Tootfinder

How pathetic is it that the Secretary of Education had cards with the answers to questions given to her?
https://www.instagram.com/reel/DKfezL7O046/LNeGcEq37-Kw

FPF Unveils Paper on State Data Minimization Trends
https://fpf.org/blog/fpf-unveils-paper-on-state-data-minimization-trends/
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Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration
Matthew Russell, Aman Shah, Giles Blaney, Judith Amores, Mary Czerwinski, Robert J. K. Jacob
https://arxiv.org/abs/2506.04167

Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration
AI-based interactive assistants are advancing human-augmenting technology, yet their effects on users' mental and physiological states remain under-explored. We address this gap by analyzing how Copilot for Microsoft Word, a LLM-based assistant, impacts users. Using tasks ranging from objective (SAT reading comprehension) to subjective (personal reflection), and with measurements including fNIRS, Empatica E4, NASA-TLX, and questionnaires, we measure Copilot's effects on users. We also evaluate …

Frobenius theorem and fine structure of tangency sets to non-involutive distributions
Giovanni Alberti, Annalisa Massaccesi, Andrea Merlo
https://arxiv.org/abs/2506.03715

Frobenius theorem and fine structure of tangency sets to non-involutive distributions
In this paper we provide a complete answer to the question whether Frobenius' Theorem can be generalized to surfaces below the $C^{1,1}$ threshold. We study the fine structure of the tangency set in terms of involutivity of a given distribution and we highlight a tradeoff behavior between the regularity of a tangent surface and that of the tangency set. First of all, we prove a Frobenius-type result, that is, given a $k$-dimensional surface $S$ of class $C^1$ and a non-involutive $k$-distributi…

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
Shuai Wang, Yinan Yu
https://arxiv.org/abs/2506.01784 https://

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
While Large Language Models (LLMs) excel at many natural language processing tasks, they often suffer from factual inaccuracies in knowledge-intensive scenarios. Integrating external knowledge resources, particularly knowledge graphs (KGs), provides a transparent and updatable foundation for more reliable reasoning. Knowledge Base Question Answering (KBQA), which queries and reasons over KGs, is central to this effort, especially for complex, multi-hop queries. However, multi-hop reasoning pose…

Combine and conquer: model averaging for out-of-distribution forecasting
Stephane Hess, Sander van Cranenburgh
https://arxiv.org/abs/2506.03693 https://

Combine and conquer: model averaging for out-of-distribution forecasting
Travel behaviour modellers have an increasingly diverse set of models at their disposal, ranging from traditional econometric structures to models from mathematical psychology and data-driven approaches from machine learning. A key question arises as to how well these different models perform in prediction, especially when considering trips of different characteristics from those used in estimation, i.e. out-of-distribution prediction, and whether better predictions can be obtained by combining…

This https://arxiv.org/abs/2501.15056 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Feedback-Aware Monte Carlo Tree Search for Efficient Information Seeking in Goal-Oriented Conversations
Effective decision-making and problem-solving in conversational systems require the ability to identify and acquire missing information through targeted questioning. A key challenge lies in efficiently narrowing down a large space of possible outcomes by posing questions that minimize uncertainty. To address this, we introduce a novel framework that leverages Large Language Models (LLMs) to generate information-seeking questions, with Monte Carlo Tree Search (MCTS) to strategically select quest…

On the maximum number of edges of outer k-planar graphs
Maximilian Pfister
https://arxiv.org/abs/2505.24490 https://arxiv.org/pdf/250…

On the maximum number of edges of outer k-planar graphs
We study the maximum number of straight-line segments connecting $n$ points in convex position in the plane, so that each segment intersects at most $k$ others. This question can also be framed as the maximum number of edges of an outer $k$-planar graph on $n$ vertices. We outline several approaches to tackle the problem with the best approach yielding an upper bound of $(\sqrt{2}+\varepsilon)\sqrt{k}n$ edges (with $\varepsilon \rightarrow 0$ for sufficiently large $k$). We further investigate …

This https://arxiv.org/abs/2412.03238 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Dynamic Consistent $k$-Center Clustering with Optimal Recourse
Given points from an arbitrary metric space and a sequence of point updates sent by an adversary, what is the minimum recourse per update (i.e., the minimum number of changes needed to the set of centers after an update), in order to maintain a constant-factor approximation to a $k$-clustering problem? This question has received attention in recent years under the name consistent clustering. Previous works by Lattanzi and Vassilvitskii [ICLM '17] and Fichtenberger, Lattanzi, Norouzi-Fard, and…

A first view on the density of 5-planar graphs
Aaron B\"ungener, Jakob Franz, Michael Kaufmann, Maximilian Pfister
https://arxiv.org/abs/2505.24364 ht…

A first view on the density of 5-planar graphs
$k$-planar graphs are generalizations of planar graphs that can be drawn in the plane with at most $k > 0$ crossings per edge. One of the central research questions of $k$-planarity is the maximum edge density, i.e., the maximum number of edges a $k$-planar graph on $n$ vertices may have. While there are numerous results for the classes of general $k$-planar graphs for $k\leq 2$, there are only very few results for increasing $k=3$ or $4$ due to the complexity of the classes. We make a first st…