#generation

55 articles

01 ·★ MEMBER·PAPER·7 min read Paper Explained: WorldCrafter — A Video World Model That Remembers What You Saw, via Camera-Queryable Memory Video world models tend to repaint a room differently when you walk back into it. WorldCrafter tackles this with an implicit 3D-aware memory that is read out from the viewpoint you are about to look from. We explain it from scratch with equations, interactive figures, and code. 02 ·★ MEMBER·PAPER·7 min read Paper Explained: Realtime-Venus — A Voice-and-Video Assistant That Keeps Talking While Work Runs in the Background Listening, speaking, and background delegation all share one clock ticking once per second. We walk through the 9B models and the harness that let an assistant hand off tool calls without pausing the conversation, from analogy to equations to pseudocode. 03 ·★ MEMBER·PAPER·10 min read Paper Walkthrough: MaP-WAM — Turning a Robot's Memory into Plans So the Executor Stays the Same Size as History Grows For robot tasks you can't solve without remembering the past, MaP-WAM stops feeding the executor the full video history and instead compresses it into a plan for what to do next. The executor predicts its own progress alongside actions to decide when a step is done. We walk through the design, which reports 83.3% on RMBench and 78.0% on real robots, straight from the paper. 04 ·★ MEMBER·PAPER·8 min read Paper Explained: Can MiniMax-H3 Reason About the Physical World? Testing an Omni-Modal Generator on What the Prompt Leaves Out An evaluation paper that gives MiniMax-H3 — a generator that takes text, images, audio and video together — 517 tasks where the answer is never in the prompt and can only be worked out by combining the inputs. Overall success is 41.97%, and telling events apart by sound manages just 27.40%. 05 ·★ MEMBER·PAPER·8 min read Paper Explained: LIT — Stopping Robots from Cheating on Visual Cues by Giving Vision a Single Doorway Robot foundation models learn to lean on irrelevant cues like backgrounds and lighting. LIT first teaches goal-directed motion without images, then lets vision reach the action expert only through pose-predicting latent tokens — improving LIBERO-Plus success on all four architectures tested. 06 ·★ MEMBER·PAPER·7 min read Paper Explained: SpatialBlock — Teaching Vision-Language Models Spatial Sense with 15,000 Block-Stacking Puzzles A paper that improves spatial reasoning in vision-language models using nothing but 15,000 synthetic block-stacking questions — no real-scene 3D labels. We walk through how the data is built, the two training recipes, what actually helped, and where it falls short. 07 ·★ MEMBER·PAPER·6 min read Paper Explained: Mi-Ripple — Diagnose First, Then Repair the Ripple That Iterative AI Editing Leaves Behind Feed an AI-edited image back in as the reference for the next edit, and grid-like and grainy texture — digital ripple — starts to build up. Mi-Ripple first tells apart a lattice you can cut out in the frequency domain from grain tangled up with the picture, then picks either a notch filter or regeneration from a cleaned reference. 08 ·★ MEMBER·PAPER·8 min read Paper Explained: SenseNova-U1.5 — One Model That Both Sees and Draws, With No Encoder and No VAE SenseNova-U1.5 is an 8B unified model that learns straight from pixels and words, with no vision encoder for understanding and no VAE for generation. We walk through its seam-free spatial decoder and its 'train specialists separately, then distill them into one' post-training, following the paper itself. 09 ·★ MEMBER·PAPER·11 min read Paper Explained: VDiff-Bench — Even Frontier MLLMs Miss the Low-Level Changes in a Spot-the-Difference Puzzle VDiff-Bench asks a multimodal LLM what changed between two near-identical images, as a four-way multiple choice. We walk through how its 1,756 questions across 10 change categories were built, what 11 models scored, why several of them answer 'no difference' when noise or texture changed, and what this means if you use an MLLM to grade image edits. 