kernel Discrete Diffusion for Few-Step Generation

TL;DRMasked diffusion models decide the tokens of a denoising step independently, and with few steps, tokens that must agree often do not. LKF makes each step a mixture of $M$ factorized components tied by one shared latent and trains it from scratch with the exact mixture likelihood. The masked diffusion language model (MDLM) is the case $M=1$. With eight components and best-of-8 decoding, LKF lowers the generative perplexity at 32 steps by 2.1 to 3.3 times relative to MDLM and score entropy discrete diffusion (SEDD) on LM1B and WikiText-103, and by 47% relative to MDLM given the same eight rollouts and selector.

Abstract

Discrete diffusion and flow-matching models denoise a sequence over many steps, but to keep each step cheap, they factorize the transition across positions and decide every token independently. This makes few-step generation challenging for text when the target couples two positions, such as a subject and a verb that must agree. An independent update commits to them separately, and many function evaluations are spent repairing the mismatch. Existing few-step methods buy back the lost correlation by distilling or rectifying a slow teacher, and so inherit the teacher's quality ceiling. We ask instead whether a model can express correlated steps natively, and answer with Latent-Kernel Discrete Diffusion (LKF), a from-scratch masked diffusion kernel that is a mixture of $M$ factorized components tied by a single shared latent. Conditioned on the latent, each component is cheap, and the mixture is summed over the latent in closed form for small $M$. We show that a single step places mass on correlated completions with the same sampling time complexity as a factorized model, since one latent is drawn per sequence and reused across the entire denoising trajectory. We also show that the Masked Diffusion Language Model (MDLM) is a special case of our LKF model at $M=1$. The experiments for unconditional text generation on the One-Billion-Word (LM1B) and WikiText-103 benchmarks show that our LKF model learns strongly heterogeneous components and improves generative perplexity by $2.1\times$ to $3.3\times$ over the likelihood baselines without losing diversity. The gain grows with $M$, and at $M=8$, it surpasses distilled and rectified few-step samplers.

Independent draws within a step

A masked diffusion model generates a sequence of $L$ tokens by unmasking it over several denoising steps. To keep a step cheap, it factorizes the transition from the current state $x_t$ to the next state $x_s$ across positions,

$$p_{t\to s}(x_s\mid x_t)\;=\;\prod_{i=1}^{L}p_i(x_s^i\mid x_t),$$

and decides each token independently given $x_t$. With few steps, each step reveals many tokens, and tokens revealed together are often coupled. A subject and its verb must agree in number, and an opening bracket needs its closing match. A factorized step can match the marginal of each token and still place much of its probability on combinations that are jointly inconsistent. What it misses is the total correlation (TC) of the step,

$$\mathrm{TC}(X_s\mid x_t)\;=\;\sum_{i=1}^{L}\mathrm{H}(X_s^i\mid x_t)-\mathrm{H}(X_s\mid x_t),$$

which is zero for any product distribution. In the example of Figure 1, the completions dog barks and dogs bark are equally likely, and each position is uniform over its two words. The target step has $\mathrm{TC}=\log 2$, while the product of its marginals puts half of its mass on dog bark and dogs barks. Later denoising steps then have to repair such mismatches, which costs function evaluations.

Few-step accelerators recover part of this correlation from a trained teacher, either by distillation, as in SDTT and Di4C, or by rectifying its coupling, as in ReDi. They inherit the teacher's quality ceiling. We ask instead whether a model can express correlated steps natively, without a teacher.

A product of per-position distributions has zero total correlation, however accurate its marginals are. Correlated tokens then need either more steps or a step of a different form.

Try one step

Factorized step

The loudly.

dog barks0

dog bark0

dogs barks0

dogs bark0

Agreement in no draws yet

LKF step, $M=2$

The loudly.

dog barks0

dog bark0

dogs barks0

dogs bark0

Agreement in no draws yet

Both steps use the numbers of Figure 1. The factorized step draws dog or dogs and, independently, barks or bark, each with probability one half. The LKF step draws $k=1$ or $k=2$ with probability one half, and the drawn component fills both positions. The factorized step agrees on about half of its draws, and the LKF step agrees on all of them.

