CIAR: Interval-based Collaborative Decoding for Image Generation Acceleration
Authors: Keming Ye
Zhou Zhao
Fan Wu
Shengyu Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Cloud Device Collaboration
Image Generation
Uncertainty Quantification
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Auto-regressive (AR) models have recently made notable progress in image generation, achieving performance comparable to diffusion-based approaches. However, their computational intensity and sequential nature impede on-device deployment, causing disruptive latency. We address this via a cloud-device collaboration framework \textbf{CIAR}, which utilizes on-device self-verification to handle two key properties of visual synthesis: \textit{the vast token vocabulary} required for high-fidelity images and \textit{inherent spatial redundancy} which leads to extreme predictability in homogeneous regions, while object boundaries exhibit high uncertainty. Uniform verification wastes resources on such redundant tokens. Our solution centers on an on-device token uncertainty quantifier, which adopts continuous probability intervals to accelerate processing and make it feasible for large visual vocabularies instead of conventional discrete solution sets. Additionally, we incorporate a Interval-enhanced decoding module to further speed up decoding while maintaining visual fidelity and semantic consistency via a distribution alignment training strategy.
Extensive experiments demonstrate that CIAR achieves a 2.18× speed-up and reduces cloud requests by 70\%, while preserving image quality compared to existing methods.

Published as a conference paper at ICLR 2026
CIAR: INTERVAL-BASED COLLABORATIVE DECOD- ING FOR IMAGE GENERATION ACCELERATION
Keming Ye', Zhou Zhao', Fan Wu?, Shengyu Zhang'
'Zhejiang University Shanghai Jiao Tong University
(kemingye, zhaozhou, sy-zhangJezju. edu. en wu-fanesjtu. fer.ss..uuuuuuurr
ABSTRACT
Auto-regres nuto-regressive (AR) models have recently made notable progress in image gen- eration, achieving performance comparable to diffusion- basedd approaches. How- ever, their computational intensity and sequential nature impede on-device deploy- ment, causing disruptive latency. We address this via a cloud-device collaboration framework CIAR. which utilizes on-device self-verification to handle two key properties of visual synthesis: the vast token vocabulary required for high-fidelity images and inherent spatial redundancy which leads to extreme predictability in homogeneous regions, while object boundaries exhibit high uncertainty. Uniform verification wastes resources on such redundant tokens. Our solution centers on an on-device token uncertainty quantifier, which adopts continuous probability intervals to accelerate processing and make it feasible for large visual vocabular- ies instead of conventional discrete solution sets. Additionally, we incorporate a Interval-enhanced decoding module further speed up decoding while maintain- ing visual fidelity and semantic consistency via distribution alignment training strategy. Extensive experiments demonstrate that CIAR achieves 2.18 speed- up and reduces cloud requests by 70%, while preserving image quality compared to existing methods.
INTRODUCTION
Auto-regressive (AR) models have emerged as powerful paradigm for high-fidelity image synthe- sis (Brown et al. 2020 Touvron et al. 2023), with architectures like Anole and LlamaGen (Sun
let al. 2024 Chern et 2024) achieving remarkable results rivaling diffusion models (Ho al.
2020
Rombach et al. (022). These approaches leverage discrete token representations that capture rich visual semantics through codebooks of increasing size, enabling unprecedented photorealism. However, despite their capabilities, AR models suffer from critical limitations that impede their practical deployment. (Van Den Oord al. 2017 Esser et
2021) A key challenge lies in their substantial parameter size, driven by the expanding codebooks used for tokenization, which makes hard for on-device deployment. Furthermore, the inherent sequential nature of AR models, requir- ing one- by-one token generation, results in slow inference speeds that degrade the users' experience, especially on resource-constrainee devices.
A promising direction to address these practical limitations is to leverage cloud-device collabo- rative framework. this setup, a lightweight AR model is deployed on the device for fast token generation, while larger model on the cloud performs token verification in parallel, similar to the speculative decoding (SD) paradigm (Chen et al. [2023] Cai et al. 2023). Several developments in visual speculative decoding provide feasible foundation for this architecture, offering the potential to combine the efficiency device computing with the power of cloud-based models for real-time image generation. (Wang et al. 2024b Jang et al. 2024)
However, two fundamental challenges critically undermine conventional cloud-device collaboration for AR image generation. The first is the prohibitive overhead from high-volume token verifica-

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Published as a conference paper at ICLR 2026
2.12x
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Accepttncee Rate of Tokees
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LM
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Mila cloud (a) Results of greedily acceptance without verification
(b) Difference between our framenork with traditional methods
(a) Acceptance analysis of Lantern. The pic chart shows the ratio of max-prob vs. other tokens, and the bar chart compares Lantern without verification to the baseline. (b) Comparison of decoding frameworks. From left right: baseline, Lantern, and our CIAR with Inter-Head and cloud-device collaboration, which reduces latency while preserving output quality.
Figure 1:
tion. Unlike text, image tokens scale quadratically with resolution, creating a data explosion that establishes the network as the primary bottleneck (Van Den Oord et al. 2016; Chang et al.) 2002). This not only negates the cloud's computational advantage but also incurs substantial energy and operational costs, rendering the framework impractical for real-world use. The second is the in- efficiency of the uniform verification policy. Conventional verification employs an indiscriminate, bokennby-token validation process (Zhang et al., 2025), treating every part of the image equally important. This approach is misaligned with the intrinsic properties of visual data, which exhibit spatially heterogeneous uncertainty (Kendall & Gall 2017). Vast regions of an image, such as back- grounds and smooth surfaces, contain highly redundant and predictable information (low-entropy regions). Verifying tokens in these areas squanders communication cost and cloud resources on overwhelmingly correct predictions (Bolya et al. 20022). Conversely, semantically critical areas like object boundaries and complex textures introduce high localized uncertainty where generation er- rors are most likely to occur and propagate. The uniform verification strategy fails to distinguish between these cases, resulting in suboptimal trade-off: it wastefully validates the predictable while failing to concentrate scrutiny on the uncertain, thereby limiting both speed-up potential and quality assurance.
Building on our prior analysis, we first explore the limits of pure on-device decoding. As shown in Figure [a), when the device model greedily selects the top-probability token, overall scene seman- tics are preserved but perceptual quality degrades markedly, with visible artifacts and blurring in fine details (Dahl et al., 2017). closer inspection reveals that approximately 70% tokens approved by the cloud already match the device's top-output, indicating that most tokens can be decided locally,
while a subset of detail-related tokens still benefits from cloud correction (Liu et al., 2023 Zhang al. 2023). Motivated by this finding, we hope insert lightweight uncertainty quantifier the device for self-verification However, off-the-shell shelf entropy measures/Ali et al.] 2225);; Nikitin al. (2024); Stolte al. (2025) are incffective over large visual vocabularies and ignore spatial context, thereby masking true ambiguity. Moreover, once the device commits to verified prefix, its conditional distribution may drift from the cloud model's expectations, impairing subsequent decoding (Bengio et al. 2015). Thus, it is essential to align the device and cloud distributions.
To tackle these issues, we introduce CIAR Collaborative Interval- based AutoRegressive Decod- ing framework designed for efficient, high-fidelity visual synthesis. Our framework consists of two modules: on-device Interval-based uncertainty quantifier and Interval-enhanced decoding module, and introduces distribution alignment training strategy to support them.
Interval- -ased uncertainty quantifier measures token uncertainty on device, capturing continu- ous visual distribution characteristics while improving computational efficiency. At its core is our Inter-Head which outputs upper and lower probability intervals for each token. By using con- tinuous probability intervals instead of discrete solutions, it preserves the continuous nature of vi- sual tokens for more precise uncertainty quantification and significantly reduces time complexity. decoding module facilitates collaboration between cloud and device to maintain
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distribution alignment. Excessive local token generation can lead to distribution drift, impairing visual coherence. We counteract this by incorporating prefix injection and interval-based features for enhanced-decoding which jointly constrain distribution shift and sustain high image quality. Distribution alignment training strategy is designed to train the Inter-Head for alignment with the cloud distribution. Since the Inter-Heed outputs probability intervals, which is distinct from the cloud module, we introduce an interval-aware DRO variant, employing an alignment loss computed over both lower and upper bound logits, progressively reducing divergence between distributions and ensuring output consistency.
Our contributions can be summarized as follows:
1. We propose CIAR, collaborative interval- based autoregressive decoding framework that leverages on-device uncertainty-awar verification to eliminate redundant uniform verifi- cation and accelerate image synthesis while maintaining high fidelity.
2. We design an Inter-Head for continuous interval estimation, which derives upper and lower probability intervals from logits, and an -loun-Enaaneed decoding module that uses collaborative feature guidance for enhanced-decoding supported by an interval-aware DRO training strategy, aligning cloud and device distribution despite structural discrepancy.
3. We conduct extensive experiments, demonstrating that CIAR achieves a 2.18x speed- up and reduces cloud requests by 70% compared to faat-oof-hhe-a speculative decoding methods, without compromising visual fidelity.