10 ·★ MEMBER·PAPER·13 min read Paper Explained: Reason Through the Latent! — Visual information being in a latent state doesn't mean the answer uses it Several latent visual reasoners keep their accuracy even when you corrupt the latent state — the answer is not actually flowing through it. CVRR (arXiv:2609.06746) starts recurrence from the question state after the image has been absorbed, then closes every other image-conditioned route before decoding, making latent computation necessary while keeping performance. A walkthrough grounded only in the paper's text, including its causal interventions. 11 ·★ MEMBER·PAPER·11 min read Paper Walkthrough: SceneMosaic — Physically Valid 3D Rooms from One Image, in Minutes, in Many Variations How to turn a single picture of a room into a simulation-ready 3D scene with zero collisions, in minutes rather than hours, and with several coherent furniture rearrangements thrown in. A walkthrough of how SceneMosaic stitches the speed of image-to-3D models to the precision of VLM agents, with the equations and interactive figures. 12 ·★ MEMBER·PAPER·17 min read Paper Walkthrough: Miles v0.1 — Anatomy of a Post-Training Stack Built to Survive Production A read-through of Miles, a full-stack system for agentic RL post-training of frontier MoE models: how it hunts down the numerical drift between the rollout engine and the trainer, and what a 744B model actually measured on 64 GPUs. 13 ·★ MEMBER·PAPER·12 min read Paper Deep Dive: Mask Forcing — Fixing Autoregressive Video Diffusion Distillation with a Dual-Noise Mask Distilling a bidirectional video diffusion teacher into a real-time autoregressive student tends to produce over-saturated, over-smoothed video. Mask Forcing eases this mode collapse by randomly swapping some tokens in the student's own rollout for slightly cleaner ones — no real data, no extra post-training stage. 14 ·★ MEMBER·PAPER·9 min read Paper Walkthrough: Marigold V2 — Turning an Image Editor Into a State-of-the-Art Depth Estimator on One 32GB GPU How Marigold V2 repurposes an image-editing diffusion transformer (Qwen-Image-Edit) into a state-of-the-art monocular depth estimator using QLoRA and two new losses — explained from first principles through normalization, single-step inference, iREPA, and SinkLoss. 15 ·★ MEMBER·PAPER·8 min read GE-Act 2.0 Paper Explained: Pretraining and Scaling a World-Action Model for Robotic Manipulation GE-Act 2.0 is a world-action model trained from scratch on manipulation data instead of inheriting a video generator. We walk through its single-step visual planner, the KASO rule that keeps only futures compatible with the recorded action, and what 100 zero-shot tasks actually showed. 16 ·★ MEMBER·PAPER·13 min read Paper Walkthrough: ENEAS — Telling Real Instances From Their Doppelgangers Ask for 'person' and you get the statues too. This paper fixes the failure where a mask is geometrically right and semantically wrong, using an embedding filter plus a conditional VLM judge — explained from first principles. 17 ·★ MEMBER·PAPER·10 min read Paper Walkthrough — Beyond Retrieval: LatentStream Turns Retrieved Video Into Latent Memory For never-ending video streams, LatentStream stops appending retrieved evidence as extra context and instead internalizes it into fixed-length latent memory tokens. A ground-up walkthrough of its hierarchical memory, latent evolution, and confidence-driven test-time optimization. 18 ·★ MEMBER·PAPER·9 min read Paper Walkthrough: The Design Fundamentals of Pixel Text Representation Learning An encoder that reads meaning straight off the pixels, never converting glyphs to character codes. This EMNLP 2026 paper argues that what decides its quality is not data volume but four design choices — explained from zero. 19 ·★ MEMBER·PAPER·16 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. 20 ·★ 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. 21 ·★ 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. 22 ·★ 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. 23 ·★ MEMBER·PAPER·14 min read Paper Walkthrough: Eleven Frames Are Enough — Revisiting Local Context for Long-Horizon Streaming 3D Reconstruction How do you track a camera and rebuild a scene from a video that never ends? ABot-Recon throws away long-range memory entirely and bets on the last twelve frames. A ground-up walkthrough of the paper. 