Diffusion
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Blurring the Block Boundaries: Overlapping Block Denoising for Efficient Diffusion Language Models
2026-11-10 17:40:24Amir Mann, Jonathan Fhima, Shaked Brody, Or Avitan, Asif Zamir, Shahar Tsiper, Niv NaymanAbstract
Diffusion Language Models (DLMs) have emerged as effective alternatives to autoregressive models for latency-critical tasks, enabling faster token generation. Recent advances employ block diffusion with self-editing capabilities, where each block refines tokens iteratively before advancing to the next one. Yet each block is only as fast as its slowest token, and the next block cannot start before it. We suggest Blurring the Block Boundaries (B3), a novel method with which two blocks are refined in parallel within a shared forward pass. B3 calibrates itself on the fly, opening the next block only once the model is ready to start generating it. It is a training-free and hyperparameter-free acceleration method which seamlessly integrates within any self-editing block diffusion model. Evaluated on state-of-the-art DLMs across 10 diverse datasets spanning varied tasks and difficulty levels, B3 results in on par generation quality and 11.5-14.5% speedup compared to the base model, pushing the Pareto frontier for latency-quality trade-offs in DLM inference.
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URL
https://arxiv.org/abs/2611.14387
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An Energy-Based Perspective on Machine Learning
2026-11-10 17:37:22Bo Dai, Yilun DuAbstract
Energy-based models (EBMs) define a distribution through a scalar energy, $p_\theta(x)\propto\exp(-E_\theta(x))$, and are usually presented as one model family among many. This tutorial has a broader goal: beyond introducing EBMs, it uses them as a single lens that connects the different aspects of machine learning. Any EBM is specified by three choices, the energy, which determines what the model represents; the inference procedure, which samples from or optimizes the energy; and the learning method, which fits the energy to data. We first develop these components, including sampling, variational inference, and learning by maximum likelihood, noise contrastive estimation, pseudolikelihood, score matching, and adversarial training, and show that the learning methods share one contrastive structure. We then show that discriminative models, generative models such as GANs, normalizing flows, diffusion and autoregressive models, representation learning methods such as contrastive learning, word2vec, BERT, denoising auto-encoders, and diffusion representations, reinforcement learning with EBM policies and world models, inverse reinforcement learning, and planning, as well as inference-time computation with verifiers, are all EBMs that differ in these three choices, mostly in how they handle the partition function and sampling. This view explains existing methods, organizes them into a common framework, and suggests new algorithms by replacing one component while keeping the others, with composition of energies as a principled way to combine learned models at inference time.
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URL
https://arxiv.org/abs/2611.14383
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Cognitive Mapping of Learning by Thinking Mechanisms in Large Language Models
2026-11-10 17:37:13Dong Han, Jin Zhang, Kunyue YangAbstract
Large language models (LLMs) increasingly exhibit capabilities that resemble human Learning by Thinking (LbT), the acquisition of new insight through internal processes such as explanation and reasoning, without new external input. A systematic account of the cognitive mechanisms behind these capabilities, and of their limits, remains underdeveloped. This paper proposes a Cognitive LbT Framework that maps four mechanisms (Reflective Reasoning, Synthetic Memory Formation, Internal Simulation, and Conceptual Self-Organization) onto computational processes in LLMs. Drawing on representative methods, including chain-of-thought prompting, self-consistency, self-reflection, Tree-of-Thoughts search, Self-Instruct, and self-taught reasoning, the analysis argues that selected methods can functionally approximate aspects of these mechanisms and can improve performance on some tasks without additional labeled examples, although this does not always constitute durable learning in model parameters. For example, Self-Instruct generated approximately 52,000 instructions from 175 seed tasks and reported a 33% absolute improvement on Super-NaturalInstructions (Y. Wang et al., 2023). The same mechanisms also produce characteristic failures, including hallucination diffusion, model collapse, concept drift, and high computational cost. These failures are not isolated technical defects but follow from four structural asymmetries between LLMs and human cognition: the identity of generator and monitor, closed learning loops without external grounding, symbolic rather than sensorimotor simulation, and the cost of inference-time computation. The framework yields testable propositions about when LbT in LLMs should succeed or fail and offers a structured basis for designing more reliable autonomous learning systems.
