Tootfinder

This https://arxiv.org/abs/2505.19184 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

When Two LLMs Debate, Both Think They'll Win
Can LLMs accurately adjust their confidence when facing opposition? Building on previous studies measuring calibration on static fact-based question-answering tasks, we evaluate Large Language Models (LLMs) in a dynamic, adversarial debate setting, uniquely combining two realistic factors: (a) a multi-turn format requiring models to update beliefs as new information emerges, and (b) a zero-sum structure to control for task-related uncertainty, since mutual high-confidence claims imply systemati…

Mittwoch: Pornoseiten verlassen Frankreich, Google pausiert KI-Suche für Fotos
Kritik an Altersverifikation Googles Ask Photos aufgeschoben Fusionsrekord in Wendelstein Youtuber mit stärkstem Hand-Laser Bit-Rauschen über Computex

I finally got rid of the old bed I’d kept since moving in, so the room feels pretty empty right now.
I’m in the process of reorganizing everything and plan to turn this space into my own anarchist sanctuary.
Soon, I’ll add a bookshelf and more anarchist art to the wall, right next to the communist hammer and sickle symbol.
#Declutter

This fucking weather…

we're so fucking back

Spiderman Meme: Cowboys TE is facing competition from a bunch of hims this summer https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/06/02/cowboys-brevyn-spann-ford-player-profile-2025/8400240900…

This https://arxiv.org/abs/2505.00812 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization
Recent studies indicate that deep neural networks degrade in generalization performance under noisy supervision. Existing methods focus on isolating clean subsets or correcting noisy labels, facing limitations such as high computational costs, heavy hyperparameter tuning process, and coarse-grained optimization. To address these challenges, we propose a novel two-stage noisy learning framework that enables instance-level optimization through a dynamically weighted loss function, avoiding hyperp…

Enhancing Finite State Machine Design Automation with Large Language Models and Prompt Engineering Techniques
Qun-Kai Lin, Cheng Hsu, Tian-Sheuan Chang
https://arxiv.org/abs/2506.00001

Enhancing Finite State Machine Design Automation with Large Language Models and Prompt Engineering Techniques
Large Language Models (LLMs) have attracted considerable attention in recent years due to their remarkable compatibility with Hardware Description Language (HDL) design. In this paper, we examine the performance of three major LLMs, Claude 3 Opus, ChatGPT-4, and ChatGPT-4o, in designing finite state machines (FSMs). By utilizing the instructional content provided by HDLBits, we evaluate the stability, limitations, and potential approaches for improving the success rates of these models. Further…

Energy-Embedded Neural Solvers for One-Dimensional Quantum Systems
Yi-Qiang Wu, Xuan Liu, Hanlin Li, Fuqiang Wang
https://arxiv.org/abs/2505.24194 https://…

Energy-Embedded Neural Solvers for One-Dimensional Quantum Systems
Physics-informed neural networks (PINN) have been widely used in computational physics to solve partial differential equations (PDEs). In this study, we propose an energy-embedding-based physics-informed neural network method for solving the one-dimensional time-independent Schrödinger equation to obtain ground- and excited-state wave functions, as well as energy eigenvalues by incorporating an embedding layer to generate process-driven data. The method demonstrates high accuracy for several w…

This https://arxiv.org/abs/1206.3472 has been replaced.
link: https://scholar.google.com/scholar?q=a

Perfect taxon sampling and fixing taxon traceability: Introducing a class of phylogenetically decisive collections of taxon sets
Phylogenetically decisive collections of taxon sets have the property that if trees are chosen for each of their elements, as long as these trees are compatible, the resulting supertree is unique. This means that as long as the trees describing the phylogenetic relationships of the (input) species sets are compatible, they can only be combined into a common supertree in precisely one way. This setting is sometimes also referred to as \enquote{perfect taxon sampling}. While for rooted trees, the…