Tootfinder

Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
Yang Wang, Chenghao Xiao, Chia-Yi Hsiao, Zi Yan Chang, Chi-Li Chen, Tyler Loakman, Chenghua Lin
https://arxiv.org/abs/2509.03867

Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
We introduce Drivelology, a unique linguistic phenomenon characterised as "nonsense with depth", utterances that are syntactically coherent yet pragmatically paradoxical, emotionally loaded, or rhetorically subversive. While such expressions may resemble surface-level nonsense, they encode implicit meaning requiring contextual inference, moral reasoning, or emotional interpretation. We find that current large language models (LLMs), despite excelling at many natural language processing (NLP) ta…

Improving Online-to-Nonconvex Conversion for Smooth Optimization via Double Optimism
Francisco Patitucci, Ruichen Jiang, Aryan Mokhtari
https://arxiv.org/abs/2510.03167 https://…

Improving Online-to-Nonconvex Conversion for Smooth Optimization via Double Optimism
A recent breakthrough in nonconvex optimization is the online-to-nonconvex conversion framework of \cite{cutkosky2023optimal}, which reformulates the task of finding an $\varepsilon$-first-order stationary point as an online learning problem. When both the gradient and the Hessian are Lipschitz continuous, instantiating this framework with two different online learners achieves a complexity of $\mathcal{O}(\varepsilon^{-1.75}\log(1/\varepsilon))$ in the deterministic case and a complexity of $\…

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Ni Maxine:
🎵 Time (BBC Introducing Meryside)
#NiMaxine

Anyone from Microsoft following me?
I’m going to leave GitHub (I’ve been a user since the beta days) and will incessantly ask everyone I know to leave it as well if you don’t roll back the “can’t opt out” force-feeding of “AI” nonsense.
You should also have no integration or collaboration with Xai at all, ever.
Make your management have some sense, this is hostile and disgusting and wrong.
#github

Nonsymmorphic symmetry protected hourglass Dirac chain topology and conventional superconductivity in ZrIrGe
Pavan Kumar Meena, Dibyendu Samanta, Shashank Srivastava, Poulami Manna, Sudeep Kumar Ghosh, Ravi Prakash Singh
https://arxiv.org/abs/2510.03188

Nonsymmorphic symmetry protected hourglass Dirac chain topology and conventional superconductivity in ZrIrGe
Ternary transition-metal germanide superconductors with nonsymmorphic symmetries offer promising platforms for symmetry-protected topological phases. In this work, we investigate ZrIrGe, which crystallizes in the nonsymmorphic TiNiSi-type structure. Electrical, magnetic, and specific heat measurements confirm bulk type-II superconductivity with a full gap and a transition temperature of 2.84(7) K, consistent with weak-coupling BCS behavior. First-principles calculations reveal hourglass-shaped …

This computer science professor agrees with @…. The humanities are crucial.
I mean, it cuts the other way too: a lot of the credulous nonsense about AI would melt away if people had a better idea how to think about software systems and mathematical models, what they really do and don’t do, how to look at them critically.
The key in my view is people having multiple ways of seeing and not narrowing themselves.
https://infosec.exchange/@dnsprincess/115140939294365490

ProxSTORM -- A Stochastic Trust-Region Algorithm for Nonsmooth Optimization
Robert J. Baraldi, Aurya Javeed, Drew P. Kouri, Katya Scheinberg
https://arxiv.org/abs/2510.03187 htt…

ProxSTORM -- A Stochastic Trust-Region Algorithm for Nonsmooth Optimization
We develop a stochastic trust-region algorithm for minimizing the sum of a possibly nonconvex Lipschitz-smooth function that can only be evaluated stochastically and a nonsmooth, deterministic, convex function. This algorithm, which we call ProxSTORM, generalizes STORM [1, 2] -- a stochastic trust-region algorithm for the unconstrained optimization of smooth functions -- and the inexact deterministic proximal trust-region algorithm in [3]. We generalize and, in some cases, simplify problem assu…

I jest, but I do think that irrational persuasion / disarming people’s good sense / con artistry / scamming is a real skillset that some rare people have in spades — a genuine if horrifying form of intelligence — and we dismiss the otherwise nincompoopish people who have it our own peril.

Subgradient Methods for Nonsmooth Convex Functions with Adversarial Errors
Martijn G\"osgens, Bart P. G. Van Parys
https://arxiv.org/abs/2510.03072 https://

Subgradient Methods for Nonsmooth Convex Functions with Adversarial Errors
We consider minimizing nonsmooth convex functions with bounded subgradients. However, instead of directly observing a subgradient at every step $k\in [0, \dots, N-1]$, we assume that the optimizer receives an adversarially corrupted subgradient. The adversary's power is limited to a finite corruption budget, but allows the adversary to strategically time its perturbations. We show that the classical averaged subgradient descent method, which is optimal in the noiseless case, has worst-case perf…

Progressive Bound Strengthening via Doubly Nonnegative Cutting Planes for Nonconvex Quadratic Programs
Zheng Qu, Defeng Sun, Jintao Xu
https://arxiv.org/abs/2510.02948 https://

Progressive Bound Strengthening via Doubly Nonnegative Cutting Planes for Nonconvex Quadratic Programs
We introduce a cutting-plane framework for nonconvex quadratic programs (QPs) that progressively tightens convex relaxations. Our approach leverages the doubly nonnegative (DNN) relaxation to compute strong lower bounds and generate separating cuts, which are iteratively added to improve the relaxation. We establish that, at any Karush-Kuhn-Tucker (KKT) point satisfying a second-order sufficient condition, a valid cut can be obtained by solving a linear semidefinite program (SDP), and we devise…