A mixture of factorized steps

LKF keeps each component factorized and ties the components together with one shared latent $k\in\{1,\dots,M\}$. Its step from $x_t$ to $x_s$ is the mixture

$$\Psi^{\theta}_{t\to s}(x_s\mid x_t)\;=\;\sum_{k=1}^{M}w^{\theta}_k(x_t,t,s)\prod_{i=1}^{L}P^{\theta}_i(x_s^i\mid x_t,k,t,s).$$

A step first draws $k$ from the router weights $w^{\theta}$ and then samples all positions independently from component $k$. Given $k$, the step is as cheap as a factorized one. Summed over $k$, the positions become coupled, and the latent carries the correlation of the step. With $M=1$, the kernel is the factorized step of MDLM.

Figure 2: training and inference in LKF — **Training (a) and inference (b) with $M=4$ components.** (a) A clean sequence $x_1$ is masked to $x_t$ and to a target $x_s$. One pass of the diffusion transformer (DiT) trunk on $x_t$, conditioned on $(t,s)$, gives the router weights $w_1,\dots,w_4$ and the four factorized components $P^{(k)}$. All four enter the exact mixture likelihood of $x_s$, and its negative logarithm plus a router regularizer is the training loss. (b) Sampling starts from the all-mask state and draws one latent $k$, here $k=3$. Each of the $J$ steps runs the same trunk, samples the masked positions from component $k$, and feeds $x_s$ back as the next state.

Training with the exact mixture likelihood

For a target state $x_s$, let $\ell_k=\sum_{i:\,x_t^i=\texttt{[MASK]}}\log P^{\theta}_i(x_s^i\mid x_t,k)$ be the log-probability that component $k$ assigns to all revealed tokens together. The loss sums over the $M$ components in closed form, with no sampled latent and no variational bound,

$$\log\Psi^{\theta}_{t\to s}(x_s\mid x_t)=\operatorname{logsumexp}_k\big(\log w_k+\ell_k\big),\qquad \mathcal L_{\text{nll}}=-\mathbb E\big[\log\Psi^{\theta}_{t\to s}(x_s\mid x_t)\big].$$

Because the sum over positions is inside the logsumexp, one latent has to explain the whole masked set jointly. Exchanging the two would train the per-position marginal $\sum_k w_k P^{\theta}_i$ instead, which is blind to correlation and leaves the latent without a training signal. In the language experiments, the target is the clean sequence $x_1$ at $s=1$. A router regularizer keeps the latent in use,

$$\mathcal R=-\lambda_{\text{ent}}\,\mathrm H\big(\mathbb E_b[w]\big)+\lambda_{\text{lb}}\,\mathbb E_b\big[\mathrm H(w\mid x_t)\big],$$

where $\mathbb E_b$ averages over a batch. The first term spreads usage across the $M$ components and prevents the router from sending all inputs to one of them. The second term controls how much information the latent can carry, since $\mathrm I(k;X_s\mid x_t)\le\mathrm H(k\mid x_t)$. We set $\lambda_{\text{lb}}\le 0$, the opposite sign of the usual load-balancing loss, which keeps the prior entropy high and lets the exact likelihood sharpen the posterior over $k$.

Inference with best-of-$M$

Sampling starts from the all-mask sequence and takes $J$ steps. A rollout draws $k$ once at the start, where the router weights are a constant prior, and keeps it for the whole trajectory, which matches the sequence-level training objective. One rollout therefore uses one component. Best-of-$M$ decoding runs all $M$ components as parallel rollouts, scores each finished candidate by the model's own mixture negative evidence lower bound (NELBO), and returns the candidate with the lowest score. It needs no reward model, external judge or teacher, and the rollouts run concurrently at the sequential depth of a single chain.

Exact likelihood

The loss sums over the $M$ components in closed form, with no sampled latent and no variational bound.

Contains MDLM

With one component, the kernel is the factorized step of MDLM.

Teacher-free

LKF trains from scratch and needs no distillation or rectification of another model.

Shared trunk

The latent enters only the last 4 of 12 blocks. A step with $M=8$ then costs 40 block passes instead of 96.

Training and decoding algorithms

Algorithm 1 · LKF training

Draw a clean batch $x_1$ and one noise level $\tau\sim U[\varepsilon,1]$ per sequence.
Mask each position of $x_1$ with probability $(1-\varepsilon)\tau$ to get $x_\tau$, and let $\mathcal M$ be the masked positions.
Run the network once on $(x_\tau,s=1)$ to get the components $P^1,\dots,P^M$ and the router weights $w$.
For each $k$, compute $\ell_k=\sum_{i\in\mathcal M}\log P_i^k(x_1^i)$, how well component $k$ explains the whole masked set, and combine them as $\log\Psi=\operatorname{logsumexp}_k(\log w_k+\ell_k)$.
Take a gradient step on $-\log\Psi/\tau$, averaged over the batch, plus the router regularizer $\mathcal R$.

