Tootfinder

Generalization of Gibbs and Langevin Monte Carlo Algorithms in the Interpolation Regime
Andreas Maurer, Erfan Mirzaei, Massimiliano Pontil
https://arxiv.org/abs/2510.06028 https…

Generalization of Gibbs and Langevin Monte Carlo Algorithms in the Interpolation Regime
The paper provides data-dependent bounds on the test error of the Gibbs algorithm in the overparameterized interpolation regime, where low training errors are also obtained for impossible data, such as random labels in classification. The bounds are stable under approximation with Langevin Monte Carlo algorithms. Experiments on the MNIST and CIFAR-10 datasets verify that the bounds yield nontrivial predictions on true labeled data and correctly upper bound the test error for random labels. Our …

Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep Research
Gang Liu, Yihan Zhu, Jie Chen, Meng Jiang
https://arxiv.org/abs/2510.06056 https://

Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep Research
Large language models hold promise as scientific assistants, yet existing agents either rely solely on algorithm evolution or on deep research in isolation, both of which face critical limitations. Pure algorithm evolution, as in AlphaEvolve, depends only on the internal knowledge of LLMs and quickly plateaus in complex domains, while pure deep research proposes ideas without validation, resulting in unrealistic or unimplementable solutions. We present DeepEvolve, an agent that integrates deep …

An analysis of 1,100 TikTok users' watch histories across six months in 2024 shows how effective TikTok is at getting even its heaviest users to watch more (Washington Post)

Spending hours on TikTok? Here’s how the app keeps you swiping.
Washington Post reporters analyzed data from over 800 TikTok users to learn how the app turns people into power users, some of whom spend hours per day scrolling.

Stochastic BDDC algorithms
Xuemin Tu, Jinjin Zhang
https://arxiv.org/abs/2510.05993 https://arxiv.org/pdf/2510.05993

Stochastic BDDC algorithms
Stochastic balancing domain decomposition by constraints (BDDC) algorithms are developed and analyzed for the sampling of the solutions of linear stochastic elliptic equations with random coefficients. Different from the deterministic BDDC algorithms, the stochastic BDDC algorithms have online and offline stages. At the offline stage, the Polynomial Chaos (PC) expansions of different components of the BDDC algorithms are constructed based on the subdomain local parametrization of the stochastic…

From earlier this year - well worth a read. From @… and Sue Branford.
A tale of two cities: What drove 2024’s Valencia and Porto Alegre floods? https://

A tale of two cities: What drove 2024’s Valencia and Porto Alegre floods?
Neither Porto Alegre, Brazil, nor Valencia, Spain, are in the news this week. But they have been before, and almost certainly will be again, as bouts of extreme drought, followed by deluge, become more frequent and fierce. The media cameras are gone from 2024’s inundations, but in Valencia 100,000 wrecked cars remain to be disposed […]

⚗️ Algorithms reveal how propane becomes propylene for everyday products
#chemistry

Algorithms reveal how propane becomes propylene for everyday products
Countless everyday products, from plastic squeeze bottles to outdoor furniture, are derived by first turning propane into propylene.

from my link log —
Four ways to improve a perfect SQL join algorithm.
https://remy.wang/blog/ya-fast.html
saved 2026-01-04 https://dotat.at/:/QZ8MM.…

An analysis of 1,100 TikTok users' watch histories across six months in 2024 shows how effective TikTok is at getting even its heaviest users to watch more (Washington Post)

Spending hours on TikTok? Here’s how the app keeps you swiping.
Washington Post reporters analyzed data from over 800 TikTok users to learn how the app turns people into power users, some of whom spend hours per day scrolling.

A New Quantum Linear System Algorithm Beyond the Condition Number and Its Application to Solving Multivariate Polynomial Systems
Jianqiang Li
https://arxiv.org/abs/2510.05588 ht…

A New Quantum Linear System Algorithm Beyond the Condition Number and Its Application to Solving Multivariate Polynomial Systems
Given a matrix $A$ of dimension $M \times N$ and a vector $\vec{b}$, the quantum linear system (QLS) problem asks for the preparation of a quantum state $|\vec{y}\rangle$ proportional to the solution of $A\vec{y} = \vec{b}$. Existing QLS algorithms have runtimes that scale linearly with the condition number $κ(A)$, the sparsity of $A$, and logarithmically with inverse precision, but often overlook structural properties of $\vec{b}$, whose alignment with $A$'s eigenspaces can greatly affec…

An Algorithmic Information-Theoretic Perspective on the Symbol Grounding Problem
Zhangchi Liu
https://arxiv.org/abs/2510.05153 https://arxiv.org/pdf/2510.0…

An Algorithmic Information-Theoretic Perspective on the Symbol Grounding Problem
This paper provides a definitive, unifying framework for the Symbol Grounding Problem (SGP) by reformulating it within Algorithmic Information Theory (AIT). We demonstrate that the grounding of meaning is a process fundamentally constrained by information-theoretic limits, thereby unifying the Gödelian (self-reference) and No Free Lunch (statistical) perspectives. We model a symbolic system as a universal Turing machine and define grounding as an act of information compression. The argument pr…