24 ·★ MEMBER·PAPER·12 min read Paper walkthrough: Qwen-Drive-1.0 — bolting 3D perception and planning onto a VLM without touching its architecture Leave the pretrained VLM exactly as it is, attach a BEV perception head and a Planning Expert, and you get 3D detection, occupancy, maps and trajectory generation in one model — general vision-language ability nearly intact, 90.7 PDMS on NAVSIM. A ground-up walkthrough, straight from the paper. 25 ·★ MEMBER·PAPER·21 min read Lucida, explained — turning a room video back into parts you can move, by deferring precision to the end A system that rebuilds a real indoor scene from video as individually movable 3D assets. It keeps the parse–generate–place order but redistributes what each step is allowed to demand, and hands the final placement to a VLM policy that operates a 3D editor in a closed loop. 26 ·★ MEMBER·PAPER·14 min read Paper Walkthrough: DreamX-Creator — Making Sound and Picture Together in 7B, Then Finishing at 2K in One Step A ground-up walkthrough of a 7B model that denoises audio and video inside one generative process: the gated cross-modal attention, the modality-aware reinforcement learning, the one-step 2K refiner — and the unusually heavy caveats the authors put on their own results. 27 ·★ MEMBER·PAPER·13 min read Paper Walkthrough: GameWAM — Generating the Next Frame and the Next Keystroke Together A ground-up walkthrough of the first World–Action Model for native closed-loop game and GUI control: how it plans 16 actions but commits only 8, and how low-frequency noise in the sampled action source quietly spins the camera. 28 ·★ MEMBER·PAPER·10 min read Paper Explained: Beyond Data Scaling — Why the Backbone, Not the Trajectory Count, Decides Your VLA (VLAct) Robot trajectories can't be scraped off the web, so the paper argues you should squeeze reusable representations out of the trajectories you do have. Three modest interventions — preserving the VLM prior, co-supervising with multiple action heads, and partially unifying the action space across embodiments — beat industrial VLA systems on fully open data and 16 GPUs. 29 ·FREE·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. 30 ·FREE·PAPER·15 min read Paper Explained: VBVR-Pro — Making "Thinking in Pictures" Trainable, Scorable, and Comparable Native visual reasoning treats image and video generation as the medium of thought. VBVR-Pro supplies 300 procedurally generated tasks and deterministic scorers that replace the VLM judge, then pits image, video, and interleaved generation against each other on identical ground. Written for readers starting from zero. 31 ·FREE·PAPER·11 min read Paper Walkthrough: TLive-Omni — An Omni-Modal Model That Watches, Listens, and Tells You the Timestamp A from-scratch walkthrough of TLive-Omni, an omni-modal understanding model built for e-commerce live streaming: Per-vGrid timestamped audio-video interleaving, a three-stage SFT recipe, and Faithful-RFT, which rewards answer faithfulness instead of visible reasoning — grounded strictly in the paper's own numbers. 32 ·Generative Models·★ MEMBER·PAPER·11 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. 33 ·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. 34 ·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. 35 ·FREE·PAPER·10 min read Paper Walkthrough: GigaBrain-0.7 — 37,000 Hours of Embodied Experience and a Three-System Robot Brain A ground-up walkthrough of GigaBrain-0.7, which splits a robot foundation model into understanding, prediction/evaluation, and action, then pretrains it on 37,256.98 hours of embodied data in a single stage. Covers subgoal-image and progress-value conditioning, Soft Knowledge Insulation, and the real-robot numbers — strictly from the paper. 36 ·FREE·PAPER·15 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. 37 ·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. 38 ·Generative Models·★ MEMBER·PAPER·9 min read Music and Audio Generation from Scratch — Sound as Tokens A three-minute song is more than 15 million numbers. The history of music generation is largely the history of folding that sequence down to something a model can hold. Codec tokens, the fork between autoregressive and diffusion approaches, how text conditioning actually bites, and how to reconstruct the shape of Suno-style products from published parts — no prior knowledge assumed. 