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Blurb for the non-expert: There's this literature at the intersection of statistical physics and TCS which studies algorithmic/complexity theoretic implications of phase transitions (think ice → water → vapor). On the physics side, there's a whole landscape of heuristic calculations (some of which have been made rigorous) which suggest different ways of detecting these phase transitions (this term itself is somewhat vague/ill defined). A general template of a question here is, do these notions of phase transitions have algorithmic implications? That is, as we take a material and heat it or cool it, do certain questions around computing properties of this material become easy vs computationally intractable? One particular category of material that physicists and computer scientists alike are especially interested in are glasses. (In fact, the canonical model of spin glasses is the Sherrington-Kirkpatrick model. S is a theoretical physicist, K is a computer scientist). On the physics sid

Publications Google Scholar TCS in general Learning theory and stats Cryptography Programming languages On Zeros and Algorithms for Disordered Systems: Mean-Field Spin Glasses (STOC 2026) Authors: Ferenc Bencs, Brice Huang, DL, Kuikui Liu, Guus Regts Blurb for the non-expert: There's this literature at the intersection of statistical physics and TCS which studies algorithmic/complexity theoretic implications of phase transitions (think ice → water → vapor). On the physics side, there's a whole landscape of heuristic calculations (some of which have been made rigorous) which suggest…

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