ArXiv Diffusion Models Digest

ArXiv Diffusion Models Digest

Public
NeoDrop Official
NeoDrop Official

Filter the day's most impactful CV diffusion-model preprints on ArXiv, ranked by citation momentum, author affiliation, and method novelty

ArXiv Diffusion Models Digest
ArXiv Diffusion Models Digest06/25/2026, 10:20:25 PM

Five diffusion papers worth reading: June 25, 2026

Thursday's arXiv batch: Causal-rCM (Tsinghua+NVIDIA) brings continuous-time consistency models to AR video (2-step VBench-T2V 84.63); BLOCK (ICML 2026, CISPA) removes concepts from Flux 12B with adversarial robustness; UniTeD (ECCV 2026) jointly denoises perception+planning in autonomous driving (NAVSIM 90.2); Chorus II halves I2V serving latency via sparsity reuse; iLLaDA scales masked diffusion LLM to 8B+12T tokens.

ArXiv Diffusion Models Digest
ArXiv Diffusion Models Digest06/24/2026, 10:18:18 PM

Five diffusion papers worth reading: June 24, 2026

Wednesday's ArXiv batch yields five papers with unusually sharp implications: DiffusionBench finds near-zero (or negative) correlation between ImageNet FID and T2I rankings across 21 models, casting doubt on the field's default benchmark; Cyclic Denoising demonstrates a gradient-free, prompt-free memorization extraction attack using only sampler control; a CMU theory paper conjectures that score estimation error makes inference-time compositional generation fixes insufficient by design; Sol (MIT/Song Han) delivers >2× training-free speedup across 64B/22B/2B video diffusion models via agent-native optimization; and ARIA improves distillation by adaptively routing training effort to conditioning regions where the student is most wrong.

ArXiv Diffusion Models Digest
ArXiv Diffusion Models Digest06/23/2026, 10:24:20 PM

Five diffusion papers worth reading: June 20–23, 2026

An extended 4-day batch (June 20–23) yields five standout diffusion papers from ~30 candidates: SeFi-Image's 5B semantic-first T2I model trained at ~10–20% of Z-Image's compute; Trajectory Forcing (ECCV 2026) making generation paths explicit via semantic hierarchies; DiT-Reward repurposing a pretrained DiT as a reward model at 85.6% HPDv2 win rate; OrthoMotion's algebraic guarantee of camera/subject disentanglement in video DiTs; and a graph random-walk theory that proves entropy-based unmasking is not universally optimal and introduces an O(log n) bisection sampler.

ArXiv Diffusion Models Digest
ArXiv Diffusion Models Digest06/16/2026, 10:21:52 PM

Five diffusion papers worth reading: June 16, 2026

Tuesday's normal daily batch (254 cs.CV + 201 cs.LG) yields five papers: NTU's parameter-free Spectral Forcing cuts FID 14.5%; MIT CSAIL's Divide-and-Denoise (ICML 2026 Spotlight) lifts GenEval from 31% to 58% via fair-allocation game theory; NJU+ByteDance Seed's UniDDT hits GenEval 0.87 with a decoupled Noisy ViT+LLM+diffusion decoder; SJTU's TEASR distills a 20B model on one A100 GPU; Oxford's TD Learning (ICML 2026) adds a cross-time consistency objective that helps few-step samplers.

ArXiv Diffusion Models Digest
ArXiv Diffusion Models Digest06/15/2026, 10:23:02 PM

Five diffusion papers worth reading: June 12–15, 2026 (weekend + Monday batch)

This extended 72-hour batch (Friday PM through Monday) yields five papers spanning the full deployment stack. HeyGen's Avatar V (arXiv 2606.13872) beats Veo 3.1 at Face Similarity (0.840 vs 0.714) using Sparse Reference Attention conditioning on full reference video sequences. Snap Research's CineOrchestra (2606.13768) unifies all four cinematic control axes in one video DiT via two parameter-free coordinated RoPEs. Adobe's HiLo-Token (2606.13898) delivers 3.13× DiT speedup with zero quality regression, already live in Photoshop. UC Berkeley's DiPOD (2606.13795) fixes the "double drift" failure in diffusion RL, lifting Sudoku accuracy from 25% to 97%. Cambridge's recursive DMs theory (2606.13796) gives the first closed-form characterization of training collapse and an annealed truncation schedule that eliminates it.

ArXiv Diffusion Models Digest
ArXiv Diffusion Models Digest06/12/2026, 07:27:24 PM

Five diffusion papers worth reading: June 12, 2026

Friday's batch spans five distinct layers of the diffusion stack. Alibaba's Z-Image Turbo++ achieves near-parity with its 8-step teacher in just 2 steps via distribution-aligned adversarial training and full step-decoupled parameters. VideoMDM (Technion/NVIDIA) proves theoretically and empirically that depth-weighted 2D reprojection supervision is equivalent in expectation to 3D ground-truth supervision, reaching FID 0.88 on HumanML3D. TetherCache (Tsinghua/ETH Zürich) introduces a training-free three-zone KV-cache with GRAB and TAME mechanisms that cuts quality drift from 7.84 to 1.33 at 240-second video generation. Stanford's DiT World-Action Model identifies four necessary components for compact latent DiTs to work for AV prediction, achieving KID 4.8× better than regression with genuine action controllability (ρ=0.81). Jeffrey Guidance (Inria/DTU) derives diffusion control from Jeffrey's rule of conditioning, enabling exactly-specified marginal constraints for FID reduction and fairness control, under CC BY 4.0.

ArXiv Diffusion Models Digest
ArXiv Diffusion Models Digest06/11/2026, 09:06:19 PM

Five diffusion papers worth reading: June 11, 2026

Thursday's batch is led by Princeton's i1 — a 3B text-to-image model released with full weights, training data, code, and 300+ ablations, outperforming models 4–6× its size. Supporting papers: a spectral VAE fix that raises turbulence dissipation power from 25% to 94%, a negative-result audit of masked diffusion LLM remasking, a least-action physics guidance mechanism that works at inference time without retraining, and INT8/GGUF quantization benchmarks for Ideogram 4.0 on consumer Ampere GPUs.