2 RELATED WORKS
Visual Auto- Regoo-Regessvveeeeeeee Models. Visual AR models extend language model paradigms to image generation by converting images into discrete token sequences (Van Den Oord et al. 2017 Esser ict al. (021). Early works with raster scan orders suffered high cost and inferior quality compared to diffusion models. More recent methods such VQVAE/VQGAN--ue
AR models like Llam- aGen (Sun et al. 2024) utilize large vocabularies and improved tokenizers to produce high-fidelity next-token predictions. Models like Anole (Chern al. 2024 further support native multimodal and interleaved image-text generation with fine-tuned visual tokens. Despite the advances, these models remain too slow and resource-intensive for on-device use under large vocabulary, long quence settings.
Cloud-Device Collaborative Inference. Cloud-device collaboration balances computation be- tween powerful cloud servers and resource-limited devices. Federated Learning (McMahan et al.) 2017) is common approach, enabling local training with cloud-based gradient aggregation while preserving privacy, but it struggles with real-time distribution shifts. Other works focus on device adaptation: DCCL (ao et al.) (021) performs cloud pre-training followed by on-device fine-tuning, while DUET (Lv et al., 2023b) uses a cloud hypernetwork to generate personalized weights from device data. More interactive paradigms have also emerged, such CD-CCA (Wang et al.) (024a), which uploads high-uncertainty samples for cloud analysis, and DC-CCL (Ding et al. 2003), which splits large models into cloud and device modules and refines both via knowledge distillation.
3 METHODOLOGY
In this section, introduce CIAR, a collaborative interval-based autoregressive decoding frame- work, designed to accelerate on-device decoding while enhancing visual synthesis quality. We first define the problem setup, then describe the overall framework and detail the key components.
3.1 PRELIMINARY
We denote the cloud-side AR model by CloudAR with conditional distribution gelond ( T), and the device model by Device AR with Qdevice (zy [r T). Here T = (fI...., (t) is the tokenized text prompt and X = (#I,.... ., xK) the sequence of image tokens. Each token 2, takes values in C {cI:-.., ) where L. is the size of the visual codebook. In text-to-image generation, the
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Transformer Layer
Eup
Upper
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Pronuee
Tokenizer
Embedding
LM Head
VQ Decader
Cloud Model
Las.
Device Model
Prefix
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Figure 2: Overview of CIAR. (a) The cloud-side AR model generates image token prefixes from the input prompt. These prefixes are then to (b) lightweight device model with Inter-Head accepts confident tokens locally and sends uncertain ones with interval features to the cloud for verification and distribution alignment. (c) Interval-Bann -aasdd Alignment Strategy. (d) Computation of uncertainty intervals in the Inter-Head.
model samples
H J
W J
a(X (T) = II p(re Tee 7), where K = w,
W
(I)
for an H x W image and downsampling factor f. Prediction is strictly autoregressive: each 2t depends only its predecessors and the prompt. Once .I,K...,KKK are generated, they are mapped to embeddings via learned codebook:
E { E R li=
so that 2, + Cu.- The embeddings are then reshaped into tensor of shape (h, w, D) raster-scan order and passed to a decoder (e.g., VQVAE or VQGAN) to reconstruct the final image.
3.2 CIAR: COLLABORATIVE INTERVAL- BASED AUTOREGRESSIVE DECODING
CIAR consists of three key components: an on-device interval-Based uncertainty quantifier pow- ered by a lightweight Inter-Head; generation using device-provided interval cues; and distribution alignment training strategy that aligns token distributions between device and cloud.
decoding module on the cloud that refines
3.2.1 DEVICE- SIDE UNCERTAINTY QUANTIFICATION
The device uses a lightweight AR model for fast token generation. To identify reliable tokens, precise uncertainty quantification is essential. Traditional entropy-basse based methods are ill-suited for visual synthesis due to two inherent visual properties: (1) large codebooks yield flat probability dis- tributions, and (2) spatial continuity leads similar entropy among related tokens. We thus propose the Interval Head (Inter-Head) estimate uncertainty via continuous probability intervals.
Interval Head Architecture. Different from standard LM Head, Inter-Head extends the output dimension to 2 [V] where V is the vocabulary. Formally, given hidden state h, for token t:
C₁ Linear (hy), r. = Softplus (Linear,adius (h,)). P InterFuse(c,. rt).
The use of Softplus function ensures the radius rı is strictly non-negatin nog-neeative. This defines a logit interval [1, [1] [c Te, Ce r. We define
operator InterFuss Fuse to yield the final probability interval P, pl pl),, which is guaranteed to satisfy the properties of a valid solution set, namely E.pl < Σ. pp. A detailed proof is provided in the Appendix A.4
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Uncertainty Quantification. Given the probability interval Pt. require metric that converts it into scalar measure of uncertainty. A simple average is inadequate, as token uncertainty depends on both the total volume and the dispersion of the interval widths. Based on properties observed in visual autoregressive generation, we find that uncertainty increases with a larger total interval volume and rises with greater variance among interval widths. A detailed analysis of these principles is provided in the Appendix A.3
Adhering to these principles, formulate novel uncertainty score. Let òG p²- R be the vector of probability interval widths. We first define the Total Ambiguity Volune, Ue the L norm the widths, and the Confidence Disparity, Lx, as the standard deviation of the widths. The final uncertainty score U(P,) is their product:
u(Pi)
1 M
Σ
-
(4)
Totaall Amhiguity
Ex:Coniieecce Disparity
where 5; is the mean of the interval widths òe,i. This formulation ensures that the score is high only when both the total ambiguity and the disparity are significant, providing robust and sensitive measure of uncertainty. This score is then used by dynamic thresholding policy to make the final determination for token acceptance.
3.2.2 CLOUD-ENNANCEE DECODING
Early in generation, limited context on the device often causes deviation from the target distribution, while later, substantial local tokens may lead the cloud predictions to drift. To mitigate this, we introduce Cloud-Enhanced Decoding module combining Prefix Injection and Interval-enhanced Decoding.
Prefix Injection. To improve initial generation quality and reduce unnecessary cloud requests, the cloud pre-generates short prefix (yi,..., 3m where m = [o- TI, with the prefix rate. This prefix provides high-quality contextual guidance to the device, constraining local generation and alleviating distribution drift. The prefix length involves trade-off: larger p improves alignment and fidelity but increases cloud latency, while smaller P speeds up decoding at the cost of weaker guidance. Empirically, moderate achieves the best balance, significantly lowering cloud queries while maintaining visual quality.
Interval-enhancee Decoding. We introduce Interval Feature Injection incorporate device-side uncertainty into cloud verification. For each locally accepted token Tt+i, the device's Inter-Head produces center and radius logits (Ct+i, rtts), defining probability interval Pe-i. lightweight projection network & maps the token's 's hidden state fi+i and its interval Peti to a compact Interval Feature f445
f = p (Concat (f-+))))))) e IRd.
(5)
This interval feature serves structured, actionable intelligence about the device's confidence.
During the verification or resampling phase on the cloud, this auxiliary feature is injected into the cloud model's decoder. As the cloud model processes the token 2++is its decoder's computation is conditioned not only on the token's 's embedding 5(I++:) but also on our interval feature. The update rule for the cloud's hidden state hC hi++1 is thus modified:
C.C #++++1
C (F((++:) fi4.)
By augmenting the input with this confidence-aware feature, we actively steer the cloud's generative process. This maintains distributional alignment and prevents error accumulation, enhancing output consistency and visual continuity.
3.2.3 INTERVAL-BASED ALIGNMENT STRATEGY
Since the Inter-Head architecture differs from the cloud's output layer, parameter sharing is infea- sible. This necessitates training strategy that simultaneously: (1) ensures accurate probability
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interval estimation, and (2) maintains distributional alignment with the cloud model. The nature of this task, which is to simultancously optimize for an optimistic (upper bound) and pessimistic (lower bound) outcome, is conceptually analogous to finding robust solution that performs well even worst-case scenario. This parallel naturally draws inspiration from the principles of Dis- tributionally Robust Optimization (DRO). Therefore, propose the Inter-DRO Loss which can operate on probability intervals, to address this alignment challenge.
Classical Distributionally Robust Optimization. Traditional DRO improves model robustness by optimizing against distribution shifts. Instead of minimizing loss under a single distribution, DRO minimizes the worst-case expected loss over an uncertainty set U of plausible distributions. Formally, the objective for a model with parameters 0 is to solve the minimax problem:
min sup Be~ Ex- PEN
where fe is the model, 2 represents a data sample, and is the loss function.
In practice, this abstract formulation is often operationalized through more concrete settings, One prominent example is Group DRO, where the worst-case distribution selected from a finite set of known data groups = ( m... The objective then simplifies minimizing the maximum expected loss across all groups:
min max Es-,, [C(fo(2))] .EG
(8)
This core principle of optimizing against worst-case scenario forms the conceptual foundation for our Inter-DRO loss, which we will adapt to the unique structure of our probability intervals.
Inter-DRO Loss Our training objective for the Interval Head operationalizes Distributionally Robust Optimization (DRO) principles by treating the predicted lower probability bound q as the worst-case scenario. This interpretation naturally aligns with DRO's minimax philosophy. Unlike standard language modeling heads, the Interval Head outputs valid probability interval parameter- ized by center and radius logits, requiring multi-faceted loss design.
To ensure predictive accuracy and cloud-device alignment, we minimize divergence from cloud outputs Osloud via anchor loss:
Lacchr AulIp Peloud + A, CE(poloddd P)
where CE denotes cross-entropy, softmax(o). and o E fo o²h} for respective bounds.
