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01 ·★ MEMBER·PAPER·15 min read Paper Walkthrough — The Missing Temporal Link: Putting Script Timing on the Video–Audio Axis with Temporal Context Routing Video and audio stay in sync with each other while both drift off the script's timeline. This walkthrough follows the paper's own equations and numbers to show how TCR closes that third, missing temporal link with a single additive term in the cross-attention logits. 02 ·★ MEMBER·PAPER·10 min read Paper Walkthrough: Puffin-World — A World Model That Remembers Which Way Is Up What changes when you give an image generator a sense of gravity? A ground-up walkthrough of Puffin-World's Omni-Camera representation and physics propagation, with equations and interactive figures. 03 ·★ MEMBER·PAPER·9 min read Paper Walkthrough: LLaDA-Image — Building the Visual Prior from Images Alone, Then Distilling to 2–4 Steps A training recipe that builds the visual prior from images alone before any caption enters the picture. We walk through LLaDA-Image — a 6B DiT trained from scratch and distilled down to 2–4 sampling steps — following the design decisions the paper actually makes. 04 ·★ MEMBER·PAPER·14 min read SolarWM: Training on 5 Seconds, Walking for an Hour — and Opening the Whole Stack A data engine that folds 10 datasets into 1.43M clips under one contract, plus a three-stage recipe that adapts four different video backbones without erasing them. The causal models roll out for minutes to hours after training on 5-second sequences alone. 05 ·★ MEMBER·PAPER·10 min read Paper Explained — GenFirst: Let Generation Shape the Latent Space First, Reconstruction Second Image generation normally means training a VAE first and bolting a generative model onto its frozen latent space. This paper trains both at once without latent collapse — the key is the entropy term inside the KL objective, plus a simple rule: let generation go first and ramp reconstruction up later. 06 ·★ MEMBER·PAPER·9 min read Paper Walkthrough: On-Policy Self-Distillation in Diffusion Models — Turning Reward into a Target You Can Aim At When you train an image generator with rewards, a score on the finished picture never tells the model how to change its intermediate denoising predictions. DiffusionOPSD builds explicit targets from reward gradients and fits them using an EMA copy of the model itself. A ground-up explanation, strictly within what the abstract states. 07 ·★ MEMBER·PAPER·9 min read Paper Explained: VGI-Bench — Can Video Models Get the Middle of the Story Right? A ground-up walkthrough of VGI-Bench, a benchmark that treats video generation models as machines that reason in pictures. Even the strongest model, Seedance 2.0, scores only 51.0 — and self-correction during denoising almost never happens. 08 ·Generative Models·★ MEMBER·PAPER·10 min read The State of 3D Generation — From NeRF to Gaussian Splatting 3D stopped being something you sculpt and became something you fit by optimization. How the representation moved from meshes to NeRF to Gaussian Splatting, how SDS turns a 2D diffusion model into a judge that supervises 3D, and what breaks when you take any of it into a game or film pipeline. 09 ·Generative Models·★ MEMBER·PAPER·11 min read The Mathematics of Diffusion — Generation Seen Through Scores and SDEs A layer beneath 'add noise, then subtract it': diffusion models restated in the language of the score, the gradient of log-density. Why denoising and score estimation are literally the same job, what the forward SDE, reverse SDE and probability flow ODE actually assert, and how those equations turn into the knobs you set at inference time. 10 ·Generative Models·FREE·PAPER·11 min read A Practical Map of Image Generation — SD, ControlNet, and Applying LoRA A single map for anyone about to touch image generation for the first time: the four boxes of latent diffusion, where the prompt actually takes effect, locking down composition with ControlNet, fine-tuning with LoRA, and the licensing layers that quietly cause the most damage. 