39 ·FREE·PAPER·9 min read Paper Walkthrough: Annotations as Rollouts — Dropping the Ground Truth Into the Group as a Ninth Answer RL post-training for video MLLMs starves because sampled rollouts almost never contain the right answer. OraRL adds the annotation itself to the group as an extra rollout, then repairs the advantage inversion that naive mixing causes. 40 ·FREE·PAPER·12 min read 4DAnyone, Explained — Turning One Casual Video Into a 4D Person How 4DAnyone builds a free-viewpoint 4D human from a single phone video, explained from scratch. The core trick is not a better generator but two fixes — RCP and TCR — for a context that no longer fits in one attention pass. 41 ·Generative Models·FREE·10 min read VAEs from Scratch — Stir Probability into "Compress and Restore" and You Get a Generator An autoencoder that only compresses and restores cannot invent anything new. This walks through why a single drop of probability turns it into a generative model — ELBO, the reparameterization trick, and walking the latent space — assuming no prior knowledge. 42 ·FREE·PAPER·11 min read Paper Walkthrough: SemComp-Bench — Asking Video Generators 'So Did It Actually Get Done?' SemComp-Bench moves video-generation evaluation from 'does it look good' to 'did it reach the outcome I asked for'. A walkthrough of the task definition, the four-stage data pipeline, the AND-vs-average scoring, and the measured numbers — all from the paper itself. 43 ·FREE·PAPER·12 min read Paper Review: S²VOPD — Don't Strengthen the Teacher, Blur the Student's View A walkthrough of S²VOPD, which improves vision-language models without labels, rewards, or a stronger teacher. The trick is inverting where the asymmetry comes from: degrade the student's view instead of privileging the teacher's. 44 ·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. 45 ·Generative Models·FREE·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. 46 ·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. 47 ·CNNs & Image Recognition·★ MEMBER·PAPER·8 min read Paper Walkthrough: TurboVLA — Kick the LLM Out of the Loop and Run a Robot Policy at 32 Hz on an RTX 4090 with Under 1 GB of VRAM Mainstream VLA models route every control step through a large language model. TurboVLA removes the LLM and fuses vision and instructions with lightweight bidirectional cross-attention instead — hitting 97.7% on LIBERO with 0.2B parameters, 31.2 ms latency, and 0.9 GB of VRAM. A from-scratch walkthrough of the paper. 48 ·FREE·PAPER·10 min read Paper Deep-Dive: The 'Physics' of Multimodal Pretraining — Which Way Does Knowledge Actually Flow? What happens when a single model learns text and images together from day one? A large-scale controlled study from Meta FAIR and collaborators maps the asymmetric flow of knowledge between modalities, the conditions for synergy, the 'vision laziness' caused by late integration, and a recipe that gets strong image generation from just 5% of the data budget. 49 ·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. 50 ·FREE·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. 51 ·FREE·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. 52 ·FREE·PAPER·8 min read Paper Walkthrough: GST-Bench — Can VLMs Build a Global Map of a Scene from Video? Show a VLM a walkthrough video of a house, then ask 'from where you're standing now, which way is the sofa?' — even the strongest model scores barely half of what humans do. A walkthrough of GST-Bench from ByteDance Seed: the shortcut-proof benchmark design, results across 22 models, and the training data that closed 27 points of the gap. 53 ·FREE·PAPER·9 min read Paper explained: DEFT-RLVR — show a driving VLM the future trajectory too early and it fabricates its reasoning Let a teacher model peek at the ground-truth trajectory while writing chain-of-thought annotations for driving, and severe hallucinations double from 29% to 50% — the paper calls this trajectory anchoring bias. This article walks through AD-MCQ, which turns planning into a verifiable multiple-choice task, and DEFT-RLVR (arXiv:2608.01755), which reveals trajectories only after the model commits to a decision, using nothing but the paper itself as the source. 54 ·FREE·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. 55 ·Generative Models·FREE·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.