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I will defend my PhD thesis, “Physics-Constrained Generative Models for Computational Design,” on Tuesday, September 8, 2026, at PM ET. Location: MIT MIT CSAIL 32-G449 Zoom: Committee: Kaiming He, Bill Freeman, Wojciech Matusik Generative models can now propose candidate designs at high throughput, yet the physical world remains the... show more

Minghao Guo

1,332 subscribers

78,288 просмотров • 11 дней назад •via X (Twitter)

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Комментарии: 36

Фото профиля Robert Scoble

Robert Scoble11 дней назад

@MIT_CSAIL Your work caught my eye. Thank you and knock it out of the park!

Фото профиля Minghao Guo

Minghao Guo11 дней назад

Relying on downstream validation creates a fundamental trade-off between throughput and physical realizability: generating a candidate may take seconds, while validating it can take hours to months. Physical realizability is a scaling problem, not just a quality problem. (2/7)

Фото профиля Minghao Guo

Minghao Guo11 дней назад

Inspired by Marr’s separation of levels of analysis, we organize generative computational design into three levels: physical, computational, and generative. Because each level abstracts the one below, physical requirements can be weakened or lost at the interfaces between them. (3/7)

Фото профиля Minghao Guo

Minghao Guo11 дней назад

This thesis treats physical requirements as first-class citizens of the generative system. The central idea is to carry those requirements bottom-up through explicit interfaces, from the physical world through computation and into generation. We formulate computational design as constrained optimization. (4/7)

Фото профиля Minghao Guo

Minghao Guo11 дней назад

We realize these interfaces through three core elements: representation, constraints, and objective evaluation. At the representation interface, procedural molecular grammars, medial skeletons, and tetrahedral sphere primitives define candidate spaces that are valid by construction. At the constraint interface, differentiable projection modules enforce physical feasibility during generation. (5/7)

Фото профиля Minghao Guo

Minghao Guo11 дней назад

At the objective interface, unified simulators evaluate design behavior with scientific fidelity at generative throughput, spanning physical regimes from molecular dynamics to aerodynamics. Together, these contributions lay the foundation for universal precision design: general-purpose systems that tailor physically realizable designs to the specific requirements of a problem, individual, or environment, at scale. (6/7)

Фото профиля Minghao Guo

Minghao Guo11 дней назад

A research map of selected publications across my PhD study: #GenerativeAI #AIforScience #ComputationalDesign #ScientificMachineLearning #PhDDefense #worldmodel

Фото профиля Minghao Guo

Minghao Guo11 дней назад

Missing the time haha! It’s 2-3pm ET!

Фото профиля 중심

중심11 дней назад

@MIT_CSAIL at what time?

Фото профиля BrenJ

BrenJ11 дней назад

@MIT_CSAIL @evelovesolive @beffjezos felt like this was up y'alls alley.

Фото профиля DrKnowItAll

DrKnowItAll11 дней назад

@MIT_CSAIL Great work! This looks really impressive.

Фото профиля rob

rob11 дней назад

@MIT_CSAIL Great stuff here.

Фото профиля samir sawarkar

samir sawarkar11 дней назад

@MIT_CSAIL The next leap in generative AI isn’t generating more designs. It’s generating designs that cannot violate the physics. That’s the shift from “AI can imagine it” to “AI can engineer it.”

Фото профиля Aman Chourasia

Aman Chourasia11 дней назад

@MIT_CSAIL This is great!

Фото профиля Daniel Samanez

Daniel Samanez11 дней назад

@MIT_CSAIL 👍 at what time is it?

Фото профиля Ralf Sigmund🇺🇦🇪🇺 @sistar-hh.bsky.social

Ralf Sigmund🇺🇦🇪🇺 @sistar-hh.bsky.social10 дней назад

@MIT_CSAIL Congrats. It was a honor to listen to You leading us through Your work. All the best!

Фото профиля Xenonostra'

Xenonostra'11 дней назад

@MIT_CSAIL I wish you the best of luck I'm sure you will do well

Фото профиля GSC

GSC11 дней назад

@MIT_CSAIL

Фото профиля Zhiyuan

Zhiyuan11 дней назад

@MIT_CSAIL solid work! may you good luck!

Фото профиля Yuxi Xiao

Yuxi Xiao11 дней назад

@MIT_CSAIL Wow nice video for the phd thesis!!! congrats

Фото профиля Big data annotation

Big data annotation11 дней назад

@MIT_CSAIL 大佬，牛逼

Фото профиля Raj Kachhadiya (KARAM)

Raj Kachhadiya (KARAM)11 дней назад

@MIT_CSAIL Interesting, congratulations!

