
Co-founder / CEO of @TrillionLabs.
대한민국
Joined December 2012
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Jay Shin retweeted
Replying to @makermodsai
@makermodsai x @dreamlabs_ai hackathon this weekend! 🤖
For 48 hours, every participant gets free Fine-tuning-as-a-Service (FaaS) credits (normally $200 per run). Collect data on your own task, fine-tune a custom Dream-1 checkpoint, and see what it can do!
We're hosting another Physical AI Hack this weekend, November 7–8 at @fdotinc in San Francisco.
This time, we're partnering with @dreamlabs_ai and their new world action model, Dream-1, which can do tasks zero-shot. @livekit @nvidia @huggingface @dimensionalos @enact_labs are joining us too.
@livekit is the real-time layer: connect your robot to human operators and cloud models with ultra low latency, then give it a voice agent so you can talk to it. You'll get LiveKit credits for real-time streaming and voice agents.
Other technical partners: @_lightcompany @mirdainsystems @SGhashghaei @spcxstarship
Prizes:
1st place: $2,000 + a Metal Arm + credits
2nd place: $1000 + a Maker Arm + credits
3rd place: $500 + a Reachy Mini + credits
Sign Up: luma.com/pah
There are two tracks:
Hill climb (New Track):
This is a new format where everyone works to improve the model's accuracy on the same task. We'll announce the task soon and provide the props. You'll climb the leaderboard through fine-tuning, DAgger and a System 2 agent harness around Dream-1, using LiveKit and Dimensional OS.
Creative (Zero shot):
Build the most creative project you can with Dream-1, LiveKit and Dimensional OS.
Nice work! Looks relveant to our previous work rbridge
trillionlabs.co/research/rbr…
arxiv.org/abs/2509.21013
Go Dream!
Today, Dream Labs is out of stealth.
In February 2026, while we were at NVIDIA, @scott_e_reed @youliangtan @GeYunhao and I started calling a new kind of robot model a World Action Model, one that learns how the world moves from video, then uses that to decide how to act.
Today, we’re releasing Dream-1 through a public API that anyone in the world can use.
When you rent a car you’ve never driven, you don’t learn to drive all over again. Within a few minutes, it feels like your own. Robots should work the same way: a good model should be able to pick up a new robot and get to work, without being retrained from scratch.
That’s what we’ve spent the last 6 months on, and it’s what Dream-1 does. It controls bimanual robots with parallel grippers it has never seen before, zero-shot, on unseen tasks in unseen environments.
Generalization has been my North Star in robotics: first by distilling video world models into VLAs (DreamGen), then by learning to act from those world models directly (DreamZero), and now by transferring across robots (Dream-1). I’ve come to believe it’s the only direction that lasts. Pushing a narrow model from 90% to 99.9% on a single task is real work, but much of it gets washed away once a model that generalizes arrives.
Since the start of the company we’ve stayed single-minded about generalization.That focus is what got Dream-1 to control robots it was never trained on, on tasks it was never shown. And it’s what makes a public API possible at all: a model that only does one task on one robot can’t serve millions of users.
Dream-1 is still a research beta, and it will make mistakes. But it already drives cars it’s never seen.
Generalization wins in the end, and the end is closer than it looks.
Here’s Dream-1 taking the wheel of a robot it’s never seen. Full details 👇
Big congrats to @jang_yoel & @dreamlabs_ai on a $90M seed! Happy to be the first in supporting the team that coined the term “World Action Models”.
Jay Shin retweeted
Our paper is live on huggingface daily papers! Please leave an upvote if you found it interesting 😊
huggingface.co/papers/2610.1…
🧪Can AI agents take a scientific paper and write a meaningful follow-up to it?
We introduce Into the Unknown, an AI agent benchmark of 15 open scientific research problems. Each problem comes from a scientist currently or formerly at US DOE National Labs:
@LosAlamosNatLab @SandiaLabs @BerkeleyLab @AmesNatLab @PPPLab @Livermore_Lab
Banger paper. Everyone fails
🧪Can AI agents take a scientific paper and write a meaningful follow-up to it?
We introduce Into the Unknown, an AI agent benchmark of 15 open scientific research problems. Each problem comes from a scientist currently or formerly at US DOE National Labs:
@LosAlamosNatLab @SandiaLabs @BerkeleyLab @AmesNatLab @PPPLab @Livermore_Lab
New ai4science benchmark that every frontier models fails by @seungonekim
Jay Shin retweeted
Assorted things I've been mulling over regarding agents in the economy:
• Companies are in some sense the original superintelligence. It's often the case that they can use their amortized cognitive surplus to advance their interests against those of certain consumers: relative to the optimization capacity of companies, consumers can be inattentive, underinformed, irrational, etc. (Think, say, of some of the classic insurance upsells at the end of a booking process, which is typically priced substantially above what a regular policy would cost.) Herbert Simon introduced the idea of bounded rationality, and firms often end up benefiting from it. Thanks to personal agents, we will probably get somewhat more rational in how we engage with companies.