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URL
https://arxiv.org/abs/2611.14382
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From Video Frames to Robot Meta-Frames: A Unified Latent Interface for World Action Models
2026-11-10 17:21:37Guoqiang Liang, Yiqi Lin, Yanzhe Chen, Kevin Yuchen Ma, Mike Zheng ShouAbstract
Pretrained video diffusion models (VDMs) offer a powerful foundation for World Action Models (WAMs), which jointly predict robot actions and future observations. Building such models requires injecting low-dimensional robot signals (e.g. proprioception, actions, and values) into a latent space shaped by natural videos, and a key design choice is the interface through which these signals enter: existing schemes treat it as a formatting detail, spending a separate latent frame on each signal or bolting on dedicated encoders and decoders. We argue that signal injection is instead a distribution-alignment problem: how well a representation matches the prior of the pretrained VDMs during denoising. Using the relative reconstruction error of a frozen VDM denoiser as a probe, we find that conventional spatial encodings sit far off this prior, whereas frequency-domain packing yields latents that the denoiser corrects far less. Motivated by this diagnostic result, we propose MetaWAM, which folds all robot signals into a single unified meta-frame: each signal is assigned to a disjoint frequency-domain channel band and lifted to the spatial latent by an inverse DCT, yielding a decodable interface aligned with the pretrained VDM prior without any added module or architectural change. A fixed band layout together with slot-aware routing lets the same meta-frame support policy, world-model, and value prediction over a shared set of robot variables. On RoboCasa, MetaWAM reaches 68.9% success while collapsing the per-signal frames into one and cutting inference latency by 39%, and attains 85.0% success on three real-world Franka tasks. Code and models are available at this https URL
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URL
https://arxiv.org/abs/2611.14365
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Forward-Value Discretization: Faster Stochastic Diffusion Sampling Without High-Order Approximation
2026-11-10 16:53:27Yuchen Jiao, Na Li, Changxiao Cai, Yuting Wei, Gen LiAbstract
Stochastic diffusion samplers often require substantially more neural function evaluations (NFEs) than deterministic samplers to achieve comparable sample quality. Although higher-order discretization accelerates deterministic sampling, its benefits for stochastic sampling remains unclear. We establish matching upper and worst-case lower bounds showing that stochastic samplers with higher-order approximations remain first-order convergent, even with exact score functions. Thus, increasing the approximation order does not generally improve the convergence order. Motivated by this limitation, we introduce forward-value discretization, a training-free framework that evaluates the data/noise prediction model at a lookahead estimate constructed from previous predictions. For deterministic sampling, the resulting Forward DPMSolver (F-DPMSolver), improves sample quality at low NFE budgets through its DDIM-based variant, despite remaining first-order convergent. For stochastic sampling, we develop Forward DDPM (F-DDPM), which evaluates the model at a cleaner lookahead point adjusted for subsequent noise injection. This estimate reuses both the previous model prediction and the previously injected noise. Both variants admit first-order convergence guarantees with one model evaluation per iteration. Across CIFAR-10, ImageNet, FFHQ, and LSUN, F-DDPM consistently achieves lower FID than the evaluated stochastic baselines at matched NFE budgets. With 1.5 times as many NFEs as deterministic baselines, it achieves competitive sample quality, narrowing the practical efficiency gap between stochastic and deterministic sampling. Code is available at this https URL.
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https://arxiv.org/abs/2611.14328
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ForeseeDrive: Future-Token Conditioning for Anticipatory Generative Driving
2026-11-10 16:27:00Shaokai Wu, Xunchu Zhou, Pinzhi Wang, Bo Dong, Yuting Hu, Yanbiao Ji, Qiuchang Li, Xiao Cui, Qi Kong, Hongtao Lu, Yue DingAbstract
Generative driving planners can represent diverse ego futures, but a downstream scorer can choose only among sampled candidates; it cannot recover an absent maneuver. We present ForeseeDrive, which turns future scene evolution into a deployable interface for shaping candidate generation. Rather than reconstructing dense future states or scoring a fixed menu, ForeseeDrive derives query-indexed tokens from logged actor motion, occupancy evolution, route interactions, and TTC margins, and predicts the same token space from present context at deployment. The tokens condition trajectory geometry before candidates materialize. A zero-token-gated residual path and candidate-preservation loss limit unnecessary drift, while a TTC-margin guard targets brittle safe-to-unsafe boundary crossings. Under their respective benchmark protocols, ForeseeDrive reaches 89.2 PDMS on NAVSIM navtest, 0.41\,m average L2 and 0.05\% collision rate on nuScenes validation, and a 76.50 driving score on Bench2Drive. Controlled DiffusionDrive/NAVSIM diagnostics show an exact zero-token no-op, a small aligned-over-shuffled selected-PDM advantage under preservation, mixed oracle-best quality, and localized TTC-boundary failures. Together, the benchmark results and bounded mechanism tests support future-conditioned generation as a practical interface for anticipation. Code is included in the supplementary materials.
