Tootfinder

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats
Quanyan Zhu
https://arxiv.org/abs/2507.10621 https://

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats
Protecting cyberspace requires not only advanced tools but also a shift in how we reason about threats, trust, and autonomy. Traditional cybersecurity methods rely on manual responses and brittle heuristics. To build proactive and intelligent defense systems, we need integrated theoretical frameworks and software tools. Game theory provides a rigorous foundation for modeling adversarial behavior, designing strategic defenses, and enabling trust in autonomous systems. Meanwhile, software tools p…

Reimagining Dance: Real-time Music Co-creation between Dancers and AI
Olga Vechtomova, Jeff Bos
https://arxiv.org/abs/2506.12008 https://

Reimagining Dance: Real-time Music Co-creation between Dancers and AI
Dance performance traditionally follows a unidirectional relationship where movement responds to music. While AI has advanced in various creative domains, its application in dance has primarily focused on generating choreography from musical input. We present a system that enables dancers to dynamically shape musical environments through their movements. Our multi-modal architecture creates a coherent musical composition by intelligently combining pre-recorded musical clips in response to dance…

Intelligibility of Text-to-Speech Systems for Mathematical Expressions
Sujoy Roychowdhury, H. G. Ranjani, Sumit Soman, Nishtha Paul, Subhadip Bandyopadhyay, Siddhanth Iyengar
https://arxiv.org/abs/2506.11086

Intelligibility of Text-to-Speech Systems for Mathematical Expressions
There has been limited evaluation of advanced Text-to-Speech (TTS) models with Mathematical eXpressions (MX) as inputs. In this work, we design experiments to evaluate quality and intelligibility of five TTS models through listening and transcribing tests for various categories of MX. We use two Large Language Models (LLMs) to generate English pronunciation from LaTeX MX as TTS models cannot process LaTeX directly. We use Mean Opinion Score from user ratings and quantify intelligibility through…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/6]:
- A dancing bear, a colleague, or a sharpened toolbox? The cautious adoption of generative AI techn...
Rongqian Ma, Meredith Dedema, Andrew Cox

This https://arxiv.org/abs/2504.11284 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
Bipartite ranking is a fundamental supervised learning problem, with the goal of learning a ranking over instances with maximal Area Under the ROC Curve (AUC) against a single binary target label. However, one may often observe multiple binary target labels, e.g., from distinct human annotators. How can one synthesize such labels into a single coherent ranking? In this work, we formally analyze two approaches to this problem -- loss aggregation and label aggregation -- by characterizing their B…

Cynthia Kaiser, the FBI's ex-deputy assistant director leading cyber policy, intel, and engagement, is leaving to join cybersecurity company Halcyon as SVP (Tim Starks/CyberScoop)
https://cyberscoop.com/cynthia-kaiser-fbi-halcyon-ransomware/

Top FBI cyber official Cynthia Kaiser exits for Halcyon
The 20-year bureau pro wants to see what it’s like to fight ransomware from the private sector.

UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
https://arxiv.org/abs/2506.08045

UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs
Agentic UAVs represent a new frontier in autonomous aerial intelligence, integrating perception, decision-making, memory, and collaborative planning to operate adaptively in complex, real-world environments. Driven by recent advances in Agentic AI, these systems surpass traditional UAVs by exhibiting goal-driven behavior, contextual reasoning, and interactive autonomy. We provide a comprehensive foundation for understanding the architectural components and enabling technologies that distinguish…

FBI: Play ransomware gang has attacked 600 organizations since 2023 https://therecord.media/play-ransomware-gang-fbi-update-900-attacks

Series D, Episode 03 - Traitor
PRACTOR: A live target general?
GENERAL: Sleer's people picked up a rebel who'd been sent on an intelligence mission. He was adapted, given a tracer to swallow and sent back to rejoin his friends. When he reached their base, my missile commander just pressed the button.
https://blake.torp…

RADAR: Recall Augmentation through Deferred Asynchronous Retrieval
Amit Jaspal, Qian Dang, Ajantha Ramineni
https://arxiv.org/abs/2506.07261 https://

RADAR: Recall Augmentation through Deferred Asynchronous Retrieval
Modern large-scale recommender systems employ multi-stage ranking funnel (Retrieval, Pre-ranking, Ranking) to balance engagement and computational constraints (latency, CPU). However, the initial retrieval stage, often relying on efficient but less precise methods like K-Nearest Neighbors (KNN), struggles to effectively surface the most engaging items from billion-scale catalogs, particularly distinguishing highly relevant and engaging candidates from merely relevant ones. We introduce Recall A…