• How exactly will outcomes change? Well, certain kinds of price discrimination will work less well (how do coupons work if every agent hunts fastidiously for them?), and certain kinds of customer cross-subsidy will change (today, people who forget to cancel subscriptions subsidize those who don't). Taken across the economy as a whole, I'm not sure what the incidence of all of this will be.
• In corporate strategy, people have long debated the relative importance of distribution and product quality. Perhaps you've made something better, but does anyone know about it? Won't people just continue to buy the ACME Corp Mousetraps? Personal agents will, I think, act as a kind of structural subsidy for product quality. How would markets look different if individuals always spent at least 10 hours researching their purchases? On the margin, focusing on making a product better is, I think, going to become a more effective strategy for companies. This is good!
• It true, a corollary is that information about product quality will become more important and impactful. Today, most people have very limited ability to aggregate and integrate such data, but agents will have insatiable thirst for such signals. (It's often said that data is becoming more valuable because it helps in the training of models. That's obviously true, but I think there's a secondary way in which it's more valuable simply as a complement to cheaper intelligence.)
• A big question is whether agents will increase superstar effects (rational agents all settle on the same product) or increase dispersion (because everyone has slightly different tastes and preferences, which agents are good at eliciting, finally overcoming the artificial clustering we've been subjected to).
• Many internet companies have monetized demand routing, which is in turn often predicated on short attention spans. "I'll click on the first thing I see." This has led to some amount of inefficiency, as companies try to propitiate capricious ranking algorithms and incur various placement fees. It's not all deadweight loss (willingness to pay is itself a kind of signal, which is valuable to buyers), but it's not great. How will the nature and value of this routing change in a world of personal agents? I don't know the answer, but I think that the effects might be pretty big.
• Much of what I'm writing hinges on the assumption that personal agents will be on the consumer's side. I suspect that that will be the winning strategy, but whether it's true, and what it means to be true, involves a lot of other questions.
Taken as a whole, the market is a massively parallelized, decentralized, and highly imperfect value attribution machine, and, taking stock of all of the effects, it seems to me that the quality point may well be the biggest: that agents will change the nature of the backpropagation such that the rewards for better products increase. Maybe this is too optimistic, but I suspect (and hope) that companies that make things that they're proud of will feel that the universe is a little more partisan in their favor.
Jay Shin retweeted
What happens to the direction of science when we delegate it to AI?
With @seungonekim, we ranked all 6,617 #ICML2026 papers with AI—the AI Awards.
Findings:
1⃣Humans & AI disagree. Significantly.
2⃣AI is highly influenced by online hype.
3⃣AI preferred papers w/ perceived impact and generalization across settings, whereas humans preferred novelty.
4 AIs agreed fairly highly with one another.
🔗 research-taste.github.io/ai-…
1/
Jay Shin retweeted
1/ I’m trying to read more research reports from neo-labs to get a better feel for what is happening on the fundamental ML side.
[1] Spotlight Memory from Percepta.
The core question is simple:
Can a model’s memory keep growing without increasing the computational cost of each memory access?
Jay Shin retweeted
Visiting SF for COLM and curious what SF is all about?
The bad news: COLM is in the dodgiest neighborhood and if you hang around there, you’ll hate SF just like I used to.
The good news: your twitter buddy Jason Wei wrote a guide on experiencing SF like a local: jasonwei.net/blog/the-best-t…
Jay Shin retweeted
<사이버보안도 우리 AI가 필요합니다>
오늘 네이버클라우드 컨소시엄과 사이버보안 특화 AI 모델 개발의 첫걸음을 함께했습니다.
AI가 발전할수록 사이버 공격도 빠르고 정교해집니다. 그렇다고 AI의 속도를 늦출 수는 없습니다. 위협에 대응하는 우리의 기술을 더 빠르게 발전시켜야 합니다.
해외 프론티어 AI와 협력하되, 국가의 중요한 정보와 시스템을 지킬 AI 역량은 우리 스스로 갖춰야 합니다. 이것이 제가 생각하는 AI 보안주권입니다.
이번 사업에는 41개 기관이 힘을 모았습니다. 4,512장의 GPU와 보안 현장의 데이터·경험을 바탕으로 공격과 방어에 특화된 우리 AI 모델을 개발합니다.
중요한 것은 성능을 넘어 실제 현장에서 쓰이는 것입니다. 국가 핵심시설부터 중소기업까지 우리 기술로 지킬 수 있어야 합니다.
정부가 판을 만들었다면 이제 기업이 주인공입니다. 세계가 선택할 수 있는 보안 AI를 만들어낼 수 있도록 정부도 끝까지 뒷받침하겠습니다.
#AI #사이버보안 #보안특파모 #독파모 #과기정통부
naver.me/xXnkO8VE
Banger paper from our super intern @itsnamgyu
Banger paper from Google DeepMind and colleagues.
(bookmark it)
A model reads its entire KV cache on every generated token, even though it ends up attending to a tiny slice of it.
In other words, if you ask about one detail from a 1M-token conversation the global attention layers re-read all of it, per token.
The usual fix is to guess the relevant tokens first with cheap proxy scores, which still costs O(N) every step. Declarative Attention asks the model instead.