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URL
https://arxiv.org/abs/2611.14286
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From Prediction to Action: Learning Physical Context for Deformable Linear Object Manipulation
2026-11-10 16:17:48Chengxiao Dong, Alessio Caporali, Hongyu Lan, Gianluca PalliAbstract
A representation that accurately predicts a deformable object's response is not necessarily the one most useful for choosing manipulation actions. We study this distinction between predictive utility and decision utility in planar single-grasp shape control of Deformable Linear Objects (DLOs). As a common decision backbone, we introduce Geometry-Pointer-Flow (GPF), a geometry-grounded policy that separates grasp-location selection from grasp-conditioned motion generation. Its geometry-only variant reduces one-step executed shape error by 32.8% relative to a DLO-specific diffusion policy, while using 18.7 times fewer parameters and achieving approximately 193 times faster inference. On this backbone, we compare physical context represented by ground-truth parameters, estimated parameters, and learned interaction-history representations, for both forward prediction and executed control. For prediction, ground-truth physical parameters reduce next-shape error by 15.8% over geometry alone, and estimated parameters are slightly more predictive than interaction history. This ordering does not carry over to control: estimated parameters provide no detectable one-step control gain, whereas history-conditioned GPF reduces executed error by 9.9% relative to geometry-only GPF across 470 paired simulation trials. In closed loop, interaction history improves strict-threshold attainment in simulation and yields the strongest zero-shot performance on hardware. All policies are trained exclusively in simulation and transferred to the physical robot without real-world training or fine-tuning. These results show that physical context should be evaluated by the decisions it supports rather than by predictive accuracy alone. Code will be released upon acceptance.
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URL
https://arxiv.org/abs/2611.14274
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Location Matters: Sparse Latent-Variance Attacks for Face Privacy against Diffusion Personalization
2026-11-10 16:03:32Shuting Yang, Shuxin Li, Linyan Dai, Xinwei Zhang, Dong Huang, Renyang Liu, See-Kiong NgAbstract
Text-to-image diffusion models can learn and reproduce an individual's identity from only a few reference photos, creating risks of unauthorized and misleading content generation. Proactive defenses address this risk by adding adversarial perturbations to facial images before release, thereby disrupting identity acquisition during downstream personalization. However, existing methods typically rely on dense $L_\infty$-bounded perturbations, whose accumulated distortions can become visible around facial structures. Recent methods improve protected-image quality, but often at the cost of reduced protection effectiveness. In this paper, we propose Sparse Encoder Attack on Latent Variance (SEAL), a sparse adversarial protection framework that modifies only 5% of the spatial locations in a facial image. SEAL combines a differentiable top-k masking scheme that jointly optimizes perturbation locations and values with a variance-oriented objective over the frozen VAE encoder's latent distribution to disrupt identity-consistent representations. A saliency-guided dispersion penalty further discourages perturbation concentration in perceptually salient facial regions. Experiments on CelebA-HQ and VGGFace2 show that SEAL achieves the best protected-image quality among the compared methods, exceeding 41 dB PSNR with 0.97 SSIM and at most 0.07 LPIPS, while reducing identity similarity to at most 0.15 after diffusion personalization. These results demonstrate that sparse location optimization enables strong identity protection while preserving high visual quality. Code and data are available at this https URL
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URL
https://arxiv.org/abs/2611.14254
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Lipschitz-Controlled Flow Matching for Robust Image Restoration
2026-11-10 12:40:17Sedjro Salomon Hotegni, Konstantin Sonntag, Paul-Christian B\"urkner, Sebastian PeitzAbstract
Flow-matching models can produce high-quality image restorations, but their learned dynamics may amplify small input perturbations and make reconstructions unstable. We introduce LipsFlower to reduce this amplification. We derive stability bounds for Flow-from-$Y$, which initializes the ordinary differential equation from a noise-perturbed observation, as well as for observation-conditioned posterior sampling, which conditions the velocity field on the observation throughout integration. Based on this analysis, we develop LipHDiT, a Lipschitz-aware hourglass diffusion transformer that serves as the vector-field architecture of LipsFlower. LipHDiT incorporates stabilized approximate spectral normalization, bounded modulation, and convex residual mixing. We also use adversarial finite-radius gain regularization at sampled interpolation states to penalize excessive sensitivity to the state and, for conditional sampling, the observation. We test both formulations on blind face restoration, measuring adversarial robustness and the perceptual quality of the reconstructions. LipsFlower reduces perturbation amplification while keeping restoration quality competitive with the evaluated baselines. These results suggest that Lipschitz-aware flow matching can improve the stability of generative image restoration.
