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Research

Token Identity Brier Flow Improves Continued Training of Continuous Language Flows

Researchers propose Token Identity Brier Flow (TIBF), which applies squared supervision directly to token identities when continuing pretrained language flows, combining network scores with token-matching scores under a Gaussian observation model and using the Brier loss for token probabilities. They show that squared error in predicted token probabilities bounds the squared errors of continuous denoising predictions and flow velocities, and provide a risk decomposition separating observation uncertainty from posterior fitting error. Under matched continuation budgets with frozen embeddings, TIBF achieves Gen.PPL values of 33.96 on OWT and 81.17 on LM1B, outperforming continuous-MSE and cross-entropy continuation controls and attaining the lowest Gen.PPL at every tested sampling budget on both datasets.

arxiv16h agoScore 70

continuous language modelingdiffusion modelslanguage modeling

Research

Measured Coupling Improves Parallel Decoding in Diffusion Language Models

This paper introduces a method to improve parallel decoding in diffusion language models by directly measuring the coupling between token positions—how committing one token changes confidence at another—using cheap probes inside the main forward pass. This replaces the mean-field decoding proxy that penalizes similar predictions, requiring no training or extra model. On three models and four benchmarks, the method reduces decoding steps and is faster in most settings, including with a key-value cache, at an average accuracy cost of 1.1 points.

arxiv16h agoScore 70

diffusion modelsdecoding algorithmslanguage generation

Research Editor selected

DyPSI: Joint Generation of Physics Fields and Sensor Trajectories for Dynamic Sensing

DyPSI is a new framework for dynamic physics sensing that jointly generates future spatiotemporal fields and sensor trajectories from historical observations. It uses continuous Sensor Position Fields, a shared Functional Tucker representation, and a joint diffusion model to produce temporally coherent sensing plans. Experiments on turbulent flow, sea-surface temperature, and car aerodynamics show improved error reduction over static and frame-wise placement strategies.

Why it matters: This paper introduces a novel method that addresses a key limitation in sensor placement by jointly optimizing field reconstruction and trajectory generation, with demonstrated gains on multiple real-world datasets.

arxiv1d agoScore 82

sensor placementdiffusion modelsphysics-informed AI

Research Editor selected

D3D: Decoupled Diffusion Drafting for Asynchronous Speculative Decoding

D3D is a standalone diffusion drafter that removes inter-round dependency in speculative decoding, allowing drafting of successor blocks during verification in a packed pass. It combines blockwise denoising with auxiliary autoregressive supervision and prunes layers to reduce cost. Across seven benchmarks, D3D achieves 5.00x and 5.71x speedups with Qwen3-8B and Qwen3-32B targets, outperforming DFlash.

Why it matters: This paper presents a concrete, recent AI development with measurable speedup improvements and open-source code, offering practical value for efficient LLM inference.

arxiv1d agoScore 88

Speculative DecodingDiffusion ModelsEfficient Inference

Research Editor selected

Learning Clean Generative Processes from Noisy Measurements

This paper introduces a method to train generative models of clean images using only noisy observations, without ever seeing clean data. It shows that Noise2Noise regression can learn posterior predictions at or above the training noise level, and that imposing a consistency condition uniquely determines the continuation to the clean image distribution for several noise types. Experiments on CIFAR-10, photon-counting microscopy, and synthetic aperture radar demonstrate that the consistency relation repairs the generation process below the noise level, which otherwise deteriorates.

Why it matters: This is a novel theoretical and practical contribution that enables training generative models on noisy data alone, with broad applicability in imaging domains where clean data is unavailable.

arxiv4d agoScore 82

diffusion modelsnoise-robust learningimage generation

Research

EPC-RawDC: Physics-Constrained 3D Diffusion Improves CBCT-to-CT Synthesis

Researchers introduced EPC-RawDC, a conditional 3D latent diffusion framework for CBCT-to-CT image synthesis that incorporates scanner-specific acquisition physics rather than relying on image data alone. The method combines a geometry-equivariant projection constraint with raw-data consistency that uses noisy projection measurements from each scan as scan-specific supervision during training. Evaluated on two clinical cohorts with different scanner geometries (Elekta JUST and simulated Varian SynthRAD2025), it reached 0.97 SSIM, improved PSNR over DDPM by 2.66 dB and 2.10 dB, and cut MAE by 20.3% and 19.0% respectively. Code is slated for release on GitHub after publication.

arxivOct 30, 2026Score 78

CBCT to CT synthesisdiffusion modelsradiotherapy imaging

Research

EventSeeColor Uses Diffusion to Reconstruct Color from Monochrome Camera Events

EventSeeColor combines event-reconstructed grayscale images with aligned event data to guide a diffusion-based colorizer. The method uses semantic context, region-balanced chroma supervision and local RGB refinement to improve reconstructed appearance. The authors report leading structural similarity, perceptual quality and chromatic-region color accuracy among the external methods evaluated across two real event datasets.

arxivOct 30, 2026Score 79

EventSeeColorEvent-based visionDiffusion models