AI: Which Way Will the Berlin Wall Fall?

I have taken to describing the current situation thusly:

The last time I felt this exhilarated but this unsettled was when the Berlin Wall fell in 1989.

In important ways it represented an exciting advance, a true change in the world. There was hope, even celebration, but there was also uncertainty and even fear; would it all work out? What would the response from Moscow be? A little later, as the Soviet Union itself disintegrated, what would come out the other side -- and who would control the nukes?

I'll admit, I have reservations about AI, but even above those I didn't expect things to really move this fast. Having lived through many plateaus, I assigned a high probability to LLMs hitting another plateau in capability below their current level. I am startled, and concerned, but also intrigued.

There is so much more to say, but I'll leave it here for now.

AI: New Papers Coming Out at the Rate of Data?

 Will AI put out new papers, theorems, code, and other artifacts, just like existing systems put out data that has to be synthesized into knowledge?

The massive dump of mathematics papers from OpenAI a few days ago suggests that we are going to be flooded with a new type of "data point": a theorem, function, larger piece of code, or even entire paper, just like digital systems and the Internet have flooded us with data in the last couple of decades.

Maybe we can call them synthetic papers, or synths, instead of actual academic journal papers?

What will it be like to try to sift through those, looking for the message or the meaning?

Each synth won't really represent a new piece of knowledge until, of course, it's evaluated for correctness, but also not until it can be incorporated into further work, and particularly not until humans can understand it, vouch for its importance and build on it. (Unless we all just want to go the way of the Eloi, but that would be a very long and somewhat separate conversation.)

Cross-Validation of Open-Source Quantum Network Simulators

New paper from our collaboration with Argonne/Chicago. I think this is one of the most thorough, careful papers on quantum repeaters out there.

We began more than two years ago with the idea of just making sure our simulators work right, to compare the output on some simple tasks and strengthen our confidence in both simulators. It turned into a journey of understanding, identifying a long list of assumptions underlying our work that differed in the two groups.

It also resulted in a number of outright bugfixes to both simulators, including several months of head scratching near the end where the paper was essentially ready to go but we couldn't get an equation and the output of one of the simulators to match. Now resolved.

If you work on quantum networks, check it out -- and check your own assumptions about protocols and design and compare to ours!

As always, feedback welcome. And thanks to all ten coauthors, especially Michal and Joaquin, for pushing through on the coding, math and writing.

Joaquin Chung (1 and 2), Michal Hajdušek (3), Naphan Benchasattabuse (3), Robert J. Hayek (1 and 4), Alexander Kolar (1 and 2), Ansh Singal (1 and 4), Kento Samuel Soon (3), Kentaro Teramoto (5), Allen Zang (1 and 2), Caitao Zhan (1), Raj Kettimuthu (1 and 2), Rodney Van Meter (3) ((1) Argonne National Laboratory, (2) University of Chicago, (3) Keio University, (4) Northwestern University, (5) Mercari Inc.)

https://arxiv.org/abs/2610.09322

Oh, and I should point out that there was a preliminary version of this published at an IEEE INFOCOM workshop in 2025. The new version is better, though! (Literally almost two years of extra work, substantially deeper and more detailed.)

https://ieeexplore.ieee.org/abstract/document/11152951