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URL
https://arxiv.org/abs/2611.13995
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RB-LDC: Redundancy-Balanced Latent Coding for Noise-Robust Diffusion Latents
2026-11-10 11:41:05Hyunseok Jeong, Jaeho Jeon, Young-Sik KimAbstract
Latent diffusion models decode approximately correct latents, making decoder sensitivity to perturbations relevant to image quality. After whitening, forward diffusion corrupts every direction at the same signal-to-noise ratio (SNR), despite unequal decoder sensitivities. We study an invertible linear layer between the encoder and diffusion process that shifts each direction's log-SNR. Its linear minimum mean-square-error (LMMSE) recovery risk has an exact spectral expression for any whitened latent distribution. Under an energy budget and a floor ensuring invertibility, minimizing decoder-sensitivity-weighted LMMSE risk integrated over the diffusion schedule yields a unique optimal allocation. The layer aligns with the decoder's expected pullback metric and equalizes marginal risk reduction across directions, computed by a deterministic spectral solver. At large budgets, the allocation follows a power law in sensitivity whose exponent is determined by the low-SNR tail of the loss weighting: one half for noise prediction and two thirds for data prediction under the cosine schedule, with a crossover on truncated schedules. We instantiate this principle as RB-LDC using Jacobian-based sensitivity estimates. On ImageNet at 256 x 256 resolution with SD-VAE and VA-VAE, trained RB-LDC improves reconstruction from noisy latents over budget-matched isotropic coding in all four metrics and five coding configurations, averaged over 15 SNRs and three pipeline seeds. It reduces average perturbed FID by 17.1 for VA-VAE block-local coding while preserving clean reconstruction. We also report intermediate SNRs where perturbed FID worsens. With a frozen diffusion model, RB-LDC yields lower FID point estimates in all 12 tested combinations of sampling progress and channel SNR under corruption during sampling.
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URL
https://arxiv.org/abs/2611.13955
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Alania-2: Turkish text-to-speech with instant and fine-tuned voice cloning
2026-11-10 10:04:47Sezgin Saygili, Emre Kaplaner, Oncel Ozgul, Fikri San KoktasAbstract
We present Alania-2, a Turkish text-to-speech (TTS) system with two built-in voices, voice design from text descriptions, and two kinds of voice cloning. Alania-2 is a 488M-parameter model with the VoxCPM architecture, which generates continuous audio latents with a diffusion head on top of a language-model backbone. We train it from scratch on 18,195 hours of Turkish speech, 90% of which is synthetic speech produced by a larger teacher model and filtered automatically. A text normalizer rewrites numbers, abbreviations and foreign names into the words a Turkish speaker would say; without it, none of 96 test sentences with digits kept all its critical parts, such as an amount or a date, through synthesis and speech recognition, against 86 with it. On Freya-TR-Eval, a public benchmark of 495 Turkish sentences scored with automatic speech recognition, Alania-2 reaches a word error rate of 1.44%, compared with 5.99% for our previous system. On its built-in voices, it comes within half a point of character error rate of a 2.3B-parameter model at under a quarter of the size, and a 7.6M-parameter single-voice model trained with the same recipe matches the most intelligible systems we measured, which suggests that training data matters more for intelligibility than model size. We also report practical findings on voice cloning: conditioning on the reference audio alone, rather than continuing it, cuts skipped words from 21.9% to 1.7%; 20-30 seconds of reference audio works best; and a low-rank adapter trained in about two minutes raises speaker similarity well above instant cloning. Finally, we describe inference-engine changes that bring first audio down to 32 ms for a single stream. We release our evaluation harness and three Turkish speech and text datasets.