Фото профиля Anand Kumar

Anand Kumar11 дней назад

@MIT_CSAIL Interesting. Looking forward to it.

Фото профиля Neil

Neil8 дней назад

@MIT_CSAIL Wow this is such an interesting research direction, do you think the future of 3D is headed this way?

Фото профиля Yossi Eliaz

Yossi Eliaz11 дней назад

@MIT_CSAIL loved the video!

Фото профиля likhesh

likhesh11 дней назад

@MIT_CSAIL Amazing, what time exactly?

Фото профиля Mandar Wagh

Mandar Wagh10 дней назад

congrats, and that is a committee. the split i keep getting stuck on: physics constraints are mostly differentiable, so they go straight into a loss. manufacturing constraints mostly are not — tool reach, support-free overhang, demoldability, minimum wall are combinatorial, and you cannot gradient your way to a draft angle. does the thesis treat those as one kind of constraint, or as two different problems? genuinely curious which way you landed.

Фото профиля the guy

the guy11 дней назад

@MIT_CSAIL 🧡

Фото профиля Jesse Jr Lim (林振燊)

Jesse Jr Lim (林振燊)11 дней назад

@MIT_CSAIL wow very cool.. paused the vid alot to see whats going on

Фото профиля X.M.

X.M.11 дней назад

@MIT_CSAIL all the best king

Фото профиля Volatile Markets

Volatile Markets11 дней назад

@MIT_CSAIL Love this!

Фото профиля Verdi

Verdi11 дней назад

@MIT_CSAIL congrats!! you didnt share a time tho? > "on Tuesday, September 8, 2026, at PM ET."

Фото профиля Jacques Mₜ=∏ₛ((1−λₛ)+λₛEₛ)

Jacques Mₜ=∏ₛ((1−λₛ)+λₛEₛ)11 дней назад

@MIT_CSAIL PhaGGOT

Фото профиля Curtis

Curtis10 дней назад

@MIT_CSAIL Grest work, great slides, great marketing video!

Фото профиля Black Canvas

Black Canvas11 дней назад

@MIT_CSAIL Wish you the best and interesting work!

Фото профиля Supratik Bhattacharya

Supratik Bhattacharya11 дней назад

@MIT_CSAIL Wow! this is super interesting. all the best!

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Startup Archive

316,188 просмотров • 1 год назад

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

MTS

11,676 просмотров • 4 месяцев назад

Demis Hassabis on the limit in today’s AI: language can describe the world, but it cannot contain it - and why "World Models" are his "longest standing passion". Language models absorbed far more structure about reality from text than many researchers expected, because human language quietly carries physics, psychology, culture, tools, plans, and cause-and-effect. But text is still a compressed residue of experience, not experience itself. A sentence can say a cup falls from a table, yet it does not fully encode weight, grip, balance, friction, timing, sound, surprise, or the tiny motor corrections a body makes before it even notices them. The world is not only made of facts that can be named; it is made of constraints that have to be lived through, touched, predicted, violated, and repaired. That is why world models matter. They aim to learn the hidden grammar of physical reality: how objects persist, how forces unfold, how space changes when an agent moves, and how action creates feedback. Language models can often reason about the world because people have written so much about it. World models try to learn what the world is like before it becomes words. The difference is exactly what matters because intelligence is not just answering well; it is knowing what would happen next if you moved, reached, pushed, smelled, slipped, or failed. A mind trained only on descriptions may become brilliant at explanation. A mind trained on experience may become better at consequence. --- Full video from "Google DeepMind" and "Hannah Fry" YT channel (link in comment)

Demis Hassabis on the limit in today’s AI: language can describe the world, but it cannot contain it - and why "World Models" are his "longest standing passion". Language models absorbed far more structure about reality from text than many researchers expected, because human language quietly carries physics, psychology, culture, tools, plans, and cause-and-effect. But text is still a compressed residue of experience, not experience itself. A sentence can say a cup falls from a table, yet it does not fully encode weight, grip, balance, friction, timing, sound, surprise, or the tiny motor corrections a body makes before it even notices them. The world is not only made of facts that can be named; it is made of constraints that have to be lived through, touched, predicted, violated, and repaired. That is why world models matter. They aim to learn the hidden grammar of physical reality: how objects persist, how forces unfold, how space changes when an agent moves, and how action creates feedback. Language models can often reason about the world because people have written so much about it. World models try to learn what the world is like before it becomes words. The difference is exactly what matters because intelligence is not just answering well; it is knowing what would happen next if you moved, reached, pushed, smelled, slipped, or failed. A mind trained only on descriptions may become brilliant at explanation. A mind trained on experience may become better at consequence. --- Full video from "Google DeepMind" and "Hannah Fry" YT channel (link in comment)