11 ·★ MEMBER·PAPER·14 min read Paper Walkthrough: EchoWM — A World You Can Walk Into, and Hear, Driven by Camera Intent Hand it a reference view and a stream of 'where I want to go', and it keeps generating 720p video together with environmental sound, music and speech. A walkthrough of EchoWM from the paper itself: one camera-intent interface for both first and third person, a single translation scale shared across the whole dataset, and the sink-plus-FIFO cache that makes long rollouts possible. 12 ·Generative Models·★ MEMBER·PAPER·11 min read Build Your Own Diffusion Model — Starting from MNIST A diffusion model built up from nothing on 28×28 handwritten digits: the two conditions a noise schedule has to satisfy, how the step number gets injected into a U-Net, and why the sampler adds noise back at the very end — the places you only discover by writing the code yourself. 13 ·Generative Models·★ MEMBER·PAPER·8 min read CFG and Samplers — What the "Strength" Knob in Generative AI Really Does What "CFG Scale" and "Sampling steps / method" actually do inside an image generator, explained from zero. CFG extrapolates along the difference between the conditional and unconditional prediction; the sampler is a numerical ODE solver. Both knobs trade something away. 14 ·Generative Models·★ MEMBER·PAPER·10 min read The Rise and Fall of GANs — An Invention Trained by Rivalry, and Why Diffusion Won In 2014, GANs upended image generation with one idea: don't write down what 'realistic' means — learn it. This traces the decade that followed, through mode collapse, the Wasserstein rescue, and the reasons diffusion models eventually took the crown. 15 ·Generative Models·★ MEMBER·PAPER·10 min read Flow Matching from Scratch — What Came After Diffusion, and Why It Goes Straight Flow matching — the formulation underneath Stable Diffusion 3 and FLUX — explained with no prerequisites. Velocity fields, ODEs, and rectified flow, told through analogy and interactive figures, all the way to the scheduler settings you actually touch in production. 16 ·CNNs & Image Recognition·★ MEMBER·PAPER·10 min read Paper Walkthrough: PhiZero — A World Model That Reasons in a Language of Physics Before It Renders A close read of PhiZero, which reasons about how the world will change in a discrete 'physical language' learned self-supervised from video, and only then renders the footage — instead of predicting pixels directly. The two-stage tokenizer and reasoner, the top spot on Physics-IQ Verified, zero-shot motion transfer, and the limitations the paper itself admits. 17 ·★ MEMBER·PAPER·8 min read Paper Explained: JoyAI-Video-Edit — Autoregressive Diffusion That Keeps Editing 720p Video at ~30 FPS, Without Ever Seeing the Future Say 'swap the background for snowy mountains' and the live feed comes back edited as it streams — a walkthrough of JD's JoyAI-Video-Edit from the paper itself. We trace the three-stage recipe (chunk-wise autoregression, Source-Anchored DMD, long-horizon distillation) that lets a 16B model edit 720p video at roughly 30 FPS. 18 ·★ MEMBER·PAPER·8 min read Paper Walkthrough: Hunyuan3D-Buffalo 1.0 — One Brain for 3D Understanding, Generation, and Editing Describe a 3D shape in words, generate it from text, edit it by instruction, carve out a named part — Tencent Hunyuan's Hunyuan3D-Buffalo 1.0 does all four in a single model. We walk through the 87M-sample data factory behind it, the Nano3D-v2 editing-pair pipeline, and the paper's most practical finding: training generation makes editing better. 19 ·★ MEMBER·PAPER·9 min read Paper Walkthrough: AURORA-LM — A Diffusion Language Model That Keeps Its Text Latent Full-Width Instead of Compressing It A walkthrough of AURORA-LM, a continuous-latent diffusion language model that rejects the usual compromise of compressing text latents to make diffusion easier. It keeps a decodable, width-1024 latent and learns its distribution with a low-rank input pathway, width-calibrated noise, and self-trajectory consistency. 20 ·Generative Models·★ MEMBER·PAPER·9 min read Diffusion Models from the Ground Up — Add Noise, Then Subtract It A reading of the DDPM paper (Ho et al., 2020) grounded strictly in its own text: the forward process that adds noise and the reverse process that removes it, why the objective settles into 'predict the noise', how a simplified loss hurt likelihood while improving samples, and what the paper does and does not say about step count.