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URL
https://arxiv.org/abs/2611.13845
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Camera equalisation of an 11.5x drop in environmental illuminance: four imitation-learning policies on an SO-101 arm
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Real-robot benchmarks of learned visuomotor policies often record room illuminance as the lighting condition. We show that, on a typical low-cost rig, room light and image light can be almost unrelated. Four imitation-learning policies (ACT, SmolVLA, Diffusion Policy and pi0.5) were trained on the same 50 demonstrations of a pick-and-place task on an SO-101 arm. Each was run for ten rollouts at about 520 lux and again at about 45 lux, an 11.5x reduction. Success was 20/40 in normal light and 19/40 in low light: a change of -2.5 percentage points (95% CI -23 to +19), with no evidence that the policies differed in sensitivity (interaction p = 0.86). The recorded video shows why: mean image brightness was only 3% lower in low light on the front camera and 19% lower on the wrist camera. Both cameras were set to automatic exposure and white balance, which most likely absorbed the difference; exposure was not locked in this experiment. An accidental repeat of one 10-rollout block reproduced its success count (7/10) but matched the first block at only 6 of 10 placements, consistent with chance. We conclude that lux alone does not describe what a policy sees. Camera settings, and image statistics compared with the training recordings, are the quantities to report and control. Data and code are public.
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URL
https://arxiv.org/abs/2611.13810
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Mixed Signals: Weight-Space Programming for EEG and ECG
2026-11-10 08:59:09Dara Varam, Taraneh Aminosharieh Najafi, David AtienzaAbstract
Weight-space programming allows language models to read a trained neural network's weights and edit them. This opens the door to ``training'' by reasoning rather than through gradient descent. Prior work has demonstrated its functionality and promise for game-playing networks and fixing specific failures in language models, even extending to diffusion models. In this work, we present its applicability to models trained on biosignal data, specifically EEG and ECG data. We show that frontier models are not only able to debug specific faults planted in biosignal-based models without being told what these faults are, but are even capable of writing all the weights of a network from scratch, given only the architecture, the task, and a minimal set of training subjects. In particular, in one set of experiments, a language model was able to match standard gradient-descent training using $6\times$ fewer training subjects, through reasoning alone and without any gradient descent step. Networks written by Claude Opus 5.5 also scored higher than classic hand-crafted EEG pipelines (alpha band power, and common spatial patterns with linear discriminant analysis) when built from the same 5 subjects. We also highlight some of the limitations, which include the inability of language models to improve already well-trained classification models, and their incapacity to reliably recognize, from the weights alone, the task a network was trained on. Overall, our results indicate that weight-space programming can be useful for small networks, such as those deployed on edge devices and wearables, but the concept still requires further investigation before it can be used in practice.
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URL
https://arxiv.org/abs/2611.13758
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Bridging the Evidence-to-Execution Gap:A Reflective Agent for Multi-Objective Peptide Design
2026-11-10 08:57:01Haosen Zhang, Yang YangAbstract
Large language models can reason over scientific literature, but textual plans do not reliably produce biological sequences with the intended properties. Sequence generators capture sequence patterns, yet they do not interpret evidence, choose interventions, or reflect on outcomes. This separation creates an evidence-to-execution gap between scientific reasoning and sequence-level control. We introduce EASER, a reflective agent that connects the two through a learned property interface. The interface consists of property-specific low-rank directions trained offline and fixed during search; an LLM controller (the controller) composes them to steer a frozen diffusion generator. From retrieved evidence, sequence context, and prior outcomes, it proposes intervention hypotheses that specify anchors, editable positions, and coefficients. Probe-and-Steer tests these hypotheses and allocates further samples from their joint predicted responses; reflection updates the agent's memory. On computational antimicrobial peptide design, EASER balances activity, non-hemolysis, and non-toxicity. Explicit hypothesis formulation yields larger multi-objective gains than direct action generation, and ablations support evidence retrieval, episodic history, reflection, and Probe-and-Steer. EASER achieves the highest mean hypervolume and lowest mean IGD+ among the evaluated search policies and generators on screened candidates. These results show that a learned property interface can make literature-grounded reasoning executable at the sequence level.