For the lower bound, which represents the worst-case prediction, enhance the anchoring loss with DRO strategy. We employ an adversarial reweighting scheme where samples in the batch that harder predict (i.e., have higher loss) are given greater weight:
(aCE(p (())
exp
CDRO
max
ww,, CE
(n))
Pio
(n)
Wp
(10)
k= exp (aCE(P(A) Pio (8))
N
where Wp is the weight derived from per-sample risk.
Finally, to enforce strict distributional alignment and ensure the Inter-Head produces accurate logits for direct decoding, we apply a stronger constraint on the interval's center prediction, Pmid- Its loss, fe, includes the anchoring loss plus explicit KL-divergence term match the cloud model's distribution: Distributional alignment enforced through KL divergence regularization:
Calign = R DxL (peloud Pmmia)
(11)
The complete Inter-DRO loss integrates these components:
Linter-DRO =Le Lu + Li
Lanebor (Pmid) + Lalign + Lunchor (Pu)) Canchor (Pw)) (Pio)
Lla
(12)
Center loss
Upper hound
Lower bound DRO
This entire training process is fully compatible with techniques such as Classifier- Free Guidance (CFG), which we employ enhance sample diversity and quality.

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Published as a conference paper at ICLR 2026
Table 1: Main results. Our method significantly reduces latency and cloud requests while maintain- ing comparable CLIP score, FID, Fl, and HPSv2 performance.
Metric
Models
Methods
CLIP (?)
FID (L)
F1(1)
HPSv2(()
Latency(S)
steps
Cloud Call
Base
0.3161
23,6900
0.6097
22.74
x1.00
x1.00
100.00%
Eagle2
0,3115
25.0700
0.6017
22.74
x1.02
x1.16
87.02%
LlamaGen(Stage I)
Lantern
0.3159
25.5494
0.5834
21.29
x1.66
x2.01
50.11%
Entropy-Lens
0.3132
24.5828
0.5796
22.03
x1.70
x2.05
52.34%
CoDe (N 0.3)
0.2827
35.6670
0.4625
18.08
x2.04
x2.734
30.00%
Ours
0.3159
24.2459
0.5997
22.48
x2.53
x3.00
30.44% %
Base
0,2822
40.0709
0.5350
23,84
x1.00
x1.00
100.00%
Fagle2
0.2925
40,9472
0.5334
84.55%
LlamaGen(Stage II)
Lantern
0.2900
41.8766
0,5364
23.06
x1.64
x1.99
50.54%
Entropy-Lens
0.2869
46.7238
0.4713
22.10
x1,91
x2.44
41.29%
CoDe 0.3)
0.2712
45.6822
0.4819
20.68
x1.86
x2.45
30.00%
Ours
0.2927
39.308S
0.5458
23.26
x2.13
x2.83
34.46
Base
0.3215
19.9455
0.6544
23.52
x1.00
x1.00
100.00%
Eagle2
0.3159
23.7103
0.6117
22.88
x1.02
x1.09
91 91.98%
Lantern
0.3181
23.9510
0.5969
22,92
x1.25
x1.81
50.35%
Anole
Entropy-Lenss
0.2966
32.3533
0.5600
x1.57
x2.53
39, 39.86'
CoDe (N 0.3)
0.2781
36.7520
0.5597
21.94
x1.55
x2.89
30.00%
Ours
0.3171
23.8593
0.5970
23.14
x1.87
x3.29
29.88 %
4 EXPERIMENTS
In this section, empirically demonstrate the efficacy of CIAR. We first report the experimental setup. Then we evaluate CIAR with other baselines in perspective image quality and acceleration. Finally, conduct an ablation study clarify the effectiveness of each component in method.
4.1 EXPERIMENTAL SETUP
To validate CIAR, we conduct experiments on the text-conditioned LlamaGen-XI. Stage LlamaGen-XL Stage II, and Anole as the cloud model. On the device side, we adopt the same architecture but restrict it to a single autoregressive layer for efficiency. We use MS-COCO (Lin) al. 2014) validation captions to generate images and compare with ground truth for evaluation.
For baselines, we consider: (i) EAGLE-2 (Li et al. 2024), which achieves state-of-the-as perfor- mance in speculative decoding; (ii) Lantern (Jang et al. (202)), a strong visual speculative decoding method; (iii) Entropy-lens (Ali et al. 2025). which measures token-level uncertainty via entropy of transformer activations; (iv) Softmax Tuu at. (024), which evaluates confidence using maxi- mum softmax probability: (v) Random-based strategy following the baseline setup in STAR (Zhang) ct al. 2024): (vi) CoDe (Chen et al.) 2025), an efficient collaborative acceleration method for VAR. For accelcration, quality under acceleration, we adopt FID, CLIP score, F1 (Heusel et al. 2017] Hessel et al. 2021 and HPSv2 (Wu et al.) . Additionally, quality under acceleration. All model training, inference latency tests, and speedup evaluations were conducted on NVIDIA GeForce RTX 4090 GPU (24GB VRAM).
report speedup, device token acceptance rate, and cloud request rate. For image
conduct quantitative analysis of image generation
4.2
MAIN RESULTS
Table compares CIAR against various baselines. We summarize the key observations as follows: (1) Text SD does not work for visual task. Applying speculative decoding designed for text yields poor results, providing almost no acceleration while noticeably degrading image quality. This is be- cause image tokens exhibit strong distributional consistency, unlike text tokens, which contain richer information diversity; (2) Existing visual acceleration methods remain suboptimal. Lantern man- dates universal cloud validation and consequently incurs high computational costs. Similarly, CoDe suffers from severe distribution shift when adapted to the Next-Token Prediction paradigm because the exclusive reliance on the small model for subsequent sequences compromises generation qual- ity. (3) Entropy-basee method is inadequate for discrete visual tokens. Augmenting Lantern

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Table 2: Performance comparison of different uncertainty method.
Metric
Models
Methods
CL.IP (t)
FTD ())
F1(()
HPSv2(t)
Speedup
Steps
Cloud Call
Base
0.3161
23.6900
0.6097
22.74
x1.00
x1.00
100.00%
Random
0.3142
30.1872
0.5369
18.16
x2.28
x2.36
36.46%
LlamaGenn(ttage I)
Entropy-Lens
0.3132
24.5828
0.5796
22.03
x1.70
x2.05
52.34%
SoftmaxCorr
0.3149
31.1009
0.5130
19.11
x2.27
x2.31
36.49%
Ours
0.3159
24.2459
0.5997
22.48
x2.53
x3.00
30.44% %
Base
0.2822
40.0709
0.5350
23.84
x1.00
x1.00
100.00%
Random
0.2876
44.9589
0.4629
20.76
x2,09
x2,48
34.58%
LlamaGentSlage IT)
Entropy-Lens
0.2869
46.7238
0,4713
22.10
x1.91
x2.44
41.29%
SoftmaxCorr
0.2825
50.0306
0.4030
19,54
x1.91
x2,40
36,30%
Ours
0.2927
39.3085
0,5458
23.26
x2.13
x2.83
34.46 %
Base
0.3215
19.9455
0,6544
23.52
x1.00
x1,00
100.00%
Random
0.2966
52.3533
0.3464
20.55
x1.53
x2,44
39.53%
Anole
Entsopy-Lens
0.2966
32.3533
0.5600
22.34
x1.57
X2,53
39.86%
SoftmaxCon
0,2966
51.0382
0.3447
20.96
x1.48
x2.30
41.04%
Ours
0.3171
23.8593
0.5970
23.14
x1.87
X3.29
29.88%
with standard entropy thresholds degrades performance because scalar entropy is too coarse to cap- ture spatially heterogeneous and semantically critical ambiguities. (4) CIAR achieves a superior efficiency-quality tradeoff. Our method reduces inference latency 3 versus the base model with only 30% cloud request frequency and cuts the computational load of Lantern by 60% while improving CLIPP and FID) scores. These gains arise from the Inter-Head which produces calibrated probability intervals to drive a selective verification policy. This mechanism allows iow-ambiguity regions to be generated locally while precisely offloading nigh-uncertainty tokens for cloud verifi- cation to preserve visual fidelity.
In addition, we conducted visual analysis, as shown in Figure The cases demonstrate that CIAR preserves fine details and maintains overall object coherence while reducing artifacts and distortions. The right side of the figure compares results with and without the Interval-Enhancer module, showing that collaboration further improves detail consistency and object integrity.
4.3 ANALYSIS OF DEVICE- UNCERTAINTY QUANTIHCCATOO
Effect of Uncertainty Mechanism. To evaluate the proposed Inter-Head mechanism, we com- pared it against Random, Entropy-lens, and SoftmaxCori baselines detailed in Table The Ran- dom strategy significantly degrades FID while Entropy-lens fails to capture subtle visual differences among tokens. Similarly, SoftmaxCorr results in sharp quality drop because relying on a single token probability ignores the broader distributional uncertainty. In contrast, our Inter- assesses uncertainty from the entire distribution and achieves a superior balance between cloud verification frequency and image fidelity.
Effeet of Device Model Capacity, We further investigate the impact of device capacity including Inter-Head capacity and device model depth with detailed results provided in A.2.5 and A.2.4 Ex-
periments on Inter-Head capacity using voting (Daliparthi] 2024) and ensemble (Duan et al.] (2025) strategies show that increasing the head count two three improves image quality with negligible computational overhead, while adding further heads yields diminishing returns. Regarding model depth, evaluated device models with varying layer counts. The results indicate that deeper de- vice models enhance image quality and significantly reduce cloud interaction frequency, although this improvement comes at the cost of slight increase in total inference latency.
