Phoebe Zeng (@pl_zeng) header
Phoebe Zeng (@pl_zeng) profile picture

In search of artificial natural kinds @PrincetonCS

Princeton
Joined January 2017
Thrilled to share that I’ll be starting @PrincetonCS this fall as a PhD candidate in the @cocosci_lab. My research will focus on AI for math. If math was erased from human memory, it seems likely we’d reinvent a similar mathematics. I hope interrogating the nature of this tight connection between human cognition and human mathematics can teach us how to design artificial mathematicians.
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Phoebe Zeng retweeted
LLMs are making novel mathematical discoveries -- but in controlled training experiments, why do they struggle to discover the number 0? Come discuss with @pl_zeng at #CCN2026, Poster B40, today from 2:00–3:45pm. Full proceedings paper: openreview.net/forum?id=dAsu…
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Model generalization improves after training on a small number of examples that include zero. Models with language pretraining require 50% fewer examples. This conclusion that language abilities scaffold mathematical discovery is reminiscent of Carey’s “Quinean bootstrapping”.
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Zero is special. Models can generalize to other numbers when held out of the training data in an analogous manner. The carry digit (9 in base 10) is also difficult for models to generalize to.
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Was great to host Jared and the @mathematics_inc team at @PrincetonCS yesterday. Thanks to everyone who participated in the exciting conversations about human and artificial mathematics.
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Phoebe Zeng retweeted
We're hosting the next Proof on August 1 in NYC. Come celebrate mathematical performance with us. It will be a special one :) @KlettPhoebe. RSVP below
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Phoebe Zeng retweeted
Anyway, science and mathematics are aesthetic pursuits and we need more culture that celebrates them as such, instead of just being grist for the mill of "impact".
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There will be much fanfare at our next Proof Liturgy. As always, all math curious are welcome!
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I’m proud to have contributed to @NormalComputing’s mission of creating a unified theory of thermodynamics and AI over the last two years. Computer science research should aim to be basic science research. I’m excited to share a self-contained introduction to thermodynamic computing; algorithms are nothing without a substrate.
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In the piece, I describe the stochastic mathematics we use to model physics and show how this formalism allows us to leverage physical systems to implement algorithms for learning.
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NYC math & physics club unite :)
Alright, NYC peeps: a few of us (@NoahChrein, @KlettPhoebe, etc.) are planning to meet up in Central Park next weekend - probably Sunday, weather permitting - to do some recreational math, discuss our favorite problems/theorems, etc. If you're interested, let me know ASAP!
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posteriors 𝞡 published at ICLR! I’ll be in Singapore next week, let’s chat all things scalable Bayesian learning! 🇸🇬👋
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Math should be seen in motion. Join me and @wjzeng at @EthicalNYC on April 2nd to participate in the mathematical arts.
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