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URL
https://arxiv.org/abs/2610.06190
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EgoHMP: Achieving Precise Human Motion Prediction in 3D Scenes via Egocentric Cues
2026-11-10 06:54:06Xin Zhao, Pengzhan Zhou, Ziyi Li, Riheng Jia, Zhida Qin, Yu LiuAbstract
The complex interactive dependencies between human behavior and 3D scenes make accurately predicting human intent a major challenge for human motion prediction (HMP). While recent studies use human gaze to infer intent, gaze signals suffer from ambiguity, drift, and a reliance on eye-tracking hardware. Research in cognitive science indicates that egocentric views inherently encode potential interactions, while head motion serves as a crucial precursor in motion planning. Motivated by this, we propose EgoHMP, a novel framework that uses egocentric views and head motions as robust carriers of interaction intent. By translating this intent into an envisioning of future interactions, EgoHMP achieves precise HMP in 3D scenes. EgoHMP first employs a vision-motion alignment encoder to align the visual and motion feature spaces. Based on these aligned features, an egocentric intent predictor with a modality-aware modulation mechanism adaptively suppresses invalid interaction cues to predict an accurate interaction probability map. Furthermore, to enhance the stability and realism of the generated motions, we propose a contact-aware motion decoder. Specifically, inspired by the human cognitive process of planning trajectories prior to action execution, we first predict a global trajectory to serve as a self-prompt, effectively mitigating the optimization conflict in diffusion models. Subsequently, we introduce a multi-stage denoiser, which relieves motion artifacts by incorporating human contact modeling through a multi-stage optimization strategy. Finally, a GCN-based motion decoder is employed to synthesize physically plausible and semantically consistent motions. Extensive experiments demonstrate that EgoHMP achieves state-of-the-art performance.
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URL
https://arxiv.org/abs/2611.13654
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When to Start Denoising? Adaptive Denoising Entry for Generative Video Compression
2026-11-10 06:02:13Zifu Zhang, Yuqiao Li, Ding Ding, Shengxi Li, Mai Xu, Daowen Li, Kai Li, Ying ChenAbstract
Generative video compression offers a promising solution for compressing and reconstructing videos under extremely low bitrate constraints, among which the diffusion-based solutions still achieve the best performance by providing the powerful generative latent prior. However, their effectiveness critically depends on when the compressed latent is introduced into the diffusion process, which directly governs the trade-off between reconstruction fidelity and perceptual realism. To address this limitation, we identify denoising start-timestep selection as a key factor governing this trade-off and formulate it as a geometric optimization problem. Based on this formulation, we propose a novel adaptive diffusion entry method for generative video compression (ADE-GVC), the first timestep-aware generative video compression framework, featuring a lightweight predictor that estimates an adaptive denoising start point from the compressed latent. A temporal adapter further extracts compression-aware conditions and injects them into a pretrained video diffusion model, enabling the reconstruction of videos with both high fidelity and realism. More importantly, we observe a strong quantitative relationship between the predicted timesteps and the distortion between the original and compressed latents, providing empirical support for our optimization formulation. Extensive experiments demonstrate that ADE-GVC achieves the state-of-the-art performance on perceptual metrics and substantially improves reconstruction realism over existing generative video compression methods. Notably, our ADE-GVC achieves an average bitrate savings of 40.99\% (on DISTS) and 95.24\% (on PSNR) compared to the latest open-source baseline GLC-Video.
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URL
https://arxiv.org/abs/2611.13620
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Direct Trajectory Optimization for Inference-Time Alignment of Text-to-Image Diffusion Models
2026-11-10 05:52:03Huixuan Zhang, Li Lin, Junzhe Zhang, Xiaojun WanAbstract
Alignment of text-to-image diffusion models with reward functions is a key challenge in controllable generation. Existing approaches typically rely on optimizing model inputs or fine-tuning denoising networks, which requires computationally expensive backpropagation through large denoising models. In this work, we propose Direct Trajectory Optimization (DTO), a training-free framework that performs alignment by directly optimizing the sampling trajectory of diffusion models. Specifically, DTO treats intermediate denoising scores and sampled noise as the optimization variables, eliminating the need for gradients through the denoising network. To mitigate the out-of-distribution problem, we introduce Reward-Optimized Score Guidance, which interpolates between the optimized scores and the pretrained model predictions to stabilize sampling, while providing a provable theoretical guarantee of reward improvement under constrained distributions. Empirically, DTO improves alignment performance across three diffusion models on two benchmarks, outperforming existing inference-time alignment methods while maintaining high efficiency. These results suggest that trajectory-level optimization is a promising direction for efficient and effective diffusion model alignment.
