iiinova — where your digital self keeps evolving

Ritwik Gupta @ritwikgupta.bsky.social · 03/08/2026

Optional Practical Training (OPT) allows international students to work after completing their degrees. A proposed policy would add a $100,000 for OPT status. Using SEVIS data, I analyze how this could impact the American talent pool ritwikgupta.me/blog/posts/2...

ritwikgupta.me

What If OPT Cost $100,000?

How a proposed $100,000 OPT fee could reshape the talent America has already welcomed and trained.

010

Ritwik Gupta @ritwikgupta.bsky.social · 10/03/2025

Oh, and I must mention the BAIR espresso machine! It was only huddled around freshly ground coffee machines did we come up with this idea (initially wondering if content length matters for statistical behaviors). If you want good research, provide your students with coffee.

020

Ritwik Gupta @ritwikgupta.bsky.social · 10/03/2025

This behavior has very interesting quirks. LLMs implicitly demonstrate time-discounting over the ICL examples. That is, recent evidence matters more!

120

Ritwik Gupta @ritwikgupta.bsky.social · 10/03/2025

Interestingly, models follow a very similar trajectory to what the true Bayesian posterior should look like with the same amount of evidence! When we prompt for coin flips from a 60% heads-biased coin but give it evidence the follows 70% heads, models converge to the latter.

110

Ritwik Gupta @ritwikgupta.bsky.social · 10/03/2025

Can we control this behavior? We tried many things before settling on in-context learning as a working mechanism. If we prompt an LLM to flip a biased coin, and then show increasing rollouts of flips from such a distribution, models converge to the right underlying parameter.

110

Ritwik Gupta @ritwikgupta.bsky.social · 10/03/2025

Biased coin flips follow a simple probability distribution that LLMs should be able to simulate explicitly. In fact, when prompted to flip a fair coin, most LLMs predict heads 70-85% of the time! This holds true even if you prompt the model to flip a biased coin 🪙

110

Ritwik Gupta @ritwikgupta.bsky.social · 10/03/2025

Do LLMs understand probability distributions? Can they serve as effective simulators of probability? No! However, in our latest paper that via in-context learning, LLMs update their broken priors in a manner akin to Bayseian updating. 📝 arxiv.org/abs/2503.04722

arxiv.org

Enough Coin Flips Can Make LLMs Act Bayesian

Large language models (LLMs) exhibit the ability to generalize given few-shot examples in their input prompt, an emergent capability known as in-context learning (ICL). We investigate whether LLMs uti...

1155

Reposted by Ritwik Gupta

JHU Computer Science @jhucompsci.bsky.social · 11/02/2025

Seminar with @ritwikgupta.bsky.social coming up! Learn more here: www.cs.jhu.edu/event/cs-sem...

Computer Science Seminar Series. Making AI Work in the Crucible: Perception and Reasoning in Chaotic Environments. February 25, 2025, 228 Malone Hall. Refreshments available 10:30 a.m. Seminar begins 10:45 a.m. Ritwik Gupta, University of California, Berkeley.

032

Ritwik Gupta @ritwikgupta.bsky.social · 10/02/2025

Recent proposals to kill the influence of “Chinese AI” in America will have devastating knock-on effects to American innovation. In this article, I discuss the statelessness of AI and the overly broad nature of Senator Hawley’s proposed legislation.

030

Ritwik Gupta @ritwikgupta.bsky.social · 24/01/2025

It is a false premise that America has a lead in AI over China. So many articles have come out recently about DeepSeek threatening our lead. The lead in *meaningful* capabilities has never existed.

032

Ritwik Gupta @ritwikgupta.bsky.social · 15/12/2024

The narrative that we have achieved peak data is so absurd to me. Humans are still around. New data is constantly being created. We just have to be more efficient about using it.

030