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arXiv:2602.15173v1 Announce Type: new Abstract: The use of large language models either as decision support systems, or in agentic workflows, is rapidly transforming the digital …
arXiv:2602.15212v1 Announce Type: new Abstract: In this paper, we introduce a secure wireless agentic AI network comprising one supervisor AI agent and multiple other AI …
arXiv:2602.15248v1 Announce Type: new Abstract: Invoice or payment dilution is the gap between the approved invoice amount and the actual collection is a significant source …
arXiv:2602.17676v1 Announce Type: new Abstract: The rapid deployment of Large Language Models and AI agents across critical societal and technical domains is hindered by persistent …
arXiv:2602.17826v1 Announce Type: new Abstract: Language models exhibit fundamental limitations -- hallucination, brittleness, and lack of formal grounding -- that are particularly problematic in high-stakes …
arXiv:2602.17831v1 Announce Type: new Abstract: Evaluating the reasoning capabilities of Large Language Models is increasingly challenging as models improve. Human curation of hard questions is …
arXiv:2602.17902v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to automate scientific workflows, yet their integration with heterogeneous computational tools remains ad …
arXiv:2602.17910v1 Announce Type: new Abstract: Traditional AI alignment primarily focuses on individual model outputs; however, autonomous agents in long-horizon workflows require sustained reliability across entire …
arXiv:2602.17990v1 Announce Type: new Abstract: LLM-based systems increasingly generate structured workflows for complex tasks. In practice, automatic evaluation of these workflows is difficult, because metric …
arXiv:2602.18025v1 Announce Type: new Abstract: Scalable robot policy pre-training has been hindered by the high cost of collecting high-quality demonstrations for each platform. In this …
arXiv:2602.18095v1 Announce Type: new Abstract: Natural language understanding requires interleaving textual and logical reasoning, yet large language models often fail to perform such reasoning reliably. …
arXiv:2602.18201v1 Announce Type: new Abstract: Unsupervised representations are widely assumed to be neutral with respect to sensitive attributes when those attributes are withheld from training. …