Tootfinder

Zak-OFDM: Low Complexity Joint Equalization of OFDM Carriers in Doubly-Spread Channels
Saif Khan Mohammed, Sandesh Rao Mattu, Nishant Mehrotra, Venkatesh Khammammetti, Robert Calderbank
https://arxiv.org/abs/2506.23045

Zak-OFDM: Low Complexity Joint Equalization of OFDM Carriers in Doubly-Spread Channels
We communicate over wireless channels by first estimating and then equalizing the effective channel. In Zak-OTFS (orthogonal time frequency space) modulation the carrier waveform is a pulse in the delay-Doppler (DD) domain, formally a quasi-periodic localized function with specific periods along delay and Doppler. When the channel delay spread is less than the delay period, and the channel Doppler spread is less than the Doppler period, the response to a single Zak-OTFS carrier provides an imag…

System-performance and cost modeling of Large Language Model training and inference
Wenzhe Guo, Joyjit Kundu, Uras Tos, Weijiang Kong, Giuliano Sisto, Timon Evenblij, Manu Perumkunnil
https://arxiv.org/abs/2507.02456

System-performance and cost modeling of Large Language Model training and inference
Large language models (LLMs), based on transformer architectures, have revolutionized numerous domains within artificial intelligence, science, and engineering due to their exceptional scalability and adaptability. However, the exponential growth in LLM size and complexity has outpaced advancements in compute capacity, memory bandwidth, network performance, and cost efficiency, posing significant challenges to their scalability on distributed systems. To address these limitations, alternative m…

From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows
Mohamed Amine Ferrag, Norbert Tihanyi, Djallel Hamouda, Leandros Maglaras, Merouane Debbah
https://arxiv.org/abs/2506.23260

From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows
Autonomous AI agents powered by large language models (LLMs) with structured function-calling interfaces have dramatically expanded capabilities for real-time data retrieval, complex computation, and multi-step orchestration. Yet, the explosive proliferation of plugins, connectors, and inter-agent protocols has outpaced discovery mechanisms and security practices, resulting in brittle integrations vulnerable to diverse threats. In this survey, we introduce the first unified, end-to-end threat m…

Minimizing Ray Tracing Memory Traffic through Quantized Structures and Ray Stream Tracing
Moritz Grauer, Johannes Hanika, Carsten Dachsbacher
https://arxiv.org/abs/2505.24653

Minimizing Ray Tracing Memory Traffic through Quantized Structures and Ray Stream Tracing
Memory bandwidth constraints continue to be a significant limiting factor in ray tracing performance, particularly as scene complexity grows and computational capabilities outpace memory access speeds. This paper presents a memory-efficient ray tracing methodology that integrates compressed data structures with ray stream techniques to reduce memory traffic. The approach implements compressed BVH and triangle representations to minimize acceleration structure size in combination with ray stream…

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Jonas Svedas, Hannah Watson, Nathan Laubeuf, Diksha Moolchandani, Abubakr Nada, Arjun Singh, Dwaipayan Biswas, James Myers, Debjyoti Bhattacharjee
https://arxiv.org/abs/2506.09275

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Distributed deep neural networks (DNNs) have become a cornerstone for scaling machine learning to meet the demands of increasingly complex applications. However, the rapid growth in model complexity far outpaces CMOS technology scaling, making sustainable and efficient system design a critical challenge. Addressing this requires coordinated co-design across software, hardware, and technology layers. Due to the prohibitive cost and complexity of deploying full-scale training systems, simulators …

Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning
Waylon Luo (Kent State University), Jiapeng Zhao (Cisco), Tong Zhan (Meta), Qiang Guan (Kent State University)
https://arxiv.org/abs/2506.10889

Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning
Present-day quantum systems face critical bottlenecks, including limited qubit counts, brief coherence intervals, and high susceptibility to errors-all of which obstruct the execution of large and complex circuits. The advancement of quantum algorithms has outpaced the capabilities of existing quantum hardware, making it difficult to scale computations effectively. Additionally, inconsistencies in hardware performance and pervasive quantum noise undermine system stability and computational accu…