Algorithm 2 · Best-of-$M$ decoding with $J$ steps

For each component $k=1,\dots,M$, in parallel, start a rollout from the all-mask sequence.
At step $j=0,\dots,J-1$, run the network and reveal each masked position with probability $1/(J-j)$, drawing its token from $P_i^k$. Revealed tokens stay fixed.
Fill any position still masked after $J$ steps with the most likely token under $P_i^k$.
Score each candidate $\hat x^k$ by the mixture NELBO, averaged over $R$ draws of the noise level and mask shared across $k$, and return the candidate with the lowest score.

How much correlation one step can carry

We write $\mathrm{TC}(X_s\mid x_t)$ for the total correlation of the step distribution at a fixed state $x_t$, and $\mathrm I$ for mutual information. Three results bound what a mixture step can express.

Proposition 1 · Latent decomposition and correlation capacity

$$\mathrm{TC}(X_s\mid x_t)=\sum_{i=1}^{L}\mathrm I(k;X_s^i\mid x_t)-\mathrm I(k;X_s\mid x_t),\qquad 0\le\mathrm{TC}(X_s\mid x_t)\le(L-1)\,\mathrm H(k\mid x_t)\le(L-1)\log M.$$

All correlation of an LKF step passes through the latent. A step with $M$ components carries at most $(L-1)\log M$ nats of total correlation, and none when the router is certain of $k$.

Theorem 1 · Mixture approximation lower bound

Let $P$ be a distribution on sequences of $L$ tokens and $Q=\sum_{k=1}^{M}w_kQ_k$ a mixture of $M$ product distributions, with $Q>0$ wherever $P>0$. Then

$$\mathrm{KL}(P\,\|\,Q)\;\ge\;\big[\mathrm{TC}(P)-(L-1)\log M\big]_+.$$

Correlation of the target beyond the capacity of the mixture turns into approximation error. Applied to one step, with $P$ the true step and $Q$ the LKF step, the bound limits how close training can bring the two. The bound is tight on the hidden-agreement corpus below.

Theorem 2 · Parity separation

Let $P_d$ be the uniform distribution on the even-parity strings of $\{0,1\}^d$. A mixture $Q$ of at most $M$ product distributions within total variation distance $\mathrm{TV}(P_d,Q)\le\varepsilon<\tfrac13$ of $P_d$ needs

$$M\;\ge\;(1-3\varepsilon)\,2^{\,d-1}.$$

Some targets need exponentially many components. Parity couples all $d$ positions at once, and a mixture with far fewer than $2^{d-1}$ components cannot approximate it well.

Synthetic corpora with known correlation

We test both theorems on two synthetic corpora whose total correlation is known in closed form, and train LKF with $M\in\{1,2,4,8,16\}$ and three seeds. Hidden agreement repeats one of 16 tokens at all 8 positions, and a mixture of 16 components represents it without error. There, the captured information $\hat{\mathrm I}=\mathrm I(k;X_s\mid x_t)$ rises from 0 to 2.46 nats at $M=16$, which is 89% of the $\log 16$ ceiling, and the held-out negative log-likelihood (NLL) falls from 3.50 to 2.56 nats. On parity, which is uniform over the even-parity binary strings, the latent stays unused for all $M$. Its captured information and total correlation remain at zero, and the NLL stays flat near 7.1 nats, as Theorem 2 predicts.

$M$	$\log M$	$\hat{\mathrm I}$ (nats)	$\widehat{\mathrm{TC}}$ (nats)	$(L-1)\log M$	held-out NLL
1	0.00	0.00	0.00	0.0	3.50
2	0.69	0.68	4.71	4.9	3.11
4	1.39	1.30	9.01	9.7	2.93
8	2.08	1.87	13.02	14.6	2.70
16	2.77	2.46	17.12	19.4	2.56

Hidden agreement, full transition from $t=0$ to $s=1$, means over three seeds. Both information quantities stay below their ceilings, $\log M$ and $(L-1)\log M$, and the NLL falls as $M$ grows.