4.4 ANALYSIS OF
ENHANCED DECODING
Effect of Prefix Rate. We inject cloud-generated prefixes distributional anchors to mitigate local generation drift. Figure indicates that higher prefix rates steadily improve image quality and reduce the frequency of cloud verification requests. However, the inference speedup exhibits non- monotonic behavior because the computational cost of generating initial cloud tokens offsets the benefits of reduced verification at very high rates. A rate of 0.06 strikes optimal balance between
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Speedup
0.3150
34
24.5
3.0
0,3100
2.5
0,3075
Camn
0.3850
13.5
2.0
26
0.3000
Prefix Rate (Thresbold
Profis Rate (Thresheld 0.3)
Figure 3: Comparison of different prefix rate
Bosa
Leusess
Enscogyy
CLAR w/o Sahawo
CLAS
of
Visual analysis of different methods. "CIAR w/o Inter-Enhance" denotes our method without interval-enhancee decoding using interval features.
Figure 4:
guidance and overhead whereas higher rates compromise latency. Visual comparisons in Figure further confirm that our method yields superior consistency and realistic details.
Effect of Uncertainty Threshold. We further analyze the uncertainty threshold with full results in A.2.1 Increasing the threshold enhances speed by accepting more local tokens but risks quality degradation. We identify 0.30 as the optimal operating point which provides a significant speedup over conservative settings with negligible perceptual loss.
4.5 ANALYSIS OF CONTINUOUS-BASRR UNCERTAINTY
Our continuous- based uncertainty estimation circumvents the exponential latency scaling inherent in discrete methods that require solution enumeration. As illustrated in Table and Figure 5 discrete approaches incur prohibitive computational costs as the codebook size i increases while yielding only marginal quality gains. In contrast,
method achieves significantly lower latency than the
discrete counterpart at k = 100 while delivering superior CLIP and FID scores. These results confirm that continuous intervals provide highly efficient estimation mechanism that ensures high- quality generation without the overhead of enumerating feasible solutions. Additional qualitative examples are detailed in A.2.2
CONCLUSION
We introduced CIAR, collaborative cloud-device framework for efficient autoregressive visual generation. At its core, CIAR introduces an interval-based uncertainty quantifier, Inter-Head, to en- able on-device self-verification, reducing cloud computation. To maintain distributional consistency between cloud and device, CIAR leverages Cloud-Enhanced Decoding with interval-feature condi- tioning and prefix guidance, alongside an Inter-DRO loss for output alignment. Experiments demon- strate that CIAR achieves substantial speed-ups while preserving visual quality, enabling practical AR visual model deployment on devices and enhancing real-world multimodal user experiences.

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Published as a conference paper at ICLR 2026
Latency Comparison
Table 3: Performance comparison between Dis-
crete method and Continuous method.
300-
Metric
Methods
Settings
CLIP
FID (L)
= 100
0.3081
26.0371
100-
k 200
0.3096
25.5508
Discrete
k 300
0.3123
24.8237
100
150
200
250
300
350
400
k 400
0.3091
24.9401
values
Continuous
Ours
0,3176
24.2463
Figure 5: Latency comparison between Discrete and Continuous methods.
ACKNOWLEDGMENTS
National Natural Science Foundation of China (No. U24A20326, 62402429, 62441236). Supported by the Key Research and Development Program of Zhejiang Province (No. 2025C01026). Ningho Yongjiang Talent Introduction Programme (2023A-397-G), Young Elite Scientists Sponsorship Pro- gram by CAST (2024QNRC001), The author gratefully acknowledges the support of Zhejiang University Education Foundation Qizhen Scholar Foundation. Sponsored by CIPS-LMG Huawei Innovation Fund.
ETHICS STATEMENT
Our work on CIAR, a collaborative cloud-device framework for efficient autoregressive image gen- eration, adheres to the ICLR Code of Ethics. The primary goal of CIAR is to enhance the reliabil- ity and transparency of visual synthesis systems through interval-based uncertainty quantification, which benefit high-stakes applications such as medical imaging and assistive tools by reducing overconfidence and errors. In this study, no human subjects or animal experimentation was involved. All datasets used were sourced in compliance with relevant usage guidelines, ensuring violation of privacy or security protocols. We have taken proactive measures to avoid biases in model train- ing and evaluation, and no personally identifiable information was processed. While generative technologies like CIAR could potentially be misused for malicious purposes, such as creating deep- fakes, we conducted this research with a commitment to responsible innovation, advocating for the development of safeguards like detection mechanisms. We maintain full transparency and integrity throughout the research process, aligning with ethical Al principles.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we will open-source the implementation of the CIAR method. The complete code, along with detailed documentation, is included in the supplementary materials submitted with this paper. Additionally, upon acceptance, the code will be made publicly available on GitHub to facilitate further research and application.
REFERENCES
Riccardo Ali, Francesco Caso, Christopher Irwin, and Pietro Liò. Entropy-lens: The information signature of transformer computations. arXiv preprint arXiv:2502. 16570, 2025.
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. Scheduled sampling for sequence prediction with recurrent neural networks. Advances in neural information processing systems, 28, 2015.
Daniel Bolya, Cheng Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. Token merging: Your vit but faster. arXiv preprint arXiv:2210.0946). 2022.
10

---

Published as a conference paper at ICLR 2026
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot leamerr. Advances in neural information processing systems, 33:1877-1901, 2020.
Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, and Tri Dao. Medusa: Simple framework for accelerating Ilm generation with multiple decoding heads, 2023.
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. Maskgit: Masked generative image transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1 11315-11355 2022.
Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean- Saan-Baptiste Lespiau, Laurent Sifre, and John Jumper. Accelerating large language model decoding with speculative sampling. arXiv preprint arXiv:2302.013 18, 2023.
Zigeng Chen, Xinyin Ma, Gongfan Fang, and Xinchao Wang. Collaborative decoding makes visual rego-segressive modeling efficient. In Proceedings of the Computer Vision and Pattern Recogni- fion Conference, pp. 23334-23344, 2025.
Ethan Chern, Jiadi Su, Yan Ma, and Pengfei Liu. Anole: An open, autoregressive, native large mul- timodal models for interleaved image-text generation. arXiv preprint arXiv:2407.06135 2024.
Ryan Dahl, Mohammad Norouzi, and Jonathon Shlens. Pixel recursive super resolution. In Pro- ceedings of the IEEE international conference on computer vision, pp. 5439-5448, 2017.
Venkata Satya Sai Ajay Daliparthi. Andhra bandersnatch: Training neural networks predict par-
allel realities. arXiv preprini 1..e22411.19911 2024.
Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, and Guihai Chen. De- cel: Device-cloud collaborative controlled learning for large vision models. arXiv preprint arXiv:2303. 2023.
Keyu Duan, Zichen Liu, Xin Mao, Tianyu Pang, Changyu Chen, Qiguang Chen, Michael Qizhe Shich, and Longxu Dou. Efficient process reward model training via active learning. arXiv preprint Stt::2504.105555 2025.
Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference computer vision and puttern recogni- tion, pp. 12873- 12883, 2021.
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning, arXiv preprint arXiv:2104.08718, 2021.
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge local nash equilibrium. Advances in neural information processing systems, 30, 2017.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851. 2020.
Doohyuk Jang, Sihwan Park, June Yong Yang, Yeonsung Jung, Jihun Yun, Souvik Kundu, Sung- Yub Kim, and Eunho Yang. Lantern: Accelerating visual autoregressive models with relaxed speculative decoding. arXiv preprint arXiv:2410.03355, 2024.
Yiping Kang, Johann Hauswald, Cao Gao, Austin Rovinski, Trevor Mudge, Jason Mars, and Lingjia Tang. Neurosurgeon: Collaborative intelligence between the cloud and mobile edge. ACM SIGARCII Computer Architecture News, 45(1)661 2017.
Alex Kendall and Yarin Gal. What uncertainties do we need in bayesian deep learning for computer vision? Advances in newral information processing systems, 30, 2017.
Stefanos Laskaridis, Stylianos Venieris, Mario Almeida, Ilias Leontiadis, and Nicholas D Lane. Spinn: Synergistic progressive inference of neural networks over device and cloud. In Proceed- ings of the 26th annual international conference on mobile computing and networking, pp. 1-15, 2020.
11

---

Published as a conference paper at ICLR 2026
Hongshan Li, Chenghao Hu, Jingyan Jiang, Zhi Wang, Yonggang Wen, and Wenwu Zhu. Jalad: Joint accuracy-and latency-aware deep structure decoupling for edge-cloud execution. In 2018 IEEE 24th international conference on parallel and distributed systems (ICPADS), pp. 671-678,
IEEE, 2018.
Yuhui Li, Fangyun Wei, Chao Zhang, and Hongyang Zhang. Eagle-2: Faster inference of language models with dynamic draft trees. arXiv preprint arXiv:2406. 16858, 2024.
Tsung- Yi Lin, Michael Maire, Serge Belongie, James Hays, Pictro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pp. 740-755. Springer, 2014.
Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Alvin Cheung, Zhijie Deng, Ion Stoica, and Hao Zhang. Online speculative decoding. arXiv preprint arXiv:2310.0717. 2023.
Zheqi Lv, Wenqiao Zhang, Shengyu Zhang, Kun Kuang. Feng Wang. Yongwei Wang, Zhengyu Chen, Tao Shen, Hongxia Yang. Beng Chin Ooi, et al. Duet: A tuming-fire fieeng-free device-cloud collabo- rative parameters generation framework for efficient device model generalization. In Proceedings of the ACM Web Conference 2023, pp. 3077-3085, 2023a.
Zheqi Lv, Wenqiao Zhang, Shengyu Zhang, Kun Kuang. Feng Wang, Yongwei Wang, Zhengyu Chen, Tao Shen, Hongxia Yang, Beng Chin Ooi, et al. Duet: A tuning-free device-cloud collabo- rative parameters generation framework for efficient device model generalization. In Proceedings of the ACM Web Conference 2023, pp. 3077-3085, 2023b.
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication--effcieee learning of deep networks from decentralized data. In Artificial intelli- gence and statistics, pp. 1273-1282. PMLR, 2017.
Alexander Nikitin, Jannik Kossen, Yarin Gal, and Pekka Marttinen. Kernel language entropy: Fine- grained uncertainty quantification for lims from semantic similarities. Advances in Neural Infor- mation Processing Systems, 37:8901-8929, 2024.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Nore Stolte, János Daru, Harald Forbert, Dominik Marx, and Jorg Behler. Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water. Journal of Chemical Theory and Computation, 21(2):886-89, 2025.
Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06555, 2024.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprini 13977,,
Weijie Tu, Weijian Deng, Liang Zheng, and Tom Gedeon. What does softmax probability tell us about classifiers ranking across diverse test conditions? arXiv preprint arXiv:2406.09908 2024.
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks.
In International conference on machine learning, pp. 1747-1756. PMLR, 2016.
Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017.
Guanqun Wang, Jiaming Liu, Chenxuan Li, Yuan Zhang, Junpeng Ma, Xinyu Wei, Kevin Zhang, Maurice Chong, Renrui Zhang, Yijiang Liu, al. Cloud-device collaborative learning for multi- modal large language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 26646-1255. 2024a.
12

---

Published as a conference paper at ICLR 2026
Zili Wang, Robert Zhang, Kun Ding, Qi Yang, Fei Li, and Shiming Xiang. Continuous speculative decoding for autoregressive image generation. arXiv preprint arXiv:2411.11923 2024b.
Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to- image synthesis. arXiv preprint arXiv:2306.099: 09341, 2023.
Jiangchao Yao, Feng Wang, Kunyang Jia, Bo Han, Jingren Zhou, and Hongxia Yang. Device-cloud collaborative learning for recommendation. In Proceedings of the 27th ACM SIGKDD Conference Knowledge Discovery & Data Mining, pp. 3865-3874, 2021.
Guangyi Zhang, Yunlong Cai, Guanding Yu, Petar Popovski, and Osvaldo Simeone. Quantize- cumple-and-verffy. Llm acceleration via adaptive edge-cloud speculative decoding. arXiv preprint wixv:2507.00005, 2025.
Jun Zhang, Jue Wang. Huan Li, Lidan Shou, Ke Chen, Gang Chen, and Sharad Mehrotra. Draft & verify: Lossless large language model acceleration via self-speculative decoding, arXiv preprint 0xXiv:2309,08168, 2023.
I Linhai Zhang, Jialong Wu, Deyu Zhou, and Guoqiang Xu. Star: Constraint lora with dynamic active learning for data-efficient fine-tuning of large language models. arXiv preprint arXiv:2403.01165, 2024.
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A APPENDIX
A.1 PSECDO CODE
Algorithm CIAR: Collaborative Interval- -ased AutoRegressive Decoding
Input:
Meloud: Cloud autoregressive model
Maevice: Device autoregressive model
Hinter: Interval-Head module
D: Prefix rate
T: Uncertainty threshold
L: Total sequence length
K: Device sequence length
prompt: Input conditioning
Output: Generated token sequence Xo.E-1
1: Initialize: / 4- 0, < 0, prefix <- 0
2: m t 1p- L]
3: while < m do
>> Compute prefix length
4: Ft + Matond (prompt,, 10:1-1)
D Auto-regressive generation
5: x x-xU (z:), prefix +- prefix U (x)}
6: 1+1+1
7:
end while
8:
while L do
> Device-side generation with verification Device prediction Interval center/radius
9:
h4 - Maevice (Beviccepreeix... (prefix, Xmmt-1)
10:
C₁, r + Inter-Head
11:
P. InterFuse(c,,rr)
12:
Ui + u(P:)
b
Eq
uf S 7 then
14:
Accept It.
P On-device Self-verification
t-t+1
16:
else
buffer farl,tt
18:
while ls < K Al <kAltk<> <
Se+R, P.+k + Maevice (1)+++111 buffer buffer U (fe+kr Petk) + ++1
20:
21:
22:
end while
23:
f + Φ (Concat (buffer))
b Eq
24:
Xverified & Metoud (Xo:t-tXo:t--
Cloud verification
25:
x f U Xverified
26:
ssample Meloud (x, (2) t + Xverined +
27:
end if
29:
end while
return X0:L-1
Algorithm fillustrates the complete inference process of the CIAR framework. Initially, the cloud model Metoud accepts an input prompt and generates a prefix sequence of image tokens. This prefix is then transferred to the device model Máeviee which performs autoregressive generation. Each token produced by the device model processed by the Inter-Head module to compute its uncertainty score. If the score surpasses threshold the system collects subsequent sequence of K tokens and forwards them to the cloud for verification and resampling. During this step, the interval features derived from the tokens are also transmitted the cloud as supplementary information. The autoregressive generation terminates once the total token sequence length reaches L, which point image decoding begins.
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A.2 IN-DEPTH ANALYSIS
A.2.1 ANALYSIS OF DIFFERENT THRESHOLDS
We investigate the impact of the uncertainty threshold on generation quality and efficiency with fixed prefix rate of 0.06. Figure [ presents the corresponding CLIP, FID, speedup, and cloud call rate. Increasing the threshold allows more tokens to be accepted locally, which reduces cloud requests and improves inference speed. At threshold of 0.30, the method achieves a CLIP score of 0.3159 and an FID of 24.2459, representing negligible perceptual differences compared to the more conservative threshold of 0.25, which yields 0.3 and 23.2130. However, this slight adjustment brings a meaningful 15% relative speedup from 2.20x to 2.53x and lowers the cloud call rate from 30.98% to 30.44%. Further increasing the threshold beyond 0.30 leads to greater acceleration but significant degradation in visual quality. At 0.35, CLIP drops to 0.3108 and FID rises to 27.1990 0.40, CLIP falls to 0.3048 and FID worsens to 28.5853. These results show that blindly raising the threshold sacrifices image quality for speed. We select 0.30 as it offers favorable balance between acceleration and perceptual quality.
Threshold vs CLIP & FID
Threshold vs. Speedup & Cloud Call
0.320
30
3.5
(LIPPI)
Speutipp cihavillas, Vigher faster)
1BD4)
Cloud Call
2.5
(1I
0.310
76
2.0
35
4.5
0.310
0.25
OursB.J)
6,15
8.4
12
0.5
0.25
Oungk.1
8.35
0.4
Threhell Rate (L06)
Threheli (Prefis (L06)
Figure 6: Comparison of different threshold
A.2.2 DISCRETE vs. CONTINUOUS
x= 100
k- 800
x- 300
k- 400
Figure 7: Visual analysis of Discrete metric and Continuous metric
Figure presents image samples generated by the discrete uncertainty quantification method under varying k values. We evaluated four monotonically increasing k values and conducted compara- tive analysis with the CIAR method using identical prompts and random seeds. The results indicate that at k=100, while latency is reduced, the limited token count for uncertainty computation leads to imprecise uncertainty estimates, resulting in the lowest image quality. As increases, image de- tails become more refined, yet significant distortions and artifacts remain apparent. In comparison,
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Table 4: Impact of different N values on CoDe and comparison with CIAR.
Metric
Models
Method
Param,
CLIP (1)
FID (L)
F1(()
HPSvv(1)
Latency(s)
Cloud Call
Base
0,3161
23.6900
0.6097
22.74
x1.00
100.00%
0.10
0.2566
44.7305
0.4028
15.84
x2.71
10.00%
0.15
0.2620
42,6082
0.4088
16.36
x2.41
15.00%
LlamaGen(Stage I)
CoDe
0.20
0.2627
40.3943
0.4508
16.83
x2.14
20.00%
0.25
0.2783
38.3103
0.4639
17.43
x2.13
25.00%
0.30
0.2827
35.6670
0.4625
18.08
x2.04
30.00%
Ours
0.3159
24.2459
0.5997
22.48
X2.53
30.44%
Table 5: Performance comparison of device models with varying depths.