The model declares where it needs to look, inside its own chain-of-thought.
In this way, generation splits into three modes: global reads the full context, focus reads one specific region, and local reads only recent output.
The inference engine parses those declarations the same way it parses tool calls and skips most of the cache read.
On zero-shot on off-the-shelf weights across 15 long-context tasks, attended tokens during decoding drop 52.0% on Gemma-4-31B and 31.1% on Qwen-3.6-27B.
Paper: arxiv.org/abs/2609.02737
Chat with Paper: academy.dair.ai/papers/langu…
Jay Shin retweeted
South Korea has once again established a clear #3 position in the global AI race, trailing only the United States and China. The depth and vibrancy of Korea’s AI ecosystem is supported by strong domestic talent, public investment, and the incentives created by Korea’s Sovereign AI Foundation Model project
A number of South Korean labs, including Motif Technologies, Upstage, SK Telecom, and LG AI Research, have now scored above 30 on the Artificial Analysis Intelligence Index. This concentration of competitive model developers highlights the growing density of Korea’s domestic AI ecosystem. Among them, Motif Technologies and Upstage have models scoring above 40, making them the highest-scoring models on the Intelligence Index developed outside the United States and China.
Leading models from each lab include:
➤ Motif Technologies’ Motif 3 scored 47 on the Intelligence Index (314B total parameters, 13B active; open weights).
➤ Upstage’s Solar Pro 4 scored 42 on the Intelligence Index. It is a proprietary reasoning model.
➤ SK Telecom’s A.X K2 scored 35 on the Intelligence Index (692B total parameters, 33B active; open weights).
➤ LG AI Research’s K-EXAONE 2.0 scored 31 on the Intelligence Index (750B total parameters, 37B active; open weights).
Outside the competition, Korea’s model-development ecosystem has continued to broaden. Trillion Labs, Korea Telecom (KT), and other AI labs are building out their own model families, adding to a domestic field that is considerably deeper and more competitive than it was a year ago.
Jay Shin retweeted
어제 국무회의에서 독자 AI 파운데이션 모델의 성과를 보고드리며, 대한민국이 미국·중국과 함께 글로벌 AI 3강으로 자리 잡고 있다고 말씀드렸습니다.
지난 1월 스탠포드 ‘주목할만한 AI’ 3위에 이어 불과 한 시간 전, 글로벌 AI 모델 평가기관 Artificial Analysis가 대한민국을 미국과 중국에 이어 명확한 3위(clear third)라고 평가했습니다.
더 반가운 것은 한두 기업만의 성과가 아니라는 점입니다. Motif 3, Upstage Solar Pro 4, SKT A.X K2, LG K-EXAONE 2.0 등 독파모 참여 기업 이외에도 한국의 트릴리온랩스, KT도 언급이 되고 있습니다.
미국과 중국을 제외하면 가장 높은 수준의 모델들도 우리나라에서 나오고 있습니다.
독자 AI 모델 프로젝트를 시작한 이유는 하나의 국가대표 모델을 만드는 것이 아니라, 여러 기업이 서로 경쟁하고 성장하며 세계와 겨룰 수 있는 두터운 AI 생태계를 만들자는 것이었습니다.
물론 3위라는 순위에 만족하기에는 아직 갈 길이 멉니다. 더 좋은 모델을 만들고, 산업과 국민이 실제로 많이 쓰는 AI로 발전시켜야 합니다.
그래도 오늘은 우리 연구자와 기업들의 노력에 박수를 보내고 싶습니다. 대한민국 AI, 이제 제대로 세계와 경쟁하고 있습니다.
#독파모 #ai3강 #motif #Skt #lg #upstage
South Korea has once again established a clear #3 position in the global AI race, trailing only the United States and China. The depth and vibrancy of Korea’s AI ecosystem is supported by strong domestic talent, public investment, and the incentives created by Korea’s Sovereign AI Foundation Model project
A number of South Korean labs, including Motif Technologies, Upstage, SK Telecom, and LG AI Research, have now scored above 30 on the Artificial Analysis Intelligence Index. This concentration of competitive model developers highlights the growing density of Korea’s domestic AI ecosystem. Among them, Motif Technologies and Upstage have models scoring above 40, making them the highest-scoring models on the Intelligence Index developed outside the United States and China.
Leading models from each lab include:
➤ Motif Technologies’ Motif 3 scored 47 on the Intelligence Index (314B total parameters, 13B active; open weights).
➤ Upstage’s Solar Pro 4 scored 42 on the Intelligence Index. It is a proprietary reasoning model.
➤ SK Telecom’s A.X K2 scored 35 on the Intelligence Index (692B total parameters, 33B active; open weights).
➤ LG AI Research’s K-EXAONE 2.0 scored 31 on the Intelligence Index (750B total parameters, 37B active; open weights).
Outside the competition, Korea’s model-development ecosystem has continued to broaden. Trillion Labs, Korea Telecom (KT), and other AI labs are building out their own model families, adding to a domestic field that is considerably deeper and more competitive than it was a year ago.



























