arXiv:2509.25271v1 Announce Type: new Abstract: Existing safety evaluation methods for large language models (LLMs) suffer from inherent limitations, including evaluator bias and detection failures arising from model homogeneity, which collectively undermine the robustness of risk evaluation processes. This paper seeks…
arXiv:2509.25279v1 Announce Type: new Abstract: Large Language Models (LLMs) are now widely used across many domains. With their rapid development, Reinforcement Learning with Verifiable Rewards (RLVR) has surged in recent months to enhance their reasoning and understanding abilities. However, its…
arXiv:2509.25252v1 Announce Type: new Abstract: “The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.” Large Language Models have conquered natural language but remain prisoners of their own probabilistic nature–confidently hallucinating facts they never truly knew.…
arXiv:2509.25260v1 Announce Type: new Abstract: The extent to which decoder-only language models (LMs) engage in planning, that is, organizing intermediate computations to support coherent long-range generation, remains an open and important question, with implications for interpretability, reliability, and principled model…
arXiv:2509.25250v1 Announce Type: new Abstract: The rise of AI-native Low-Code/No-Code (LCNC) platforms enables autonomous agents capable of executing complex, long-duration business processes. However, a fundamental challenge remains: memory management. As agents operate over extended periods, they face “memory inflation” and…
arXiv:2509.25244v1 Announce Type: new Abstract: Purpose: Neo Grounded Theory (NGT) integrates vector clustering with multi agent systems to resolve qualitative research’s scale depth paradox, enabling analysis of massive datasets in hours while preserving interpretive rigor. Methods: We compared NGT against…
arXiv:2509.25239v1 Announce Type: new Abstract: Chain-of-Thought (CoT) elicits reasoning in large language models by explicitly generating intermediate steps in natural language. In contrast, Latent Thought in looped models operates directly in the continuous latent space, enabling computation beyond discrete linguistic…
arXiv:2509.26030v1 Announce Type: cross Abstract: The Muon optimizer is consistently faster than Adam in training Large Language Models (LLMs), yet the mechanism underlying its success remains unclear. This paper demystifies this mechanism through the lens of associative memory. By ablating…
arXiv:2509.25282v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly capable of orchestrating complex tasks in low-code environments. However, these agents often exhibit hallucinations and logical inconsistencies because their inherent reasoning mechanisms rely on probabilistic associations rather than…
arXiv:2509.26200v1 Announce Type: cross Abstract: This paper introduces a novel framework for proactive cross-domain resource orchestration in 6G RAN-Edge networks, featuring large language model (LLM)-augmented agents. The system comprises specialized RAN (energy efficiency) and Edge (latency assurance) agents that engage…