Metrie
Models
#laayers
CLIP (1)
FID (U)
F1(())
HPSv2(()
Latency(s)
Steps
Cloud Call
Layer
0.3159
24.2459
0.5997
22.48
x2.53
x3.00
30.44%
2 Layers
0,3113
23.8820
0.6270
22.44
x2.54
x3.44
27. 76.06%
LlamaGen(Stage
Layers
0.3171
23.8404
0.5899
22.44
x2.19
x3.58
26. 10%
5 Layers
0.3171
23.8232
0.6180
22.45
x2.14
x3.66
25.68%
the CIAR method achieves markedly superior image quality, with more accurate rendering of ob- ject details. This outcome underscores the ability of CIAR to effectively minimize latency while maintaining precise uncertainty quantification, thereby ensuring high image fidelity.
A.2.3
COMPARISON WITH CODE IN NTP PARADIGM
CoDe (Chen et al. 2025) is collaborative acceleration framework originally designed for the Next- Scale-Prediction (NSP) paradigm, assigning semantic-heavy early scales to the large model and subsequent scales to smaller model. To adapt CoDe for LlamaGen (which follows the Next- NTP paradigm), we define N as the ratio the sequence predicted by the large cloud model. Table presents the impact of N on performance.
Analysis. While CoDe provides acceleration, consistently underperforms CIAR in generation quality. This degradation stems from the fundamental difference between the paradigms: NSP pre- dicts holistic image features where early scales provide a robust global context, whereas NTP relies strict token-wise autoregression. When the device model (small model) predicts long consecutive sequences (low N), the lack of high-capacity correction leads to severe distribution shift and error accumulation. Increasing N mitigates quality loss by involving the cloud model frequently but significantly compromises the acceleration gain. Consequently, CoDe is suboptimal for NTP-based autoregressive models compared CIAR's dynamic, uncertainty- aware allocation.
A.2.4 IMPACT OF DEVICE MODEL LAYERS
We investigate the trade-off between the device model's capacity and overall efficiency by varying the device model depth (2, and 5 layers). Results summarized in Table
Analysis. Increasing the network depth significantly enhances the device model's representational capability, resulting in improved FID scores. Crucially, stronger device model generates tokens with higher confidence, thereby reducing the frequency of cloud model intervention (lower cloud call rate). However, this comes at the cost of increased computational latency per step on the device. The results suggest that slightly increasing the device model depth can be viable strategy to balance generation quality and acceleration, depending on the specific hardware constraints.
A.2.5 IMPACT OF HEAD CAPACITY
The Inter-Head is pivotal for uncertainty estimation. We examine the impact of its capacity by train- ing 5 distinct Inter- Heads under different random seeds and aggregating them using strategies:
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Table 6: Impact of Inter-Head capacity with Ensemble and Voting aggregation mechanisms.
Metric
Method
#Head
CLIP (1)
FID (1)
FI(1)
HPSv2(t)
Latency(s)
Steps
Cloud Call
Head
0.3159
24.2459
0.5997
22.48
x2.53
x3.00
30.44%
Heads
0.3157
24.1517
0.5878
22.38
x2.46
x2.98
29.45%
Ensemble
3 Heads
0.3151
24.1301
0.6099
22.30
x2.45
x2.97
28.86%
4 Heads
0.3137
24.6195
0.5999
22.26
x2.49
x2.97
28.73%
5 Heads
0.3147
24.6723
0.5988
22.26
x2.50
x3.00
28.38%
Head
0.3159
24.2459
0.5997
22.48
X2.53
x3.00
30.44%
2 Heads
0.3167
24.0400
0.6095
22.34
x2.29
x2.98
29.38%
Voting
3 Heads
0.3165
24.1466
0.6195
22.25
x2,48
x3.00
28.97%
Heads
0.3169
24.3542
0.5678
22.25
X2.53
X2.98
28.56%
5 Heads
0.3135
24.3353
0.5910
22.22
x2.48
x2.98
28.54%
Table 7: Ablation study on the Inter-DRO loss.
Metric
Models
Loss
CLIP (1)
FID (U)
F1(f)
HPSv2(
Latency(s)
Steps
Cloud Call
w/o Le
0.3149
25.2721
0.5889
21.92
x2.37
x2.98
28.05%
w/o Lu
0.2891
47.8981
0.4110
x2.39
x2.99
32.17%
LlamaGen(Stage I)
w/o L1
0.2945
25.2849
0.5889
22.45
x2.37
x2.98
28.05%
w/o Lanchor
0.2168
68.6400
0.4114
15.78
x2.30
x2.85
33.79%
Ours
0.3159
24.2459
0.5997
22.48
x2.53
x3.00
30.44%
Voting (Daliparthi, 2024): A hard-voting mechanism where the final output is determined by the majority class prediction of the heads, prioritizing robustness.
Ensemble (Duan et al. 2025 A soft-voting mechanism that averages the probability distributions from multiple heads to obtain calibrated score.
Analysis. As shown in T increasing the number of heads has a negligible impact on latency due to the lightweight nature the Inter-Head. Performance in terms of FID peaks when using 2 or 3 heads. However, increasing the count to 5 leads to a degradation in CLIP, FID, and HPSv2 scores across both strategies. This indicates that excessive capacity the uncertainty estimation module does not yield better calibration and may lead to overfitting or noise accumulation.
A.2.6 IMPACT OF THE INTER- INTER-DRO LOSS
We conduct comprehensive ablation study to isolate the contributions of each Inter-DRO compo- nent, specifically excluding the anchor loss to verify its role in device-cloud alignment. As detailed in Table the anchor loss Lauchor proves fundamental for guarantecing the classification accuracy of the Inter-Head, and its removal leads to poor training convergence alongside marked degradation in image quality. Regarding the core Inter-DRO terms, the center loss L. aligns the device-cloud distributions via KI. divergence, aiming for soft probability matching rather than the hard accuracy enforcement of the anchor loss. Simultaneously. the lower-bound loss Li facilitates the detection of uncertain tokens. Ablating either L. or Li results in minor decline collaborative performance. In contrast, the upper-ber bound loss Cu is critical for the precise selection of target tokens, evidenced by the significant quality drop upon removal. Collectively. these components empower the Inter- Head to accurately discern valid tokens and robustly evaluate uncertainty.
A.2.7 SIMULATION OF COMMUNICATION COSTS
Due to physical resource constraints, we simulate the cloud-device collaboration environment fol- lowing standard protocols in cloud-device collaboration works (Kang et al. 2017 Laskaridis al.
2020 Li et al.) 2018 Lv et al. 2023a). We evaluate the inference performance across 5G, 4G, and WiFi network conditions.
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Table 8: Comparison of communication costs between Lantern and CIAR under different network conditions. Device and Cloud denote the computational latency on the respective endpoints, while Communication represents the data transmission overhead.
Network
Method
Device (ms)
Cloud (ms)
Communication (ms)
Total (ms)
Ratio (Comm/Too
Lantern
11.63
11586.29
4424.63
16022.54
0.2762
5G
Ours
13.54
5102.6
1678.13
6794.27
0.247
Lantern
11.9
11577.91
22291.31
33881.12
0.6579
4G
Ours
14.1
4847.2
8173.95
13035.26
0.6271
Lantern
11.69
11436.11
8768.01
20215.81
0.4337
WiFi
Ours
14.11
5052.52
3343,94
8410.58
0.3976
Simulation Metric. The total latency Tittl incorporates both communication and computational overhead. The communication latency Teamm for single transmission event is calculated as:
Data.Size
Toomm L
(13)
where denotes the network lateney (RTT), B represents the bandwidth, and DalaSize refers to the transmission payload size. Consequently, the total inference latency is formulated the sum of bidirectional communication and computation costs:
Total Tellud + T + Teloud + Taevice
(14)
Here, Teloud and Tdevige represent the computational latency on the cloud and device, respectively, while Telmut and Telvice denote the downlink (cloud-to-dess to-vvicc)) and uplink (device-to-cloud) trans- mission latencies. Specific network configurations are detailed in Table
Analysis. As shown in Table 8 CIAR demon- strates superior bandwidth efficiency compared to the baseline Lantern method. By accurately iden- tifying trustworthy tokens locally, CIAR minimizes the transmission payload, resulting in significantly lower Communication-to--Toie Latency ratio (e.g., 0.247 vs. 0.276 under 5G). This selective transmis- sion strategy effectively offloads computational bur- dens from the cloud while mitigating network bot-
Table 9: Detailed configurations of different network conditions.
Network Bandwidth (Mbps)
Latency (ms)
5C
300.00
10.00
4G
20.00
50.00
WiFi
100.00
20.00
tlenecks.
A.2.8 GENERALIZATION TO DIFFERENT ARCHITECTURES
To evaluate the architectural universality CIAR, we extend our experiments to two distinct gen- erative frameworks I VAR (based Next-Scale Prediction) and DiT (a Diffusion- based model).
Settings. For the VAR adaptation, utilize single autoregressive layer to predict the scale, equipped with a fine-tuned Inter-Head. For DiT, we adapt a single DiT block equipped with an Inter-Head predict noise and variance for the subsequent timestep.
Analysis. In the VAR context, CIAR successfully accelerates inference while preserving image fidelity. However, due the inherently shorter sequence lengths in the NSP paradigm, the relative overhead of cloud verification becomes more sigmificant, resulting in a slightly reduced acceleration gain compared to CoDe in this specific setting. Conversely, for diffusion models like DiT, CIAR demonstrates remarkable efficiency, achieving IS and FID scores comparable to 25-step DDIM sampler but with 2.5x speedup. This underscores the potential CIAR accelerating iterative refinement processes beyond standard autoregression.
