Yizhou Wang

Fudan alumni, Fifth year Physics PhD student @WUSTL. He/Him/他

[Yizhou Wang @YizhouWang99]

After a year of battling we finally got our paper published on Physical Review Applied! https://t.co/ny87fpL4oM
Looking back I still really like how I tried to approach the problem: spectrum -> Total magnetic field -> Ball-like structure to simultaneous determinations.

[Yizhou Wang @YizhouWang99]

We finally put our work https://t.co/QHVQUTXy4r on arxiv! [Simultaneous Determination of Local Magnetic Fields and Sensor Orientation with Nitrogen-Vacancy Centers in Nanodiamond]
#NVCenter #Quantum #Sensing #physics

[DeepSeek @deepseek_ai]

🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!

Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.

🔗 https://t.co/JGAik1kgk0

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[Yizhou Wang @YizhouWang99]

@iamai_omni 你先给我搞出一个液氮温度下面能够方便的加工制造的材料先把MRI一定要用液氦的问题再物理飞升了又不是所有的超导体应用都可以用现在的高温超导体了我求求你们做AI的了

[Yizhou Wang @YizhouWang99]

@mylifcc 这个工作感觉就是用agent来tackle人类大脑不能tackle的东西���毕竟没几个物理学家聪明到可以一个个bond设计，这么聪明的人也不屑于去做这样tedious又耗脑子的工作，但是另一方面完全可以一个agentic swarm去做这件事情，这才是整件事情体现出来的最重要的点

[Yizhou Wang @YizhouWang99]

@mylifcc 我觉得这些质疑说实话意义都不大，主要困难还是fabrications上，以及怎么去解决整条链上的bottlenecks上面。本来虚拟实验室就是我们必要的东西，和做实验打交道多了之后���子里天天就是跑虚拟实验，搭数字孪生，倒是没什么太奇怪的。重要的是为数不多的实验数据要用来校准虚拟世界。

[Markus J. Buehler @ProfBuehlerMIT]

We built recursive meta-intelligence, an AI that creates its own scientific instruments, turns them into persistent worlds that an agent ecology with hundreds of AIs inhabits, and uses those worlds to discover mechanistic principles in one of the hardest classes of physical problems: how complex hierarchical materials (nested structures of matter that give rise to new function through organization) evolve and fail. The AI reasons across enormous spaces of possible physical trajectories, where every rupture changes what can happen next, and compresses those histories into principles (which humans can understand and design with) - complex chains of causal events, highly nonlinear, and intricate.

Scientific superintelligence is tangible here - machine-scale exploration opening cognitive channels into complexity that has been extremely difficult for humans to traverse directly. AI builds the spaces in which its next level of reasoning becomes possible; a representation becomes an instrument, the instrument becomes an executable world, and that world becomes the substrate for further intelligence. Intelligence then grows by constructing new spaces to think in.

The task we explored started from a seemingly simple prompt to explore a biological material system - the AI then chose the representation, mechanics and experiments, built a fracture laboratory to push materials to their limit, tested hypotheses and generated scientific conclusions. The swarm explored a combinatorial universe in which architecture controls function and every rupture changes the future state of the material.

The AI discovered a compact principle that defines how multiscale material architecture can program the evolution of failure. Material placement and geometric order determine how forces redistribute, whether damage cascades or remains distributed, and whether function survives substantial flaws. For this discovery to happen the AI had to reason across long path-dependent histories, simulate alternative futures and compress them into generative invariants (model-based causal reasoning, counterfactual simulation and temporal abstraction applied to an evolving physical world). It is incredible to witness this transition to a new form of intelligence and capability through scaling swarms.

A lot of positive will come out of this because it expands the human epistemic horizon as AI can traverse thousands of possible histories and return mechanisms compact enough for us to understand, test and build from. Intelligence compounds through its artifacts!

A few lessons we learned:

▶️ Learning and discovery are flows through spaces of possibility. Early work has shown how backpropagation flows through parameters, reinforcement learning through action and consequence, and autonomous swarms through representations, instruments and executable worlds, bringing it all together. Flows create structure; structure redirects future flows in the recursive instrument.

▶️ Nonlinear physics actually defines a larger principle, where high-dimensional dynamics generate stable invariants; invariants become effective variables; those variables become the substrate for a new level of cognition.

▶️ Recursion then becomes level creation - one possibility space compresses into a principle, and that principle opens a larger space above it.

[Yizhou Wang @YizhouWang99]

@abhijeet_d Hasn't that already come? Or did you guys solve hydrogen somehow?

[Yizhou Wang @YizhouWang99]

@MaxForAI 会不会是某种机器狗/鸭？

[Yizhou Wang @YizhouWang99]

@derrickcchoi Maybe a GPT sol/terra 6 with awesome CUA capabilities may be highly appreciated. It is stupid to use astra to do all the things that only needs a functional human brain but not other model has the CUA capabilities of Astra which is bad.

[Yizhou Wang @YizhouWang99]

@ikai_freedom This is a trolling account

[Yizhou Wang @YizhouWang99]

@reach_vb @iuditg Does multiple unnecessary hashes count? Also maybe over defensive behaviors and multiple fallback loops?

[Yizhou Wang @YizhouWang99]

@cherylnatsu 又不是拿几个TB训模型何必上数据中心卡…… 而且也没有���货，RTX 6000 Pro好歹有现货，内存也不至于太慢，推理速度完全够用，96GB不加NVLink训练个小模型也很香

[Yizhou Wang @YizhouWang99]

@btraut Thank you! Have already seen the RAM usage going down by a lot now

[Yizhou Wang @YizhouWang99]

@petergyang Cache miss

[Yizhou Wang @YizhouWang99]

@ajambrosino Literally doing an ablation test on a small sample and the results are astounding.

(Cannot imagine that I write this without an LLM

[Yizhou Wang @YizhouWang99]

@thsottiaux That being said did you get a hair cut or you simply change back to your original pfp w/o getting the haircut?

[Yizhou Wang @YizhouWang99]

@thsottiaux Would we get a reset because you get the haircut?

[QuEra Computing @QueraComputing]

Today QuEra is announcing results from our research preview to the Model Hardware Standard (MHS), begun with Anthropic and HHMI Janelia. An AI agent (Claude) designed, wrote, and validated a controller for a critical function inside our quantum computers: laser locking.

[Yizhou Wang @YizhouWang99]

Congratulations to my friend Jinyuan Wu's first author PRL! Jinyuan has been an extraordinary researcher who is known for his rigorous academic style and extremely trustworthy results. Wish him the best in the process of seeking Postdoc position.

[Physical Review Letters @PhysRevLett]

New theoretical research describes how time-resolved ARPES measurements can identify trions and distinguish between their positive and negative signatures by simulating them in a monolayer transition metal dichalcogenide.

Read the paper: https://t.co/yxHpUl5fpL

[Physical Review Letters @PhysRevLett]

New theoretical research describes how time-resolved ARPES measurements can identify trions and distinguish between their positive and negative signatures by simulating them in a monolayer transition metal dichalcogenide.

Read the paper: https://t.co/yxHpUl5fpL