Tootfinder

🇺🇦 #NowPlaying on KEXP's #MorningShow
Sylvan Esso:
🎵 Uncatena
#SylvanEsso
https://sylvanesso.bandcamp.com/track/uncatena

"Sanierungsbedürftig": Rund 25.000 Kilometer deutscher Fernstraßen haben Schäden
Viele Autobahnen und Bundesstraßen in Deutschland sind bröckelig, haben Risse und Schlaglöcher. Die Reparaturen gehen nur schleppend voran.

Metacognition and self-regulated learning in manipulative robotic problem-solving task
Margarida Romero (UniCA, UIC, LINE), George Kalmpourtzis
https://arxiv.org/abs/2508.05112 …

Metacognition and self-regulated learning in manipulative robotic problem-solving task
Metacognition is an important aspect in creative problem solving (CPS) and through this chapter we analyse the meta-reasoning aspects applied in the different processes of monitoring the progress of learners' reasoning and CPS activities. Meta-reasoning monitors the way that problem-solving processes advance and regulate time and efforts towards a solution. In the context of an ill-defined problem, exploration is required to develop a better-defined problem space and advance towards the solutio…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Sylvan Esso:
🎵 Ferris Wheel
#SylvanEsso
https://giraffelookout.bandcamp.com/track/sylvan-esso-ferris-wheel-giraffe-lookout-dj-edit
https://open.spotify.com/track/0yiJNNm39cp1T7RnXOMVLX

Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes
Nicholas H. Nelsen, Houman Owhadi, Andrew M. Stuart, Xianjin Yang, Zongren Zou
https://arxiv.org/abs/2510.05568

Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes
Methods for solving scientific computing and inference problems, such as kernel- and neural network-based approaches for partial differential equations (PDEs), inverse problems, and supervised learning tasks, depend crucially on the choice of hyperparameters. Specifically, the efficacy of such methods, and in particular their accuracy, stability, and generalization properties, strongly depends on the choice of hyperparameters. While bilevel optimization offers a principled framework for hyperpa…

Extension of Krein's special method for solving integral equations
G. A. Grigorian
https://arxiv.org/abs/2510.04608 https://arxiv.org/pdf/2510.04608

Extension of Krein's special method for solving integral equations
Extencion of Krein's special method for solving of integral equation to that method for solving of systems of integral equations is established. Generalizations of formulae for solution of integral equations are obtained. The result obtained is demonstrated by examples.

ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming
Weichun Shi, Minghao Liu, Wanting Zhang, Langchen Shi, Fuqi Jia, Feifei Ma, Jian Zhang
https://arxiv.org/abs/2510.05774

ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming
Constraint programming (CP) is a crucial technology for solving real-world constraint optimization problems (COPs), with the advantages of rich modeling semantics and high solving efficiency. Using large language models (LLMs) to generate formal modeling automatically for COPs is becoming a promising approach, which aims to build trustworthy neuro-symbolic AI with the help of symbolic solvers. However, CP has received less attention compared to works based on operations research (OR) models. We…

SubApSnap: Solving parameter-dependent linear systems with a snapshot and subsampling
Eleanor Jones, Yuji Nakatsukasa
https://arxiv.org/abs/2510.04825 https://

SubApSnap: Solving parameter-dependent linear systems with a snapshot and subsampling
A growing number of problems in computational mathematics can be reduced to the solution of many linear systems that are related, often depending smoothly or slowly on a parameter $p$, that is, $A(p)x(p)=b(p)$. We introduce an efficient algorithm for solving such parameter-dependent linear systems for many values of $p$. The algorithm, which we call SubApSnap (for \emph{Sub}sampled $A(p)$ times \emph{Snap}shot), is based on combining ideas from model order reduction and randomised linear algebr…

Invisible Saboteurs: Sycophantic LLMs Mislead Novices in Problem-Solving Tasks
Jessica Y. Bo, Majeed Kazemitabaar, Mengqing Deng, Michael Inzlicht, Ashton Anderson
https://arxiv.org/abs/2510.03667

Invisible Saboteurs: Sycophantic LLMs Mislead Novices in Problem-Solving Tasks
Sycophancy, the tendency of LLM-based chatbots to express excessive enthusiasm, agreement, flattery, and a lack of disagreement, is emerging as a significant risk in human-AI interactions. However, the extent to which this affects human-LLM collaboration in complex problem-solving tasks is not well quantified, especially among novices who are prone to misconceptions. We created two LLM chatbots, one with high sycophancy and one with low sycophancy, and conducted a within-subjects experiment (n=…

Human-AI Schema Discovery and Application for Creative Problem Solving
Sitong Wang
https://arxiv.org/abs/2508.05045 https://arxiv.org/pdf/2508.05045…

Human-AI Schema Discovery and Application for Creative Problem Solving
Humans often rely on underlying structural patterns-schemas-to create, whether by writing stories, designing software, or composing music. Schemas help organize ideas and guide exploration, but they are often difficult to discover and apply, especially in complex or unfamiliar domains. My Ph.D. research develops a framework for human-AI schema discovery and application to support creative problem solving. I design systems that support users in sensemaking over examples to abstract schemas, and …