A.3 ANALYSTS OF THE UNCERTAINTY QUANTIFICATION
In this section we analyze, refine, and justify the uncertainty score given in Eq. equation 4 We denote by n. [V] the vocabulary size, and for brevity drop the time index when the derivation is
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Published as a conference paper at ICLR 2026
Table 10: Performance of CIAR applied to VAR and DiT architectures on ImageNet-IK
Model
Method
ISt
FID
Precision
Recallt
Latency
DDIM-50 steps
241
2,27
x1.00
DDIM-25 steps
232
3.18
x2.00
DIT
DDIM-20 steps
221
3.81
x2.50
DDIM-12 steps
184
7.80
x4.17
Ours
240
2.32
VAR-d24
311
2.11
0.82
0.59
x1.00
VAR-d20
301
2.61
0.83
0,56
x1.65
VAR
CoDe
297
0.80
0.60
x2.90
Ours
290
2.34
0.81
0.60
x2.60
pointwise for a single autoregressive step. Thus
ô (ò),.... kn)
8 = Pi
(15)
Ω
m
Ω =
5;
(6 - 8)2,
(16)
n
11
and the uncertainty score
U(P) E.
(17)
A.3.1 PRINCIPLES: STATEMENT, CHECKING AND MINOR REFINEMENT
First, based on the characteristics of visual autoregressive generation, we propose three qualitative principles, followed by concise mathematical clarification.
Principle 1 (Total Ambiguity). space.
Uncertainty increases with the total volume of the probability
Edd- -a) = ut
(18)
Clarification. The total interval volume is measured by Ω = E, 8 Intuitively upper-bounds the L1-diameter of the feasible probability polytope (see Proposition below), hence it is a natural first-order measure of how much the predicted distribution may vary.
Principle 2 (Confidence Disparity). Uncertainty is also high when confidence varies greatly across tokens (some intervals wide, others narrow). Uniformly narrow intervals imply low uncer- tainty.
Var - - u1
(19)
Clarification. The standard deviation Σ quantifies the spread (anisotropy) of allowable perturbations across coordinates. large Σ indicates the ambiguity concentrated in a subset of tokens (direc- tional uncertainty), whereas Σ = 0 indicates perfectly uniform widths 6; for all i. We emphasize an important design decision: the proposed metric intentionally combines total volume and disparity that the score is elevated only when both the total freedom and its anisotropy are significant. This is consistent with downstream needs in autoregressive visual synthesis, where one often wants detect "actionable" uncertainty (large and structured ambiguity) rather than ubiquitous, perfectly symmetric fuzziness across the whole vocabulary.
Principle (Local Certainty). If probability mass is concentrated on a single token (an interval vanishes and its upper bound approaches 1), uncertainty diminishes.
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Published as a conference paper at ICLR 2026
#i: - of +1 u4
(20)
Clarification. If token i attains (approximately) all probability mass in both lower and upper bounds (i.e., p = p.¹ = 1), then necessarily the remaining tokens' feasible probability mass be- comes (nearly) zero. Under this natural consistency regime the widths 6j for * i must be small and thus Q and are small, yielding small U. We make precise statements this type below (Proposition 2)
A.3.2 FROM PRINCIPLES TO THE PRODUCT FORM
We now justify the particular algebraic choice 4 = Ω2 E.
Axiomatic Desiderata. simple set of desiderata for sealar function f()) capturing the follow- ing principles:
1. f(0) 2 0 for all
2. f(6) Of 0 (no ambiguity) if the widths are perfectly uniform (E 0), reflecting the design decision that perfectly symmetric ambiguity is not considered "high priority" uncertainty for autoregressive decision-making
3. f (a.5) should increase with scalar (monotonicity in total scale);
4. f should be sensitive to both scale (S2) and dispersion (E) and be simple to compute and interpret.
These requirements are satisfied by the multiplicative ansatz f()) ((2) h(E) with monotone g, and the simplest choice g(2) = Ω, h(E) = Σ. The product has the desirable property that vanishes if either factor is zero and scales quadratically under uniform scaling ā ~ ao, which matches the intuition that both a large feasible set and substantial anisotropy are needed to produce high-priority uncertainty signal.
A.3.3 MATHEMATICAL PROPERTIES AND PROOFS
We now give several formal properties of
Claim (Non-negativity and Zeros). U(P) 2 Moreover U(P) 0 if and only if or Σ = 0 (i.., 5 is the all-zero vector or d is constant across coordinates).
Proof. Immediate from definitions: Ω 2 Σ 2 0, hence = QE 2 0. = 0 then 6 = 0 and Σ = 0 and 2/ 0. If Σ 0 then all ó; and 2 Ω 0 0. Conversely, ? 0 implies either
= 0 or Σ 0.
Claim (Scaling). For any scalar a 2 0, ((())))))))))))))))())) (8))
Proof. If 8' ad then D all and '' aE; multiply obtain U/ a²u.
Claim 3 (Upper Bound in Terms of Q). For any 2 O.
u The upper bound is tight: equality is atlained when one coordinate equals Q and the remaining n coordinates are zero.
E<
Ω and hence
(21)
n
Proof. For fixed Ω the quantity E, 57 is maximized by concentrating all mass on single coordinate (this is the well-known extremal property of f2 under fixed f1). Concretely, with & 9/nn.
22
Ω2 (n 1).
82
Ω
n
n
n²
n
Taking square-roots yields the bound on Σ: multiplying by Ω gives the stated bound on U. The one-hot vector (2, 0g...,0. 0) attains the bound.
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Published as a conference paper at ICLR 2026
Claim 4 (Upper Bound in Terms of Σ 6,). 1 Let ==
== Ei= 83.
Then
U S S2
(22)
2
This bound is tight for suitable two-point configuration.
Proof. Write μr 3. Then S =n>²² + 1²²). Using U = nyu.. and treating S2 as fixed, maximize g(p) == / S2/n - n.² over E [0, / S2/nn. Standard calculus yields the maximizer ja (2n) and the maximum value Jrux S2/2.
Proposition 1 (Feasible Set Diameter). Let P == {p R": : feasible polytope of true discrete distributions consistent with the interval bounds. Then for any P,9 E P.
E,Ps 1} be the
1p 1112 S S2
(23)
Consequently Qt upper-bounds the L, Li-dmeeeeer of P.
Proof. For each coordinate we have Ip: Summing yields |pp 4|l1 = Σ, p: q: Σ, di = Ω2
qil S because both pa, qi lie in the interval [pf p.
Proposition 2 (Local Certainty (A Sufficient Condition)). Suppose there exists an index is such that pf + and pk 1. Then & - and hence U 0. More generally, if + 1 and Σ) x Pj 0, then Q + O.
Proof. If p/ + then, because E, pf S 1, necessarily p + 0 for all j * k. If also pk - 1 and
Ejtt P3 + 0, then for j * & both P/ - O and p - 0. Hence for all j F k. 6j = Pp p3 + 0, and 65 px - ph. - by the hypothesis ph. pl
Therefore 12 Σ. 8 + 0. and since Σ is
bounded by Ω up to constants, 4 = QE - O.
A.3.4 STATISTICAL / GEOMETRIC INTERPRETATION
The feasible set P is the intersection of the probability simplex with the axis-aligned hyperrectangle I1 p... Two geometric consequences follow.
Diameter control. By Proposition [ Ω upper-bounds the maximal L₁ distance between feasible distributions. Thus Ω controls the worst-case change in any linear functional p over PP for any R",
max la a Tgl S ||l]| max Ip all, S ||al
(24)
P.PEP
P-q
In particular, the range of any coordinate p: is bounded by 6, and the range of expectations of bounded functions is controlled by Ω2
Anisotropy and actionability. Σ quantifies the anisotropy of the hyperrectangle {ō:} } (it is zero for an isotropic rectangle 5, = const). High anisotropy (large E) implies that the fea- sible polytope allows comparatively large perturbations along a small set of coordinates this precisely the case where one can expect actionable model decisions (e.g. choose to re-query or defer) centred on particular tokens.
The chosen product L = 22 therefore measures (i) how large the feasible set can (through ?) and (ii) how anisotropic/localized that freedom is (through Σ). Both ingredients are important for an uncertainty signal that feeds a selective acceptance/defeeral policy in autoregressive synthesis.
A.3.5 FURTHER ALGEBRAIC REWRITES AND INTUITION
Let μ 5 = 12/n and define the coefficient of variation
CV = 4
(25)
whenever / > 0. Then
u = npw = nup2 CV.
(26)
This decomposition clarifies trade-offs:
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If the average width is tiny, 24 is small even when relative dispersion CV is large.
If CV is tiny (nearly uniform widths), U is small regardless of j..
If both μ and CV are moderate-to-large, grows quickly (quadratically in the uniform- scaling sense).
A.3.6 PRACTICAL REMARKS FOR THRESHOLDING AND NORMALIZATION
1. Threshold selection. Because U scales quadratically under å + aδ, raw thresholds should account for the scale of typical interval widths. In practice one can either (i) normalize by n or by Ω to obtain a dimensionless metric, or (ūi calibrate the threshold on a held-out validation set of generation situations.
2. Behavior for perfectly uniform large widths. As designed, U vanishes when widths perfectly uniform. an application requires a non-zero response for the case "all tokens are equally very ambiguous", replace the multiplicative factor by vΣ² + or use combined additive form St + d with small X1 0. This is deliberate modeling choice; the product form emphasizes structured (non- mnn-issorropc) ambiguity.
