Tootfinder

DEXTER-LLM: Dynamic and Explainable Coordination of Multi-Robot Systems in Unknown Environments via Large Language Models
Yuxiao Zhu, Junfeng Chen, Xintong Zhang, Meng Guo, Zhongkui Li
https://arxiv.org/abs/2508.14387

DEXTER-LLM: Dynamic and Explainable Coordination of Multi-Robot Systems in Unknown Environments via Large Language Models
Online coordination of multi-robot systems in open and unknown environments faces significant challenges, particularly when semantic features detected during operation dynamically trigger new tasks. Recent large language model (LLMs)-based approaches for scene reasoning and planning primarily focus on one-shot, end-to-end solutions in known environments, lacking both dynamic adaptation capabilities for online operation and explainability in the processes of planning. To address these issues, a …

Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes
Zilong Lin, Zichuan Li, Xiaojing Liao, XiaoFeng Wang
https://arxiv.org/abs/2508.12622

Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes
The advancement of AI technologies, particularly Large Language Models (LLMs), has transformed computing while introducing new security and privacy risks. Prior research shows that cybercriminals are increasingly leveraging uncensored LLMs (ULLMs) as backends for malicious services. Understanding these ULLMs has been hindered by the challenge of identifying them among the vast number of open-source LLMs hosted on platforms like Hugging Face. In this paper, we present the first systematic study …

MGT-Prism: Enhancing Domain Generalization for Machine-Generated Text Detection via Spectral Alignment
Shengchao Liu, Xiaoming Liu, Chengzhengxu Li, Zhaohan Zhang, Guoxin Ma, Yu Lan, Shuai Xiao
https://arxiv.org/abs/2508.13768

MGT-Prism: Enhancing Domain Generalization for Machine-Generated Text Detection via Spectral Alignment
Large Language Models have shown growing ability to generate fluent and coherent texts that are highly similar to the writing style of humans. Current detectors for Machine-Generated Text (MGT) perform well when they are trained and tested in the same domain but generalize poorly to unseen domains, due to domain shift between data from different sources. In this work, we propose MGT-Prism, an MGT detection method from the perspective of the frequency domain for better domain generalization. Our…

Rapidly Adapting to New Voice Spoofing: Few-Shot Detection of Synthesized Speech Under Distribution Shifts
Ashi Garg, Zexin Cai, Henry Li Xinyuan, Leibny Paola Garc\'ia-Perera, Kevin Duh, Sanjeev Khudanpur, Matthew Wiesner, Nicholas Andrews
https://arxiv.org/abs/2508.13320

Rapidly Adapting to New Voice Spoofing: Few-Shot Detection of Synthesized Speech Under Distribution Shifts
We address the challenge of detecting synthesized speech under distribution shifts -- arising from unseen synthesis methods, speakers, languages, or audio conditions -- relative to the training data. Few-shot learning methods are a promising way to tackle distribution shifts by rapidly adapting on the basis of a few in-distribution samples. We propose a self-attentive prototypical network to enable more robust few-shot adaptation. To evaluate our approach, we systematically compare the performa…

What's in the Box? Reasoning about Unseen Objects from Multimodal Cues
Lance Ying, Daniel Xu, Alicia Zhang, Katherine M. Collins, Max H. Siegel, Joshua B. Tenenbaum
https://arxiv.org/abs/2506.14212

What's in the Box? Reasoning about Unseen Objects from Multimodal Cues
People regularly make inferences about objects in the world that they cannot see by flexibly integrating information from multiple sources: auditory and visual cues, language, and our prior beliefs and knowledge about the scene. How are we able to so flexibly integrate many sources of information to make sense of the world around us, even if we have no direct knowledge? In this work, we propose a neurosymbolic model that uses neural networks to parse open-ended multimodal inputs and then applie…

PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning
Yuhui Shi, Yehan Yang, Qiang Sheng, Hao Mi, Beizhe Hu, Chaoxi Xu, Juan Cao
https://arxiv.org/abs/2506.15683

PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning
With the popularity of large language models (LLMs), undesirable societal problems like misinformation production and academic misconduct have been more severe, making LLM-generated text detection now of unprecedented importance. Although existing methods have made remarkable progress, a new challenge posed by text from privately tuned LLMs remains underexplored. Users could easily possess private LLMs by fine-tuning an open-source one with private corpora, resulting in a significant performanc…

Enhancing Cross-task Transfer of Large Language Models via Activation Steering
Xinyu Tang, Zhihao Lv, Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao, Zujie Wen, Zhiqiang Zhang, Jun Zhou
https://arxiv.org/abs/2507.13236

Enhancing Cross-task Transfer of Large Language Models via Activation Steering
Large language models (LLMs) have shown impressive abilities in leveraging pretrained knowledge through prompting, but they often struggle with unseen tasks, particularly in data-scarce scenarios. While cross-task in-context learning offers a direct solution for transferring knowledge across tasks, it still faces critical challenges in terms of robustness, scalability, and efficiency. In this paper, we investigate whether cross-task transfer can be achieved via latent space steering without par…

Machine Mirages: Defining the Undefined
Hamidou Tembine
https://arxiv.org/abs/2506.13990 https://arxiv.org/pdf/2506.13990

Machine Mirages: Defining the Undefined
As multimodal machine intelligence systems started achieving average animal-level and average human-level fluency in many measurable tasks in processing images, language, and sound, they began to exhibit a new class of cognitive aberrations: machine mirages. These include delusion, illusion, confabulation, hallucination, misattribution error, semantic drift, semantic compression, exaggeration, causal inference failure, uncanny valley of perception, bluffing-patter-bullshitting, cognitive stereo…

Universal Jailbreak Suffixes Are Strong Attention Hijackers
Matan Ben-Tov, Mor Geva, Mahmood Sharif
https://arxiv.org/abs/2506.12880 https://

Universal Jailbreak Suffixes Are Strong Attention Hijackers
We study suffix-based jailbreaks$\unicode{x2013}$a powerful family of attacks against large language models (LLMs) that optimize adversarial suffixes to circumvent safety alignment. Focusing on the widely used foundational GCG attack (Zou et al., 2023), we observe that suffixes vary in efficacy: some markedly more universal$\unicode{x2013}$generalizing to many unseen harmful instructions$\unicode{x2013}$than others. We first show that GCG's effectiveness is driven by a shallow, critical mechani…

Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages
Andrea Nasuto, Stefano Maria Iacus, Francisco Rowe, Devika Jain
https://arxiv.org/abs/2508.06435

Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages
Large language models (LLMs) are transforming social-science research by enabling scalable, precise analysis. Their adaptability raises the question of whether knowledge acquired through fine-tuning in a few languages can transfer to unseen languages that only appeared during pre-training. To examine this, we fine-tune lightweight LLaMA 3.2-3B models on monolingual, bilingual, or multilingual data sets to classify immigration-related tweets from X/Twitter across 13 languages, a domain character…