Rohan Paul

49,938 просмотров • 3 месяцев назад

Working on a 3D/Pixel Art tileset to use with my custom level design tools Tiles can be textures or entire models, like on the larger walls (grass, moss and other details are procedural) At the end of the video I show how it looks in-game #gamedev #indiegame #lowpoly #pixelart

Working on a 3D/Pixel Art tileset to use with my custom level design tools Tiles can be textures or entire models, like on the larger walls (grass, moss and other details are procedural) At the end of the video I show how it looks in-game #gamedev #indiegame #lowpoly #pixelart

Tiani Pixel | Follow me on BSky!

75,960 просмотров • 2 лет назад

Can Minnesota Go Red? Minnesota Senate candidate Michele Tafoya on Somali fraud, ICE ops, the SAVE Act, and U.S. hockey Gang, 2026 will be a pivotal year for the nation’s future. Can the Republicans hold the Congress? Will ICE deportations continue? And: How many of our tax dollars have been leaving our country due to various frauds? You know Michele Tafoya from Gutfeld! and other Fox shows — as well as her previous years as a sideline reporter in numerous other sports. At this writing, she is the leading candidate for the Republican candidate for senate in Minnesota. Can she pull it off? And if so… can she turn a Minnesota Senate seat red? In a state that seems to be at the point of the spear for so many of the nation’s 2026 controversies? Join us for a fun conversation with Michele Tafoya, Republican candidate for Senate from Minnesota (who also happens to be very funny…!). So click here for a sizzle... and subscribe for the full pod.

Can Minnesota Go Red? Minnesota Senate candidate Michele Tafoya on Somali fraud, ICE ops, the SAVE Act, and U.S. hockey Gang, 2026 will be a pivotal year for the nation’s future. Can the Republicans hold the Congress? Will ICE deportations continue? And: How many of our tax dollars have been leaving our country due to various frauds? You know Michele Tafoya from Gutfeld! and other Fox shows — as well as her previous years as a sideline reporter in numerous other sports. At this writing, she is the leading candidate for the Republican candidate for senate in Minnesota. Can she pull it off? And if so… can she turn a Minnesota Senate seat red? In a state that seems to be at the point of the spear for so many of the nation’s 2026 controversies? Join us for a fun conversation with Michele Tafoya, Republican candidate for Senate from Minnesota (who also happens to be very funny…!). So click here for a sizzle... and subscribe for the full pod.

Paul Mauro

23,268 просмотров • 6 месяцев назад

My conversation with Kanishka Rao, who went from working on particle physics at CERN to driving robotics at Google DeepMind. We get into the Gemini Robotics 2 release, trending training recipes in the industry, physical AGI, world models, and more. 1:00 Intro 1:47 Does AI's pace feel crazy? 4:04 From particle physics PHD to robotics 6:03 Speech recognition's deep learning moment 8:43 Whole-body humanoid control 10:47 The trash-bag tying demo 13:07 The robotics data pyramid 14:13 Transferring skills across robots 16:30 How does Motion Transfer work 16:56 Why a separate reasoning model 19:02 Can distillation work in robotics? 20:02 The ASIMOV safety benchmark 22:47 VLAs vs world models 25:10 The missing tactile sensing piece 27:48 How far to physical AGI? 30:07 Fundamental breakthrough needed? 31:53 World models for evaluation 33:45 Will the model layer commoditize? 35:11 Humanoid as a form factor

My conversation with Kanishka Rao, who went from working on particle physics at CERN to driving robotics at Google DeepMind. We get into the Gemini Robotics 2 release, trending training recipes in the industry, physical AGI, world models, and more. 1:00 Intro 1:47 Does AI's pace feel crazy? 4:04 From particle physics PHD to robotics 6:03 Speech recognition's deep learning moment 8:43 Whole-body humanoid control 10:47 The trash-bag tying demo 13:07 The robotics data pyramid 14:13 Transferring skills across robots 16:30 How does Motion Transfer work 16:56 Why a separate reasoning model 19:02 Can distillation work in robotics? 20:02 The ASIMOV safety benchmark 22:47 VLAs vs world models 25:10 The missing tactile sensing piece 27:48 How far to physical AGI? 30:07 Fundamental breakthrough needed? 31:53 World models for evaluation 33:45 Will the model layer commoditize? 35:11 Humanoid as a form factor

The Humanoid Hub

22,037 просмотров • 1 месяц назад

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.

abdel

17,001 просмотров • 4 месяцев назад