Yanguang (Carter) Cao

Broad interest in Pharmacokinetics and pharmacodynamics, systems pharmacology, monoclonal antibody, cancer resistance, evolution theory. Opinions are my own

[Itai Yanai @ItaiYanai]

🔥 Published today, and just in time for summer conferences, here are your guidelines for presenting badly

[Yanguang (Carter) Cao @Cartercao2]

@mathoncbro Biological circuits may serve the purpose, math bio models are usually biology system-specific not as generalizable as NN?

[UNC Pharmacy @UNCPharmacy]

Calling all graduate students and post-docs! Join @DPMP_UNC for their Future Leaders Launchpad on May 6!

Take part in networking opportunities, a career blueprint mapping, an interactive industry & academic panel and more!

Register today: https://t.co/gKrwWEk5EC

[Science Translational Medicine @ScienceTM]

An intranasal flu antibody can protect against both #influenza A and B in mice and nonhuman primates and is safe in humans, according to new preclinical experiments and two phase one clinical trials. @annabeuk @HarvardChanSPH https://t.co/prFnYPivIF

[Faheem Ullah @Faheem_uh]

FREE Websites for Downloading PhD Thesis

1. Open Access Theses and Dissertations
↳ https://t.co/tEFUArcsMY

2. Open Thesis
↳ https://t.co/6MiYNaHzcj

3. DART-Europe E-theses Portal
↳ https://t.co/InZwPUDKJ0

4. ProQuest Dissertations & Theses
↳ https://t.co/4owg8BgsCD

5. MIT Theses
↳ https://t.co/qRQJFeoyB3

6. Digital Library of Theses & Dissertations
↳ https://t.co/fVAJEmCAfH

7. Caltech Thesis
↳ https://t.co/8Jw8i4c4qX

8. British Library
↳ https://t.co/N3fEm01YdN

9. Electronic Theses & Dissertation Centre
↳ https://t.co/GltZz8wMAu

10. Harvard DASH
↳ https://t.co/IVySwYe74O

[Carlos E. Perez @IntuitMachine]

I just read a paper that completely broke my brain.

It describes a system that solved an AI task with over 1,000,000 sequential steps... with ZERO errors.

Using AI models that are known to be flaky and make mistakes.

How is that even possible? 🤯

We all know LLMs have an error rate. Even 99.9% accuracy is a death sentence for long tasks.

Imagine you need 1,000 correct steps in a row. With a 99.9% success rate per step, your chance of finishing the whole thing is only ~36%.

At a million steps? Forget it. It's statistically impossible.

So for years, the race has been to build bigger, "smarter" models to get that per-step error rate closer to zero. We're trying to build a perfect genius.

But this paper ("Solving a Million-Step LLM Task with Zero Errors") does the complete opposite. It's a total paradigm shift.

Here's the "holy shit" moment:

Stop trying to make the AI perfect. Instead, build a system that's immune to its imperfections.

How?

Smash the problem into the tiniest possible pieces. (They call it Maximal Agentic Decomposition).

Have a team of simple, cheap AIs vote on the answer for each tiny piece.

It's less like hiring one world-class chef and praying they don't have an off day, and more like designing the McDonald's kitchen.

The system guarantees the burger is the same every time, even if any individual worker could mess up.

The reliability comes from the process, not the person.

They tested this on the Towers of Hanoi puzzle—a classic benchmark where AIs fail spectacularly as the task gets longer.

They set it up for 20 disks. That requires 1,048,575 perfect moves in a row.

(seriously, over a million steps)

A single AI trying this would be a comedy of errors.

But their system of "micro-agents" voting on every single move... nailed it. Flawlessly.

And the plot twist? The most expensive, "state-of-the-art" models weren't even the best for the job. A smaller, cheaper model (gpt-4.1-mini) was more cost-effective because the tasks were so simple.

This is a huge deal for AI safety, too.

A single, god-like AI is a black box. It's unpredictable.

But a system of a million simple agents? You can inspect it. You can audit each step. The agents have no grand "worldview"—their entire existence is to solve one tiny puzzle and then disappear. It's controllable.

So next time you're building something with an LLM, maybe stop asking "how can I prompt the model to be smarter?"

And start asking: "How can I design a system where it's okay for the model to be dumb?"

The real power isn't just in the model. It's in the architecture you build around it.

This isn't just about AI. It's a fundamental lesson in engineering and problem-solving.

You don't always need perfect components to build a perfect machine. You just need a damn good design.

...which makes you wonder what else we're trying to solve by chasing individual perfection instead of building better systems.

[James Zou @james_y_zou]

Causal DAG is a really neat approach to generate high-quality reasoning process reward at scale, without relying on LLM judge! 💯

Great job @WanjiaZhao1203 @AquaHorseM
@ShiJingzhe41415 w/ awesome collaborators����

[Matt Dancho (Business Science) @mdancho84]

🚨 BREAKING: IBM launches a free Python library that converts ANY document to data

Introducing Docling. Here's what you need to know: 🧵

[Massimo @Rainmaker1973]

The fastest land animal

[Swapna Kumar Panda @swapnakpanda]

Stanford's FREE Courses on AI & ML:

❯ CS221 - Artificial Intelligence
❯ CS229 - Machine Learning
❯ CS230 - Deep Learning
❯ CS234 - Reinforcement Learning
❯ CS229M - ML Theory
❯ CS224N - NLP with Deep Learning
❯ CS224U - NL Understanding

Course links inside:

[Journal of Clinical Oncology @JCO_ASCO]

Phase I Study of 131I-Metaiodobenzylguanidine With
Dinutuximab 6 Vorinostat for Patients With Relapsed or
Refractory #Neuroblastoma: A New Approaches to
Neuroblastoma Therapy Trial.

Learn more. https://t.co/iZtErWXKeT

[Matt Dancho (Business Science) @mdancho84]

Bayes' Theorem is a fundamental concept in data science.

But it took me 2 years to understand its importance.

In 2 minutes, I'll share my best findings over the last 2 years exploring Bayesian Statistics. Let's go.

[Massimo @Rainmaker1973]

Physics is fun

[📹 MetDaan]

[Massimo @Rainmaker1973]

A closeup of how a mosquito sucks blood from the human body.

[Massimo @Rainmaker1973]

How to draw in 3D using two coupled pencils.

[Science girl @sciencegirl]

Graph theory model the Rubik's Cube

[Uri Alon @UriAlonWeizmann]

Our latest on aging, where we reveal an unexpected link between healthspan and equality of lifespans.
By @y1fanyang, out now @NatureComms

[Science girl @sciencegirl]

This illusion where you see a 3 dimensional cube even though it’s not there,

Anamorphosis

📹Sonhos com Dimensão

[Nassim Nicholas Taleb @nntaleb]

Always check by Monte Carlo

[Frontiers - Immunology @FrontImmunol]

New Research: Guselkumab binding to CD64+ IL-23–producing myeloid cells enhances potency for neutralizing IL-23 signaling https://t.co/GSrw9ALRul #FrontiersIn #Immunology