Diagnostics on the hidden-agreement corpus with sixteen components: captured information per transition, total correlation of frozen and routed components, and sample quality across refinement steps — **Diagnostics on hidden agreement with $M=16$.** (a) The captured information concentrates in the first, most masked transition, with 2.21 of the 2.46 nats in $t=0\to0.25$. (b) Each frozen component is close to deterministic, while the routed mixture reconstructs 26.7 of the 28 bits of total correlation, and shuffling $k$ across the batch leaves this unchanged. (c) The fraction of samples that are valid strings of the corpus rises from 0.47 at one step to 0.95 at 32 steps.

The latent captures correlation close to its ceiling where a mixture can represent it and stays unused where it cannot. Most of the captured information falls in the earliest, most masked transitions, which a few-step sampler can least afford to skip.

Few-step text generation

We train LKF from scratch with $M\in\{1,4,8\}$ on LM1B, with the BERT-base-uncased vocabulary, and on WikiText-103, with the GPT-2 vocabulary, at a sequence length of 128 for 200k steps. We report the generative perplexity (gen-PPL) under GPT-2 Large across the number of function evaluations (NFE), together with the sample entropy $H$, since the judge rewards low-entropy text. The baselines are the likelihood models MDLM and SEDD, the few-step accelerators SDTT, Di4C and ReDi, and an autoregressive transformer (AR) on LM1B.

Generative perplexity across NFEs

LM1B	ELBO-PPL ↓	gen-PPL ↓ at NFE						$H$ at 32 ↑	$\mathrm I(k)$
LM1B	ELBO-PPL ↓	1	2	4	8	16	32	$H$ at 32 ↑	$\mathrm I(k)$
MDLM	33.96	1082	1231	926	614	410	304	≈4.0	n/a
MDLM, best-of-8†	n/a	1992	1401	770	428	281	199	5.13	n/a
SEDD	42.37	3235	3253	1117	485	303	225	4.41	n/a
SDTT teacher, 7 rounds	n/a	1645	1122	612	359	243	194	4.08	n/a
Di4C, best round (4)	n/a	1375	899	453	236	156	115	3.99	n/a
ReDi, best rectification (3)	n/a	141	133	121	124	119	117	3.22	n/a
AR (reference)	27.61	135 at a fixed 128 NFE						4.34	n/a
LKF $M=1$, best-of-8†	n/a	2024	1308	706	400	262	183	5.12	0.00
LKF $M=1$	34.22	2398	1619	913	573	429	321	5.22	0.00
LKF $M=4$†	35.52	2169	1649	820	407	226	166	5.09	0.86
LKF $M=8$†	34.62	1827	1515	625	284	169	105	4.93	1.48

WikiText-103	ELBO-PPL ↓	gen-PPL ↓ at NFE						$H$ at 32 ↑	$\mathrm I(k)$
WikiText-103	ELBO-PPL ↓	1	2	4	8	16	32	$H$ at 32 ↑	$\mathrm I(k)$
MDLM	28.47	7801	4344	1523	613	343	228	4.33	n/a
SEDD	29.16	4003	4052	1382	638	362	247	4.36	n/a
LKF $M=1$	27.89	6198	3308	1336	558	316	223	5.33	0.00
LKF $M=4$†	29.79	5187	2475	849	312	164	102	5.28	0.87
LKF $M=8$†	28.56	4403	2195	673	241	119	75	5.25	1.41

† Best-of-$M$ decoding, which scores $M$ parallel rollouts by the model's own NELBO and spends $M(J+T)$ network calls for $J$ steps and $T$ scoring passes, against $J$ for one chain. The best-of-8 rows of MDLM and LKF $M=1$ use eight noise draws of one model at the same number of calls as LKF $M=8$. ELBO-PPL is the exponential of the per-token NELBO, $H$ is the sample entropy at 32 NFEs, and $\mathrm I(k)$ is the captured information in nats. Bold and underline mark the best and second-best perplexity in each column, without the AR reference.

