Mohamed S. Baloul, MD

🩺 Proud and excited for my new rank as Assistant Professor of Medical Education. A heartfelt thanks to everyone who supported me along the way - family, mentors, and colleagues. Looking forward to contributing to the future of medical education. @MayoSurgery @MayoSurgEd

Blessed to have matched at my top and favorite program, University of Wisconsin! @uw_medicine. Thank you to all my family and mentors who made this possible! #Match2026

We are excited to welcome our next incoming general surgery residents! Welcome to the Mayo Clinic family! #gensurgmatch2026 #mayo #youlooklikeasurgeon #leftglovefirst

Introducing 𝑨𝒕𝒕𝒆𝒏𝒕𝒊𝒐𝒏 𝑹𝒆𝒔𝒊𝒅𝒖𝒂𝒍𝒔: Rethinking depth-wise aggregation. Residual connections have long relied on fixed, uniform accumulation. Inspired by the duality of time and depth, we introduce Attention Residuals, replacing standard depth-wise recurrence with learned, input-dependent attention over preceding layers. 🔹 Enables networks to selectively retrieve past representations, naturally mitigating dilution and hidden-state growth. 🔹 Introduces Block AttnRes, partitioning layers into compressed blocks to make cross-layer attention practical at scale. 🔹 Serves as an efficient drop-in replacement, demonstrating a 1.25x compute advantage with negligible (<2%) inference latency overhead. 🔹 Validated on the Kimi Linear architecture (48B total, 3B activated parameters), delivering consistent downstream performance gains. 🔗Full report: github.com/MoonshotAI/Att…

Here is our updated database of grants for early careers researchers in all fields. It goes way beyond traditional NIH and NSF funding opportunities. We list 428 types of grants. Download it here: research.jhu.edu/rdt/funding-op…

For the 8th straight year, Newsweek has named Mayo Clinic the No. 1 hospital in its World’s Best Hospitals rankings. "Finding more cures for patients is what drives our staff to transform healthcare so that new discoveries can benefit people everywhere," says CEO @GFarrugiaMD. Read more: mayocl.in/4u0L9Yy

There are generally 2 types of LLM users, those that use it to learn everything , and those that use it so they don’t have to learn anything.

The assumption: train longer, see better. The reality: some junior residents already exhibit expert-like gaze patterns. Some seniors still scan like novices.

Gaze reflects cognition. Where you look, how long you fixate, how you scan, these aren't random. They're windows into how you process surgical information. #ASC2026

We assess surgical competency by time in training. But "expert" isn't binary, neither is "novice." Eye-tracking reveals a spectrum of visual strategies within every PGY level. Two PGY3s can watch the same operation and see it completely differently.

Phenomenal AAS Presidential address by Dr. @BrenessaL this morning. The visual that resonated with me was the interwoven threads. Education is a noble and vital mission. Without it, academia ceases to exist. #ASC2026 @AcademicSurgery

Closing 2025 grateful for progress in deep learning, computer vision, and eye-tracking. ..and best of all, learning to be a dad 👶! The ultimate reminder that the best systems are built with care, tested with patience, and improved with love. Excited for 2026! 🎉 Happy New Year!

And then to animate it with Veo! 🤯 @NanoBanana

Some cool stuff #NanoBananaPro is doing!

With the launch of Nano Banana Pro, we're also rolling out the ability for Gemini users to check whether an image was generated with or edited by Google AI using SynthID, our digital watermarking technology. Now you can upload any image into the Gemini app and ask "Is this AI-generated?" Gemini then scans the image for the imperceptible SynthID watermarks that are embedded into all Google AI-generated images - including those by Nano Banana Pro! Learn more about Google’s efforts to increase AI transparency: goo.gle/synthid

Residency programs you are getting a star 🌟 Watch out world.. Future IM doc in the making.. 🌏🔥 #MedTwitter

IMO — Fei-fei is spot on... and this is why #SyntheticData for LLMs is a weak strategy for AGI Consider the following example: - Step 1: take a large LLM training dataset (e.g. "the whole internet") - Step 2: remove every sentence with the words "Kim" or "Kardashian" in it (and typo versions of those words) - Step 3: Train an LLM (the "Teacher LLM") on this new dataset - Step 4: Use the Teacher LLM to create a new MASSIVE synthetic dataset - Step 5: Train a Student LLM on the synthetic dataset. - Step 6: Prompt the model, "Who is Kim Kardashian?" Here's the problem... there's an information bottleneck There's NO WAY the Student LLM could accurately answer that question. It's never heard the word "Kim" or the word "Kardashian" before. There's no amount of logic, reason, parameter scaling, super-intelligence, or otherwise that could enable the model to know about Kim Kardashian. However, synthetic data CAN be useful for other things. Consider the Reversal Curse. In "The Reversal Curse", researchers at Vanderbilt, UK AISI, Apollo, NYU, Sussex, and Oxford discovered the following occurrences in a datapoint: - George Washington was the First President of the United States - Kim Kardashian is the daughter of Kris Jenner - Donald Trump is the current President of the United States If you took these sentences, and you DELETED any occurrance from the training data... where the sentence was reversed... e.g. - The First President of the United States was George Washington" - Kris Jenner is the mother of Kim Kardashian - The current president of the United States is Donald Trump This created a problem for an LLM... because it's only sees the token frequencies in one direction. This meant that an LLM can CORRECTLY prompts like: - Who is George Washington? - Who is Kim Kardashian the daughter of? - What is Donald Trump's current occupation? But the LLM could NOT answer prompts like: - Who was the first president of the United States? - Who is the daughter of Kris Jenner? - Who is the current president of the United States? WHAA?!?!!?! It's a pretty crazy limitation... and its very telling about how LLMs work... But things get really crazy when you change one more thing. If you first ask an LLM the first prompt (the one that works) and then keep that in the context window... it CAN correctly answer the second prompt!! This describes where and how LLMs do logic. - Training data -> Prediction: NO LOGIC (can't even reverse a simple relation) - Context Window -> Prediction: LOGIC (can do complex reasoning) And this is one that Synthetic Data CAN help with!!! Why? Because with synthetic data, you can get an LLM to output things it knows and then think about it... logically permute and combine facts its aware of. Synthetic data could fix the Reverse Curse!!! (and consider how it's doing this... if it can't do any logic directly on the training data... it's doing logic in a kindof "paint by number" sense... it's doing logic by tracing along logic it's seen in the training data... super cool!) However... this highlights the benefits and the constraints of synthetic data. PRO: synthetic data can squeeze more juice from your existing data (e.g. fix the reverse curse) CON: synthetic data cannot create new information (e.g. discover "Kim Kardashian" exists when its never heard the name). Thus, synthetic data can only reveal the natural extrapolations from the information an LLM already has. Helpful... but not a silver bullet to AGI. IMO - the real silver bullet is the instant retrieval of intelligence and context from the whole world. For that, we need attribution-based control (article below).

We're deeply saddened to hear of Dr. Sean Cleary's passing. His contributions to the field, commitment to training the next generation of surgeons & unwavering dedication to his patients have left an indelible mark. We're thinking of his family, friends & @UofTSurgery colleagues.

Introducing Genie 3, the most advanced world simulator ever created, enabled by numerous research breakthroughs. 🤯 Featuring high fidelity visuals, 20-24 fps, prompting on the go, world memory, and more.

Our open models are here. Both of them. openai.com/open-models

For the 36th consecutive year since U.S. News & World Report launched its "Best Hospitals" rankings, Mayo Clinic again ranks at the top of the 2025–2026 list. Read more: mayocl.in/4716aJm