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URL
https://arxiv.org/abs/2611.11821
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Split and Bridge: Multimodal Generation via Diffusion Bridging
2026-11-10 05:18:50Ahmad Arrabi, Xiaohan Zhang, Xingyu Li, Safwan WshahAbstract
Visual data are often expressed through multiple complementary modalities (e.g., images and segmentation masks), each capturing different aspects of the same underlying structure. Most existing multimodal methods adopt a top-down paradigm, learning a shared latent variable through joint training from scratch, which limits scalability. We propose Split and Bridge (SnB), a bottom-up framework for multimodal generation that couples pretrained unimodal diffusion models at sampling time. Rather than learning a unified latent space, SnB generates coherent multimodal samples by following a shared diffusion trajectory up to a split point and then branching into modality-specific trajectories guided by a bridge module. This design enables plug-and-play reuse of powerful pretrained models without joint training, making it scalable and easily adaptable. Across standard benchmarks (PolyMNIST, CelebAMask-HQ) and more complex settings (PIE-Bench++, ROCO), SnB achieves a strong balance between sample quality and coherence. We show that SnB naturally extends to downstream zero-shot tasks such as interleaved co-generation, image editing, and style coupling/decoupling. Our results demonstrate that bottom-up generation provides a practical framework that complements existing top-down multimodal approaches. Code is available at this https URL
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URL
https://arxiv.org/abs/2611.10441
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Dark Matters: VAE-Aware HDR Encoding for SDR-to-HDR Video Reconstruction
2026-11-10 03:16:31Donghoon Cha, Bomseok Park, Hansl von Kwon, Ku DoHyung, Hanwool SulAbstract
SDR-to-HDR reconstruction is an ill-posed problem in which clipped highlights, crushed shadows, and quantization artifacts must be recovered while preserving the original content. Latent diffusion models provide powerful generative priors for restoring missing detail, but their effectiveness is constrained by how HDR signals pass through the pretrained VAE. We find that common log-like encodings suppress low-luminance chromatic differences before generation, producing a dark-side collapse in the frozen-VAE latent. We introduce EquivFlow, a compact, invertible exposure-equivariant encoding that maps exposure changes to additive shifts while retaining a scale-independent chromatic component, without modifying the VAE. Encoding-level tests across pretrained image and video VAEs show that its exposure structure and deep-dark chroma transport persist across the tested hosts. Integrated into SDR-to-HDR video diffusion, EquivFlow delivers consistent chromatic-fidelity gains across the tested settings while retaining competitive reconstruction fidelity.
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URL
https://arxiv.org/abs/2611.13504
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Discrete Model-Based Diffusion for Combinatorial Optimization in Multi-Robot Task Allocation
2026-11-09 23:59:47Haejoon Lee, Thomas Marshall Vielmetti, Xinyi Wang, Dimitra PanagouAbstract
Decision making in multi-agent and multi-robot systems often involves discrete decision variables, leading to NP-hard combinatorial optimization problems that must be solved within seconds for near real-time implementation. Denoising diffusion has emerged as a powerful tool for such problems, but existing approaches face a trade-off: Model-Based Diffusion (MBD) incorporates diffusion in sampling-based optimization (SBO) without training but only over continuous spaces, while existing diffusion-based combinatorial solvers operate over discrete spaces but require offline training. To this end, we propose Discrete Model-Based Diffusion (Discrete MBD), a training-free SBO method that directly optimizes discrete variables by approximating categorical reverse transition distributions through Monte Carlo sampling. We establish a finite-step lower bound on the probability of finding a global minimizer and show that this bound converges to one as the number of denoising steps increases. Extensive experiments on generic integer programs and heterogeneous multi-robot coalition formation and scheduling demonstrate that Discrete MBD outperforms SBO baselines in nearly all settings while achieving solution quality competitive with that of mixed-integer programming and reinforcement learning methods at a fraction of their computational cost.
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URL
https://arxiv.org/abs/2611.13382
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