On LM1B, the gain grows with $M$, and with eight components LKF reaches the lowest gen-PPL at 32 steps, 105, at a higher sample entropy than the accelerators. ReDi leads from one to sixteen steps with a nearly flat curve, but its entropy falls to 3.22, and the GPT-2 judge rewards low-entropy text. On WikiText-103, the mixture rows repeat this pattern, and LKF with $M=8$ reaches 75 at 32 steps, 3.0 times below MDLM and 3.3 times below SEDD. The autoregressive model has the best likelihood, but it needs 128 sequential steps, one per token, to reach a gen-PPL of 135.

Generative perplexity against the number of function evaluations on LM1B for MDLM, SEDD and LKF with one, four and eight components — **Generative perplexity across steps on LM1B.** LKF with $M\in\{1,4,8\}$ against MDLM and SEDD. Best-of-$M$ decoding, in dash-dotted lines, lowers the perplexity as $M$ grows, while single rollouts with one component, in solid lines, stay close to MDLM.

The gain comes from the mixture

Best-of-$M$ decoding reranks candidates, and reranking alone helps any model. To separate the two effects, we give MDLM and LKF with $M=1$ the same selector, judge and number of network calls, with eight noise draws of one model in place of eight components. Reranking lowers their gen-PPL at 32 steps from 363 to 199 and from 327 to 183, but the mixture reaches 106, a 47% and a 42% improvement. From four steps on, the advantage grows with the number of steps, from 14% to 42% at 32 steps, while at two steps the single-model pools are slightly ahead. Inside the model, replacing the $M$ components by $M$ noise draws of one component costs 16% at $M=4$ and 35% at $M=8$.

best-of-8 on LM1B	gen-PPL ↓ at $J$ steps
best-of-8 on LM1B	1	2	4	8	16	32
MDLM, 8 noise draws	1992	1401	770	428	281	199
LKF $M=1$, 8 noise draws	2024	1308	706	400	262	183
LKF $M=8$, 8 components	1791	1535	609	292	157	106
mixture advantage	+10%	−17%	+14%	+27%	+40%	+42%

A paired run in which each pool of eight candidates is ranked by its own model's NELBO with $T=4$ scoring passes, at $8(J+T)$ network calls for all rows. The last row is the improvement of the mixture over the better single-model pool. Absolute values differ by a few percent from the main table, which comes from a separate run.

Cheaper decoding

Scoring the candidates does not need extra passes. Each step already evaluates all $M$ components, and accumulating the log-probability that component $k$ assigns to the tokens it reveals gives each rollout a running evidence at no extra cost. Selecting by running evidence matches or beats the NELBO selector at each step count, with 125 against 131 at $M=8$ and 32 steps when the rollouts share their noise. Because it ranks rollouts before they finish, it also allows successive halving, which prunes the weakest rollouts partway through and reaches 145 with 96 network calls, three times fewer than full best-of-8, for an 11% higher perplexity.

All decoding strategies at $M=8$ and 32 steps

candidate pool	selector	network calls	gen-PPL ↓	$H$
8 components, independent noise	mixture NELBO	288	106	4.96
8 components, shared noise	mixture NELBO	288	131	4.98
8 components, shared noise	running evidence	256	125	4.96
8 components, successive halving	running evidence	96	145	5.00
1 component, 8 noise draws	mixture NELBO	288	163	4.96
8 components, independent noise	random pick	32	327	5.29
1 component	none	32	315	5.20

LM1B, from the ablation run of the paper. Shared noise reuses one random stream per step across the eight rollouts, so that only the component differs between them.

Example generations

Uncurated samples of LKF with $M=8$ on LM1B, in generation order. Each sample shows the component $k$ that generated it, numbered from 0 to 7, and its mixture NELBO per token, which the best-of-$M$ selector minimizes. The text uses the uncased BERT vocabulary, which is why it is lowercase with spaces around punctuation. Within a step count, a lower NELBO tends to go with more fluent text.

Samples by number of steps

The first four of 24 uncurated draws. Local phrases are fluent, but the syntax breaks within most clauses.

