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arXiv:2603.11745v1 Announce Type: new Abstract: Real-world multivariate time series, particularly in critical infrastructure such as electrical power grids, are often corrupted by noise and anomalies …
arXiv:2603.11277v1 Announce Type: new Abstract: The rapid proliferation of large language model (LLM)-based agentic systems raises critical concerns regarding digital sovereignty, environmental sustainability, regulatory compliance, …
arXiv:2603.11339v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to financial analysis, yet their ability to audit structured financial statements under explicit …
arXiv:2603.11399v1 Announce Type: new Abstract: Users on e-commerce platforms can be uncertain about their preferences early in their search. Queries to recommendation systems are frequently …
arXiv:2603.11214v1 Announce Type: new Abstract: We evaluate the autonomous cyber-attack capabilities of frontier AI models on two purpose-built cyber ranges-a 32-step corporate network attack and …
arXiv:2603.11279v1 Announce Type: new Abstract: The immense number of parameters and deep neural networks make large language models (LLMs) rival the complexity of human brains, …
arXiv:2603.11495v1 Announce Type: new Abstract: Tool-calling empowers Large Language Models (LLMs) to interact with external environments. However, current methods often struggle to handle massive and …
arXiv:2603.11513v1 Announce Type: new Abstract: Retrieval augmented generation RAG is widely deployed to improve factual accuracy in language models yet it remains unclear whether smaller …
arXiv:2603.11545v1 Announce Type: new Abstract: We present an agentic AI framework for autonomous multimodal query processing that coordinates specialized tools across text, image, audio, video, …
arXiv:2603.11564v1 Announce Type: new Abstract: The Key-Value (KV) cache is crucial for efficient Large Language Models (LLMs) inference, but excessively long contexts drastically increase KV …
arXiv:2603.11578v1 Announce Type: new Abstract: Simultaneous machine translation (SiMT) has traditionally relied on offline machine translation models coupled with human-engineered heuristics or learned policies. We …
arXiv:2603.11583v1 Announce Type: new Abstract: The success of a Large Language Model (LLM) task depends heavily on its prompt. Most use-cases specify prompts using natural …