Mingyang Deng PhD candidate@MIT
Joined August 2022
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Goodeat retweeted
Meet Pika H3 Flex, debuting today as the #1 open weight model on DesignArena’s t2v board.
It’s built for professional iteration, with exportable audio stems, resolutions up to 4k, and faster-than-realtime generation. It can produce a 15-second clip with soundtrack in as little as 7 seconds.
So, yeah. It’s kind of a flex 💅
Goodeat retweeted
VISTA for ARC-AGI-3 is open source!
Code: github.com/joshhhhhan/VISTA
How does a model understand and explore a world it has never seen?
We introduce 🔭VISTA🔭, a visual harness that gives a VLM long-horizon vision for reasoning in an interactive world. With Claude Opus 5.0, it reaches 100% RHAE on @arcprize's ARC-AGI-3, perfectly solving all 25 public games.
Blog post: vista-research.github.io/ 🧵
Goodeat retweeted
We share our early research on building Software World - a "GitHub" run by agents. We deploy agents for Python packages in a dependency chain, and each agent is tasked with collaborating with others and optimizing the package it owns.
Goodeat retweeted
Diffusion/flow matching is a go-to generative AI paradigm, but it's slow, sample inefficient and complicated. Do we really need all this complexity?
We design a small and simple one-step model, ROMS-IMLE, which attains an FID of 2.56 on ImageNet.
Joint w/ @researchirag
1/6
Goodeat retweeted
The first long-horizon visual harness to achieve a perfect 100% score on ARC-3! 🚀
Excited to share our latest work from Kaiming’s group. Huge thanks to my incredible co-authors Josh and Linlu!
How does a model understand and explore a world it has never seen?
We introduce 🔭VISTA🔭, a visual harness that gives a VLM long-horizon vision for reasoning in an interactive world. With Claude Opus 5.0, it reaches 100% RHAE on @arcprize's ARC-AGI-3, perfectly solving all 25 public games.
Blog post: vista-research.github.io/ 🧵
We’ve released the code for Drifting Models :)
Includes full training, inference, and pretrained weights.
Curious to see what people build on top of this.
github.com/lambertae/driftin…
Thanks for the highlight, Zhengyang!
Huge credit to my collaborators He Li (@lihe50hz), @TianhongLi6, @du_yilun, and my advisor Kaiming He.
In particular, He Li is exceptional and is currently applying for PhD programs.
A new paradigm & member toward 1-step & e2e generative modeling!
Great work by @Goodeat258 Mingyang!!!
cannot be more excited to read
me: learning to drift with my spindrift.
arxiv.org/abs/2602.04770
Goodeat retweeted
New research with @AdtRaghunathan, Nicholas Carlini and Anthropic!
We built ImpossibleBench to measure reward hacking in LLM coding agents 🤖, by making benchmark tasks impossible and seeing whether models game tests or follow specs. (1/9)
Goodeat retweeted
Excited to share our work with my amazing collaborators, @Goodeat258, @SimulatedAnneal, @zicokolter, and Kaiming.
In a word, we show an “identity learning” approach for generative modeling, by relating the instantaneous/average velocity in an identity. The resulting model, termed MeanFlow, demonstrates the best 1-step generative performance so far as a standalone model trained from scratch.
It exploits a simple fact by the Fundamental Theorem of Calculus: differentiation and integration are inverse operators. This creates a "complexity-gap," differentiation-integration gap: differentiation is easier than integration. Much like the verification-generation gap, we are able to unlock fully tractable learning thanks to the beautiful “asymmetry” in nature/math.
No pre-training/post-training. No distillation. No curriculum.
Simple identity. Simply training from scratch.
Please check out the paper for the best clarity : )
📜Paper: arxiv.org/abs/2505.13447
Goodeat retweeted
Mitigating racial bias from LLMs is a lot easier than removing it from humans!
Can’t believe this happened at the best AI conference @NeurIPSConf
We have ethical reviews for authors, but missed it for invited speakers? 😡
Goodeat retweeted
In diffusion models, samplers are primarily ODE-centric, overlooking slower stochastic methods. However, we show that stochastic sampler can outperform previous samplers on Stable Diffusion, if we use stochasticity correctly!
check out Restart Sampling: arxiv.org/abs/2306.14878