Sample 1 of 24k = 2, NELBO 6.56 per token

. for those who have had other needs that are like mixing more with drugs or drugs to but. in and, which republican brought in in 1994 and sever their powers. " tourne gordon.'s future policies for those around them. we prefer to loosely hedge the sink belonged to. - make think the difference were made by drawing a republican. " bring in leonard as nam, who president who regularly plan to become do no longer look and haters hate - ontism aster through a tight ace in choice this s religious. most of any country. in the campaign. " " cowen, he, take his

Sample 2 of 24k = 6, NELBO 5.56 per token

rector carlos pont. e. - pope benedict xvi overturns cutoff date.'prince 000 placed qna 1 on friday, june 1st. wanted. - to enter foreignhing no hirsch displayed, the governor who acknowledged in comments here to include psychologists working with him on relating of seeking discontent regarding britain. - mr. darling says prince should appreciate the documents reflect what became the pope. meeting is the centerpiece of globally. - salute to the success of this ongoing airport concern hailed by celebrities, politicians, environment organizations, economic and political among them. it was clear should the 82 - him al - am residence be temporarily sanctuary

Sample 3 of 24k = 1, NELBO 6.01 per token

through after that brief statement, help hall an anticoach column soldier is proceeding in the same spars through the feb doctors department, two tonnes 18 oil debris sheltering the deception, where all group not been ordered to. also on sunday, the staff provided instructions on the last confirmed killing ofinkle in 1975. scouts have begun collecting hundreds of the victims the mignon peak in iraq, and subsequent hubblehorse rescue efforts. more clues ton the high cuts and fire brian humanepto at. w., july 1991, 1996, he was in fire by helicopter and lives for guests,, including, and crisis

Sample 4 of 24k = 6, NELBO 5.49 per token

y general capital corp. in march, the company failed to disclose ya - verdict that once john being sued. by u and others gobench, z d. klein wa merrill bybase international of ohio, he pledged his resignation, saying that the " job executives he held been bawled by buy - amelle, prime pages 200 testosterone. for ralph was leaders general and you are against it. " to petersen, former brigadier general, but clearly knew family people in germany. during the time the needs of several people were involved. " gen. catherine gr warren, ohio - the media to promote the matter if it chooses to

The first four of 24 uncurated draws, followed by samples 8 and 13, the two draws of the set with the lowest NELBO. The newswire and finance text that dominates LM1B is coherent within clauses, with occasional agreement slips.

Sample 1 of 24k = 5, NELBO 2.65 per token

pre - spending mnd increased by $ 484, 000, compared to $ 4. 6 million. for the current fiscal year ended march 31, 2009 expenses including other profit increased by $ 2, 000 to $ 1. 2 million primarily by $ 5. 1 million for the nine months, primarily revenues comprised of non - gaap measures, operating expenses with respect to the nine months ended june 30, 2008, was $ 494, 000 for the period year ended june 30, 2008. for the first months of fiscal 2008 was $ 681, 000 or $ 6. 8 million a share, in the three months ended

Sample 2 of 24k = 0, NELBO 3.60 per token

they are england xv evel n'dravi ( yorkshire ), ed joyce ( australia ) ; 58 hong kong rpi buver ( sri lanka ), adam sunning ( australia ), ravi bombderpaul ( india ), david ag toshe ( hong kong ), graham onions ( sri lanka ), south africa ( england ), pd james, ravi bopara ( india ), india strauss ( england ), ian bell ( canterbury ), ryan sidebottom ), the ilungs ( england, 7 ) justin mathews ( australiaiksen ( england ) ), skipper hu raines ( middlesex followed

Sample 3 of 24k = 5, NELBO 4.06 per token

a it was last updated at 5. 01 bst on 23 3 april 2008. city of london. noon for central glasgow... £38, 000 - £37, 000 per £8, 000 employment. result in cash flow 20 % ( £38, 966. during the previous year. station leave £24, 014 - £22, 04 per annum inclusive. greater newport. west staffordshire. c.. £25. 50 to £35. 30. benefits levels is 7 - 6 positions. revised... audit id sales 44. 2 % 2007 working pay and offs. of over £3,

Sample 4 of 24k = 6, NELBO 3.48 per token

1 / prnewswire - usnewswire / - usnewswire - usnewswire / - - 16th century green palace of glory in the mint will celebrate the 35 - year - old addax in greenwich at the british embassy in rome on friday while blastinggrass won the grand prize at sustainable building society, whose new east greenweger research institute ( cumnaf ) is making henthorne contributions of the year award of $ 1, 780. annually to enable milestones of the nuclear industry. steel and sound systems, and aid original building. world by vindict fashionsar itmonger

