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arXiv:2603.08835v1 Announce Type: new Abstract: The rapid adoption of LLM-based agentic systems has produced a rich ecosystem of frameworks (smolagents, LangGraph, AutoGen, CAMEL, LlamaIndex, i.a.). …
arXiv:2603.09486v1 Announce Type: new Abstract: The encounter between human reasoning and generative artificial intelligence (GenAI) cannot be adequately described by inherited metaphors of tool use, …
arXiv:2603.09619v1 Announce Type: new Abstract: As artificial intelligence (AI) systems evolve from stateless chatbots to autonomous multi-step agents, prompt engineering (PE), the discipline of crafting …
arXiv:2603.09341v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) helps large language models (LLMs) answer knowledge-intensive and time-sensitive questions by conditioning generation on external evidence. However, …
arXiv:2603.09774v1 Announce Type: new Abstract: Achieving robust spatial reasoning remains a fundamental challenge for current Multimodal Foundation Models (MFMs). Existing methods either overfit statistical shortcuts …
arXiv:2603.09533v1 Announce Type: new Abstract: This study proposes a novel methodology for generating personalized fake news debunking messages by prompting Large Language Models (LLMs) with …
arXiv:2603.08899v1 Announce Type: new Abstract: Speculative decoding has emerged as a powerful approach to accelerate large language model (LLM) inference by employing lightweight draft models …
arXiv:2603.09157v1 Announce Type: new Abstract: As large language models evolve from conversational assistants to autonomous agents, ensuring trustworthiness requires a fundamental shift from post-hoc evaluation …
arXiv:2603.09481v1 Announce Type: new Abstract: We present GenePlan (GENeralized Evolutionary Planner), a novel framework that leverages large language model (LLM) assisted evolutionary algorithms to generate …
arXiv:2603.08999v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong reasoning performance through chain-of-thought (CoT) reasoning, yet often generate unnecessarily long reasoning paths that …
arXiv:2603.08879v1 Announce Type: new Abstract: Accessing sensitive patient data for machine learning is challenging due to privacy concerns. Datasets with annotations of personally identifiable information …
arXiv:2603.09434v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed across diverse real-world applications and user communities. As such, it is crucial that …