Tootfinder

Advanced For-Loop for QML algorithm search
FuTe Wong
https://arxiv.org/abs/2506.18260 https://arxiv.org/pdf/2506.18260

Advanced For-Loop for QML algorithm search
This paper introduces an advanced framework leveraging Large Language Model-based Multi-Agent Systems (LLMMA) for the automated search and optimization of Quantum Machine Learning (QML) algorithms. Inspired by Google DeepMind's FunSearch, the proposed system works on abstract level to iteratively generates and refines quantum transformations of classical machine learning algorithms (concepts), such as the Multi-Layer Perceptron, forward-forward and backpropagation algorithms. As a proof of conc…

AI Weakens Critical Thinking. This is How To Rebuild It. https://www.psychologytoday.com/us/blog/the-algorithmic-mind/202505/ai-weakens-critical-thinking-and-how-to-rebuild-it

AI Weakens Critical Thinking. This Is How to Rebuild It
AI does our thinking for us and our minds are paying the price. Here’s how to reclaim cognitive independence.

Evaluating Multichannel Speech Enhancement Algorithms at the Phoneme Scale Across Genders
Nasser-Eddine Monir, Paul Magron, Romain Serizel
https://arxiv.org/abs/2506.18691

Evaluating Multichannel Speech Enhancement Algorithms at the Phoneme Scale Across Genders
Multichannel speech enhancement algorithms are essential for improving the intelligibility of speech signals in noisy environments. These algorithms are usually evaluated at the utterance level, but this approach overlooks the disparities in acoustic characteristics that are observed in different phoneme categories and between male and female speakers. In this paper, we investigate the impact of gender and phonetic content on speech enhancement algorithms. We motivate this approach by outlining…

Algorithmic hardness of the partition function for nucleic acid strands
Gwendal Ducloz, Ahmed Shalaby, Damien Woods
https://arxiv.org/abs/2506.19756 https:…

Algorithmic hardness of the partition function for nucleic acid strands
To understand and engineer biological and artificial nucleic acid systems, algorithms are employed for prediction of secondary structures at thermodynamic equilibrium. Dynamic programming algorithms are used to compute the most favoured, or Minimum Free Energy (MFE), structure, and the Partition Function (PF), a tool for assigning a probability to any structure. However, in some situations, such as when there are large numbers of strands, or pseudoknoted systems, NP-hardness results show that s…

A Greedy Version of the Frame Algorithm
Brody Dylan Johnson
https://arxiv.org/abs/2506.18865 https://arxiv.org/pdf/2506.18865

A Greedy Version of the Frame Algorithm
The frame algorithm uses a simple recursive formula to approximate an unknown vector from its frame coefficients. This note introduces an adaptive version of the frame algorithm that maximizes the error reduction between steps in terms of an equivalent norm. This greedy version of the frame algorithm is proven to achieve the same guaranteed convergence rate as the traditional frame algorithm, yet, unlike its classical counterpart, does not require knowledge of the frame bounds. The robustness o…

Optimal Parallel Algorithms for Convex Hulls in 2D and 3D under Noisy Primitive Operations
Michael T. Goodrich, Vinesh Sridhar
https://arxiv.org/abs/2506.17507

Optimal Parallel Algorithms for Convex Hulls in 2D and 3D under Noisy Primitive Operations
In the noisy primitives model, each primitive comparison performed by an algorithm, e.g., testing whether one value is greater than another, returns the incorrect answer with random, independent probability p < 1/2 and otherwise returns a correct answer. This model was first applied in the context of sorting and searching, and recent work by Eppstein, Goodrich, and Sridhar extends this model to sequential algorithms involving geometric primitives such as orientation and sidedness tests. However…

A Heuristic Algorithm for Shortest Path Search
Huashan Yu, Xiaolin Wang, Yingwei Luo
https://arxiv.org/abs/2506.19349 https://arxiv.o…

A Heuristic Algorithm for Shortest Path Search
The Single-Source Shortest Path (SSSP) problem is well-known for the challenges in developing fast, practical, and work-efficient parallel algorithms. This work introduces a novel shortest path search method. It allows paths with different lengths to be extended in parallel at the cost of almost negligible repeated relaxations. A dynamic-stepping heuristic is proposed for the method to efficiently reduce the extended paths and the synchronizations. A traversal-optimization heuristic is proposed…

🇺🇦 #NowPlaying on KEXP's #ElSonido
Romperayo:
🎵 Alegría por un Zumo de Naranja Con Panela
#Romperayo
https://romperayo.bandcamp.com/track/alegria-por-un-zumo-de-naranja-con-panela-2
https://open.spotify.com/track/3c3SlCmGqAULLHI3eGuHTK

Spectral Outer-Approximation Algorithms for Binary Semidefinite Problems
Daniel de Roux, Zedong Peng, David E. Bernal Neira
https://arxiv.org/abs/2506.18265

Spectral Outer-Approximation Algorithms for Binary Semidefinite Problems
Integer semidefinite programming (ISDP) has recently gained attention due to its connection to binary quadratically constrained quadratic programs (BQCQPs), which can be exactly reformulated as binary semidefinite programs (BSDPs). However, it remains unclear whether this reformulation effectively uses existing ISDP solvers to address BQCQPs. To the best of our knowledge, no specialized ISDP algorithms exploit the unique structure of BSDPs derived from BQCQPs. This paper proposes a novel spectr…

Programming by Backprop: LLMs Acquire Reusable Algorithmic Abstractions During Code Training
Jonathan Cook, Silvia Sapora, Arash Ahmadian, Akbir Khan, Tim Rocktaschel, Jakob Foerster, Laura Ruis
https://arxiv.org/abs/2506.18777

Programming by Backprop: LLMs Acquire Reusable Algorithmic Abstractions During Code Training
Training large language models (LLMs) on source code significantly enhances their general-purpose reasoning abilities, but the mechanisms underlying this generalisation are poorly understood. In this paper, we propose Programming by Backprop (PBB) as a potential driver of this effect - teaching a model to evaluate a program for inputs by training on its source code alone, without ever seeing I/O examples. To explore this idea, we finetune LLMs on two sets of programs representing simple maths p…