Sample 8 of 24, one of the two lowest NELBOk = 2, NELBO 1.50 per token

the democratic administration has developed renewed fiscal policy with an easier attitude to put at risk. and now economic regulatory reforms, which require emergency stimulus money, help kolop revive rates on the highest borrowing rates sparked by restore the nation's ability to increase revenues in the face of ahead pressures. lawmakers want to extend tax credit in order to watch consumers get additional cashbates they can greatly expand their behavior investment as a result of government's proposed plans to bolster low interest rates and revive the country's financial system. they have intervened to unfree buy debt markets and keep investors in the financial community, waiting for that

Sample 13 of 24, one of the two lowest NELBOk = 5, NELBO 1.49 per token

the third and fourth quarter 2009 losses were $ 0. 078, 000 compared with net an income of $ 0. 03 for the fourth quarter of 2009 and 3, 743, 000 for the fourth quarter. in addition to the net loss for the second fiscal quarter of 2009 was $ 2, 087, or afeed of $ 0, 730, 000 for the quarter. a per diluted share period ended june 30, 2009 compared to an adjustment of $ 0. 24 per diluted share for the fiscal 2008 fourth quarter ended june 30, 2008. the third quarter that was $ 3, 416, 000

The first four of 24 uncurated draws, followed by sample 8, the draw of the set with the lowest NELBO. Whole sentences largely hold together, and the remaining errors are repetitions and numeric slips rather than broken structure.

Sample 1 of 24k = 7, NELBO 3.32 per token

the organization seeks to protect all natural resources from the impact of its exploration and exploration ( northeast australia, brazil, norway, iceland, russia, india, japan, iceland, mexico, hong kong, switzerland ) and japan, india and japan including the united and hong kong, peru, canada, bermuda, new mexico, canada, mexico, kingdoms of ivory coast, china, mexico, japan, australia, the united states, hong kong, singapore, shanghai, hong kong and london ( tonnes of ore ), the united states of australia ( kyoto ),. - ton tonnes of sydney ( singapore ), ( including hong kong ).

Sample 2 of 24k = 5, NELBO 1.84 per token

in mexico, net income was $19. 1. 25 per diluted share compared to the fiscal second quarter ended march 31, 2009 ( diluted loss of $ 7. 8 million or $ 0. 81 per diluted share in the second quarter of 2009 compared to $ 10. 9 ) million, or $ 0. 28 per diluted diluted share for the year ended in 2008. gaap revenue was $ 11. 9 million, or just 41 percent diluted share, as compared to $ 1. 2. 02 per diluted share, of $ 623, 000 for the first quarter of 2008. net income

Sample 3 of 24k = 6, NELBO 3.40 per token

boise, idaho, july 6 / prnewswire - firstcall / - - bainsray, idaho, may 20 / prnewswire - u. s. - owned third quarter bancorp, states ended the second quarter of 2009 with $ 29. 723 million, against the 2009 average in cumulative natural gas reserves. revenues were $ 70. 9 million or $ 58. 8 million, or $ 0. 50 per share. g $ 200. 2 million, up 81. 15 % to $ 133. 2 million compared to $ 1. 77 a share, a result that helped attract farmers kru

Sample 4 of 24k = 1, NELBO 3.03 per token

when you need to believe i'm american, it's a history moment or something about john mccain's first public address to where the american - changed era began, again : we really don't have some hitch choice of parting with time with the castro - castro of cuba... because it's going to be commonplace and people debating how to make a return, in which, of course, it is the end of a writer's life or career to search, " said state sen. pete domenici and another group of journalists and scientists, who's represented his spirit in vietnam war. "

Sample 8 of 24, lowest NELBO of the setk = 2, NELBO 0.87 per token

this press release contains forward looking statements that may cause actual results or actual results to differ materially and could be fully audited after the date of this press release. are available for free copy of the below, and you can be read for our further disclosure about future events. actual results may not " differ materially " from any of the factors that are expressly expressed or implied at appropriate time : within the regulations of our public securities, the requirements of the federal securities and exchange commission, in particular, companies'collaboration with anticipated market conditions and market conditions, economic conditions, general or economic conditions and financial and uncertain future results,

BibTeX

@inproceedings{ahmed2026lkf,
  title     = {Latent-Kernel Discrete Diffusion for Few-Step Generation},
  author    = {Ahmed, Mansoor and Fan, Yue-Tsz and Venkateswara, Hemanth and Patterson, Murray},
  booktitle = {NeurIPS 2026 Workshop on Beyond Next-Token Prediction (BeNTo)},
  year      = {2026}
}