Tootfinder

MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts?
Muntasir Wahed, Xiaona Zhou, Kiet A. Nguyen, Tianjiao Yu, Nirav Diwan, Gang Wang, Dilek Hakkani-T\"ur, Ismini Lourentzou
https://arxiv.org/abs/2507.19598

MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts?
Recent advancements in Large Language Models (LLMs) have significantly enhanced their code generation capabilities. However, their robustness against adversarial misuse, particularly through multi-turn malicious coding prompts, remains underexplored. In this work, we introduce code decomposition attacks, where a malicious coding task is broken down into a series of seemingly benign subtasks across multiple conversational turns to evade safety filters. To facilitate systematic evaluation, we int…

LLMs-guided adaptive compensator: Bringing Adaptivity to Automatic Control Systems with Large Language Models
Zhongchao Zhou, Yuxi Lu, Yaonan Zhu, Yifan Zhao, Bin He, Liang He, Wenwen Yu, Yusuke Iwasawa
https://arxiv.org/abs/2507.20509

LLMs-guided adaptive compensator: Bringing Adaptivity to Automatic Control Systems with Large Language Models
With rapid advances in code generation, reasoning, and problem-solving, Large Language Models (LLMs) are increasingly applied in robotics. Most existing work focuses on high-level tasks such as task decomposition. A few studies have explored the use of LLMs in feedback controller design; however, these efforts are restricted to overly simplified systems, fixed-structure gain tuning, and lack real-world validation. To further investigate LLMs in automatic control, this work targets a key subfiel…

Scalable inference of large-scale random kronecker graphs via tensor decomposition and Einstein summation
Sanaa Khobizy
https://arxiv.org/abs/2506.22292 ht…

Scalable inference of large-scale random kronecker graphs via tensor decomposition and Einstein summation
In this paper, we extend the analysis of random Kronecker graphs to multi-dimensional networks represented as tensors, enabling a more detailed and nuanced understanding of complex network structures. We decompose the adjacency tensor of such networks into two components: a low-rank signal tensor that captures the essential network structure and a zero-mean noise tensor that accounts for random variations. Building on recent advancements in tensor decomposition and random tensor theory, we intr…

This is the time to speak out, to disembed our selves from a fascist system, to place principles over profit and self-advancement.
To be what Viet Thanh Nguyen calls “disagreeable”.
Yes, of course there are risks.
But at this moment, with ICE in the streets and at our door,
when each hour another liberty is being erased,
and those who speak truth to power are being removed from TV,
from universities, from cultural centers,
when the cultural platform…

Noise-tolerant tomography of multimode linear optical interferometers with single photons
Yu. A. Biriukov, R. D. Morozov, I. V. Dyakonov, M. V. Rakhlin, A. I. Galimov, G. V. Klimko, S. V. Sorokin, I. V. Sedova, M. M. Kulagina, Yu. M. Zadiranov, A. A. Toropov, A. A. Korneev, S. P. Kulik, S. S. Straupe
https://arxiv.org/abs/2506.2…

Noise-tolerant tomography of multimode linear optical interferometers with single photons
Linear optical networks are fundamental to the advancement of quantum technologies, including quantum computing, communication, and sensing. The accurate characterization of these networks, described by unitary matrices, is crucial to their effective utilization and scalability. In this work, we present the method for reconstructing the transfer matrix of a linear optical interferometer based on the analysis of cross-correlation functions of photon counts between pairs of output modes. Our appr…

A non-iterative domain decomposition time integrator for linear wave equations
Tim Buchholz, Marlis Hochbruck
https://arxiv.org/abs/2507.19379 https://arxi…

A non-iterative domain decomposition time integrator for linear wave equations
We propose and analyze a non-iterative domain decomposition integrator for the linear acoustic wave equation. The core idea is to combine an implicit Crank-Nicolson step on spatial subdomains with a local prediction step at the subdomain interfaces. This enables parallelization across space while advancing sequentially in time, without requiring iterations at each time step. The method is similar to the methods from Blum, Lisky and Rannacher (1992) or Dawson and Dupont (1992), which have been d…

Advanced linear algebra
Teo Banica
https://arxiv.org/abs/2506.18666 https://arxiv.org/pdf/2506.18666…

Advanced linear algebra
This is an introduction to advanced linear algebra, with emphasis on geometric aspects, and with some applications included too. We first review basic linear algebra, notably with the spectral theorem in its general form, and with the theory of the resultant and discriminant. Then we discuss the Jordan form and its basic applications to physics, and other advanced decomposition results for the matrices. We then go into positivity topics, involving matrices and bilinear forms, and with a look in…

BotHash: Efficient and Training-Free Bot Detection Through Approximate Nearest Neighbor
Edoardo Di Paolo, Fabio De Gaspari, Angelo Spognardi
https://arxiv.org/abs/2506.20503

BotHash: Efficient and Training-Free Bot Detection Through Approximate Nearest Neighbor
Online Social Networks (OSNs) are a cornerstone in modern society, serving as platforms for diverse content consumption by millions of users each day. However, the challenge of ensuring the accuracy of information shared on these platforms remains significant, especially with the widespread dissemination of disinformation. Social bots -- automated accounts designed to mimic human behavior, frequently spreading misinformation -- represent one of the critical problems of OSNs. The advent of Large…

Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model
Hang Xu, Alexandre Bousse, Alessandro Perelli
https://arxiv.org/abs/2507.18012 https://

Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model
Dual-energy X-ray Computed Tomography (DECT) constitutes an advanced technology which enables automatic decomposition of materials in clinical images without manual segmentation using the dependency of the X-ray linear attenuation with energy. However, most methods perform material decomposition in the image domain as a post-processing step after reconstruction but this procedure does not account for the beam-hardening effect and it results in sub-optimal results. In this work, we propose a dee…

Collaborative Texture Filtering
Tomas Akenine-M\"oller, Pontus Ebelin, Matt Pharr, Bartlomiej Wronski
https://arxiv.org/abs/2506.17770 https://…

Collaborative Texture Filtering
Recent advances in texture compression provide major improvements in compression ratios, but cannot use the GPU's texture units for decompression and filtering. This has led to the development of stochastic texture filtering (STF) techniques to avoid the high cost of multiple texel evaluations with such formats. Unfortunately, those methods can give undesirable visual appearance changes under magnification and may contain visible noise and flicker despite the use of spatiotemporal denoisers. Re…