3.
Computational cost. Ω and are (n) to compute and therefore inexpensive even for large vocabularies.
A.3.7 SUMMARY
The proposed metric (PP) = ΩE (i) nonnegative and simple to compute, (ii) vanishes in the natural limiting regimes (no ambiguity, perfectly uniform ambiguity), (iii) scales quadratically under uniform dilation of widths, (iv) admits tight bounds in terms of Ω and o and (v) admits clear geometric/probabliittic interpretation via the feasible polytope P. Together these properties rigorously support the design principles and show why ti is an effective, low-cost scalar proxy for actionable uncertainty in autoregressive visual synthesis.
A.4 PROOF OF INTERVAL VALIDITY FOR INTER-HEAD OUTPUT
The Inter-Head module outputs center vector C, and radius vector r, for the hidden state h where r, is non-negative due to the Softplus function. The logit interval for each token is defined [7] [7] = [a- Tú, G + ra]. To convert these logit intervals into probability intervals P. [p's p²), we employ the InterFuse operator, which ensures that the resulting probability intervals satisfy the validity condition r / < < P.
The InterFuse operator computes the lower and upper probability bounds for each token as fol-
lows:
exp(c. r.)
pk Σ, exp(e;) - exp(es) (1)))) + exple -ri) ri))
(27)
exp(e r:)
p E, exp((c)) -xp(c;) + exp(c: + +T,)
(28)
These expressions ensure that 0 < pl < p. < 1 for each token i, as the denominators are constructed to be greater than or equal to the numerators. However, the sums S = Σ, pl and S. = -,, P. may not strictly satisfy S < S., due to the nonlinear interactions between the terms.
To guarantee the interval condition, we introduce a normalization step. Let S and S, be the sums after the initial computation. We scale the probabilities as:
0.99
P t p/ min
1,
(29)
S
1.01
pdd + p max
1,
(30)
S,
This scaling ensures that Σ, p. interval requirement. The constants 0.99 and 1.01 provide margin for numerical stability and are chosen empirically.
0.99 < and
P. 2 1.01 2 1. thus strictly satisfying the
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Published as a conference paper at ICLR 2026
In the code implementation, further enhance numerical stability by using the max-trick (subtract- ing the maximum value of C, before exponentiation) and clamping the radius to a maximum value. The final algorithm ensures that the probability intervals are valid and suitable for downstream tasks such as uncertainty quantification and collaborative decoding.
A.5 USE OF LARGE LANGUAGE MODELS (LLMS)
During the initial exploratory phase of this research, we employed large language models solely to assist in retrieving relevant literature autoregressive image generation. It important to note that the LLM was not involved in the ideation, writing, or experimental design. All research concepts, ideas, and analyses were developed and conducted by the authors.
23


Reviews:
[{"review_id": "pMtflYIptc", "reviewer": "Reviewer_vW4B", "summary": "The paper proposes CIAR, a cloud-device collaborative interval-based decoding framework designed to accelerate image generation with auto-regressive (AR) models. The method introduces an interval-based uncertainty quantifier (“Inter-Head”) for the device model to filter highly confident tokens, reducing the need for cloud verification. CIAR incorporates a continuous interval-based uncertainty measure, cloud-enhanced decoding with interval-feature conditioning, and an interval-aware DRO loss for distribution alignment between device and cloud outputs. Experimental results on text-to-image tasks demonstrate that CIAR substantially reduces latency and the number of cloud requests while maintaining comparable image quality to state-of-the-art speculative decoding baselines.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The paper clearly identifies bottlenecks in AR-based image generation for on-device deployment, specifically the challenge of excessive cloud requests and inefficiency of uniform token verification, and motivates a focused solution.\n\nThe introduction of interval-based uncertainty (via the “Inter-Head”) in the device model is a creative step toward efficiently identifying tokens that can be safely accepted on-device, addressing the challenge posed by large visual vocabularies and spatial redundancy.\n\nThe paper goes a long way in justifying and formalizing the proposed continuous interval-based uncertainty metric, providing detailed theoretical rationale, axiomatic desiderata, and mathematical properties (see Appendix A.3). This is elaborated with specific attention to the geometric/statistical meaning of the measure, which demonstrates a serious attempt at rigor (even if some aspects could benefit from deeper empirical probing).", "weaknesses": "Despite the strong baseline coverage, the paper omits a direct comparison and discussion of several directly relevant. In particular, work such as “Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient” (Chen et al., 2025) is highly relevant and should be both cited and empirically compared (e.g., as a baseline in Tables 1 and 2, and in distributional alignment discussions). The failure to engage with such work weakens the claim of CIAR’s advancement.\n\nWhile the role of prefix rate and threshold is explored (Figure 3, Figure 6), there is little sensitivity analysis on device model depth, Inter-Head capacity, or how interval regularization influences the tradeoff between speed and quality. For instance, it is unclear how the single-layer device model limits or shapes the uncertainty estimation capabilities—could more layers or changes to Inter-Head’s complexity yield diminishing returns or instability?\n\nWhile the Inter-DRO loss is well-motivated mathematically, the practical effect of its components is not independently quantified nor are variants (e.g., anchor loss alone vs. full Inter-DRO) ablated. Table 1 does not include or discuss what is lost if alignment is not enforced (e.g., mode collapse, sample drift?), and Figure 4 only vaguely notes “consistency” improvements. A major claim—alignment between cloud and device distributions—lacks direct quantitative substantiation or specific examples measuring divergences before/after.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2vkpLRN6hH", "reviewer": "Reviewer_t33Y", "summary": "This paper proposes CIAR (Collaborative Interval-based AutoRegressive Decoding), a cloud-based collaborative framework for accelerating autoregressive image generation. Traditional visual autoregressive models are slow and difficult to deploy on devices. CIAR introduces an interval-based uncertainty quantization head on the device, replacing discrete predictions with continuous probability intervals, thereby efficiently determining which tokens are acceptable locally and which need to be uploaded to the cloud for verification. Experimental results show that CIAR achieves a significant acceleration and reduction in cloud calls while maintaining image quality, improving the practicality and efficiency of visual autoregressive image generation.", "questions": "Please see the weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. This article is written clearly and logically.\n\n2. This article analyzes the challenges of deploying autoregressive image generation models on devices and proposes an edge-cloud collaborative framework.\n\n3. The proposed uncertainty estimation method is interesting.\n\n4. Comprehensive experiments demonstrate the superiority of the proposed method compared to other acceleration baselines and its advantage over other uncertainty estimation methods.", "weaknesses": "1. The paper lacks details on efficiency measurement. What hardware was used for latency and speedup testing? This seems to be unmentioned in the paper.\n\n2. As an edge-cloud collaborative method, what is the communication cost of CIAR? Are there any latency tests conducted by deploying CIAR in a real edge-cloud collaborative scenario?\n\n3. Can the proposed method also be applied to the VAR model of next-scale prediction?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "N9uHsh8I9j", "reviewer": "Reviewer_QsGz", "summary": "This paper introduces CIAR, a cloud-device collaborative framework designed to accelerate autoregressive (AR) image generation. CIAR addresses the inefficiency of uniform token verification in existing speculative decoding methods by proposing an on-device \"Inter-Head\" module that outputs continuous probability intervals to quantify token uncertainty. This allows the system to selectively offload only high-uncertainty tokens to the cloud for verification, significantly reducing latency and cloud requests while maintaining image quality. The framework is supported by an interval-enhanced decoding module and a distributionally robust training strategy to align device and cloud models. Experiments show that CIAR achieves up to 2.18× speed-up and reduces cloud requests by 70% compared to state-of-the-art baselines.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Practical Impact: The method effectively addresses key bottlenecks in on-device AR image generation (network latency, computational cost) and offers a deployable solution.\n\n2. Comprehensive Evaluation: Extensive experiments across multiple models (LlamaGen, Anole) and metrics (FID, CLIP, speed-up) convincingly demonstrate the advantages over strong baselines.\n\n3. Theoretical Grounding: The appendix provides rigorous mathematical justification for the uncertainty metric and interval fusion operator.", "weaknesses": "1. Ablation Clarity: While ablation studies are included, the relative contribution of each component (Inter-Head, prefix injection, Inter-DRO loss) to the overall performance could be more clearly disentangled.\n\n2. Generalization: Experiments are limited to specific AR architectures (LlamaGen, Anole); it's unclear how well CIAR generalizes to other AR or non-AR generative models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FpISyASxNI", "reviewer": "Reviewer_g7v5", "summary": "The paper proposes CIAR, a cloud–device collaborative framework for AR image generation. A small device model predicts tokens; an on-device Inter-Head outputs probability intervals to decide which tokens can be accepted locally, and which uncertain tokens should be sent to a cloud verifier. A distribution-alignment training is used so the device stays close to the cloud model. The authors claim 2.18× speed-up and 70% fewer cloud requests while keeping quality.", "questions": "Refer to Weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Addresses a real efficiency issue in AR models.\n2. Clear motivation about spatial redundancy and boundary uncertainty.\n3. Some quantitative results show measurable latency improvement.", "weaknesses": "Experiments focus on one dataset with limited metrics; visual quality assessment is also limited, should use some modern metrics like HPS.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)