denoising

This paper introduces Prefix-Denoising Consistency (PDC), a test-time verification method for Diffusion Language Models that improves performance on reasoning tasks by using prefix-conditioned regeneration and majority voting.

Semantic Reasoning Denoising (SRD) introduces an operatorized Markov denoising method to correct semantic errors in language model reasoning trajectories, using executable error operators and iterative refinement, with demonstrated improvements across six benchmarks and competitive performance on transfer tasks.

This paper introduces Entropy-Valley, a training-free length selector for masked diffusion machine translation that uses predictive entropy to improve adequacy, showing that length choice matters more than unmasking order.

TT-Net introduces a quantum-inspired tensor network denoising block for conditional GANs that accesses cross-channel information, outperforming SVD-Net and other methods on PSNR and SSIM metrics across various noise types.

This paper challenges the assumption that iterative denoising always improves forecasts in diffusion-based time series forecasting, proposing a global stopping criterion and a Bernoulli sampler to enhance accuracy and speed.

Introduces Continuous Interaction Diffusion (CID), a diffusion-native model–runtime architecture that integrates tool interaction into iterative denoising, enabling asynchronous external reads while generation continues without waiting for discrete tool calls.

Introduces ZUNA1.1, a 380M-parameter diffusion autoencoder for flexible EEG signal reconstruction, capable of handling variable-length sequences, arbitrary channels, and temporal intervals, while outperforming standard interpolation methods. The model is released open source under the Apache 2.0 license.

Introduces ReDe, a framework that denoises reasoning traces by filtering irrelevant and repetitive steps to improve hallucination detection in large reasoning models, achieving up to 87.32 AUROC on TruthfulQA.

This paper presents GAIA, a geometry-aware learning framework for UWB denoising and work-zone reconstruction that couples temporal range modeling with latent anchor-layout estimation. Evaluated on real-world outdoor data, GAIA reduces range MSE by 18.4% and improves polygon IoU by 15.5% over baselines, demonstrating effective boundary-level reconstruction under NLOS conditions.

This paper introduces dOPSD, an on-policy self-distillation method for diffusion language models that leverages internal denoising trajectories to improve mathematical reasoning and code generation.

PhotoQuilt is a training-free framework that generates high-resolution photomosaics by combining global layout composition with separate tile generation in latent space, overcoming diffusion models' limitations in balancing local detail and global structure.

The paper proposes DIF, a model-agnostic method for denoising implicit feedback in cold-start recommendation by using pseudo-labels from content-similar warm items and uncertainty estimation, achieving significant improvements in a billion-user video app.

RepFusion introduces a method to use pretrained multimodal LLMs as noisy representation encoders in diffusion transformers for text-to-image generation, outperforming baselines with similar compute.

Discusses various methods to optimize DiffusionGemma inference, reduce hallucination, and improve performance for tool use and agents, including entropy-bounded sampling, schema scaffolding, and retrieval during denoising.

RepFusion proposes using multimodal large language models as noisy representation encoders for diffusion transformers in text-to-image generation, outperforming traditional denoising approaches.

UniPET is a universal network for PET image denoising that handles varying dose reduction factors using domain generalization and region-aware learning, achieving state-of-the-art performance.

PhaseLock is a training-free framework that preserves motion priors from early-step inference to improve physical consistency in image-to-video diffusion models, achieving 6.2 point improvement with minimal overhead.

This paper proposes Geodesic Flow Matching, a Riemannian transport method for denoising Spatial Semantic Pointers (SSPs) on toroidal manifolds, and demonstrates a 72% reduction in tracking error and 40% efficiency gain in a spiking neural SLAM system.

This paper introduces Temporal-Spatial Parallel Decoding (TSPD) and Confidence Extrapolation (CE) to accelerate inference in diffusion-based large language models by dynamically deciding when tokens have converged and forecasting logit trends, reducing unnecessary denoising steps while preserving output quality.

A self-improving AI framework that simultaneously updates both model weights and task-specific agent architecture via a language-model feedback agent, achieving significant gains across legal classification, GPU optimization, and biological denoising tasks.