#flow-matching

14 articles

01 ·Paper Deep-Dives·★ MEMBER·PAPER·6 min read Paper Explained: Persona Dosing — Asking an LLM for a Personality Trait by the Score A steering coefficient says nothing about how strongly a trait will show up. PersonaDose trains one description-conditioned controller, then measures a strength-to-score map after training, so you can ask for "sycophancy at 60" instead of guessing a number. 02 ·★ 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. 03 ·★ 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. 04 ·★ 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. 05 ·Audio & Speech·★ MEMBER·PAPER·9 min read Paper Walkthrough: AuK — Folding Speech Generation and Editing Into One Model AuK is an open-source foundation model that puts speech generation, editing, enhancement and separation behind one door: a natural-language instruction plus optional audio. A ground-up walkthrough of its data design, MMDiT backbone, rectified flow, human-preference and GRPO post-training, and four-step distillation. 06 ·★ 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. 07 ·Agents·★ MEMBER·PAPER·13 min read Paper Walkthrough: ZimaBlue — Turning 120,000 Hours of Egocentric Video into Robot Skill A ground-up walkthrough of the World Action Model that converts 120,000 hours of action-free egocentric video into robot control: a three-stage curriculum, a 100-D unified action interface, and an asynchronous Slow-Fast pair that takes zero-shot success from 36.1% to 77.8% at a 33 ms control loop. 08 ·★ 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. 09 ·★ 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. 10 ·★ 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. 11 ·Inference & Serving·★ MEMBER·PAPER·13 min read Paper explained: Self-OPD — an image generator that distills itself, with no teacher No teacher model at all: at every denoising step the student spawns K copies of itself, scores them, and pulls toward the good branches while pushing away from the bad ones. A ground-up walkthrough of Self-OPD, an alignment method for flow matching image generators. 12 ·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. 13 ·Audio & Speech·★ MEMBER·PAPER·10 min read Paper Walkthrough: SwanTale — Designing Voices from Words Alone, with Speech and Sound in One Waveform ByteDance's SwanTale handles two jobs in one model: cloning a voice from reference audio (zero-shot) and building voices, ambience, and sound effects purely from a natural-language caption (instruct). We walk through the paper, from the 70M-caption data pipeline to Unified MoE routing and GRPO post-training. 14 ·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.