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arXiv:2603.11353v1 Announce Type: new Abstract: Many assumptions that underpin human concepts of identity do not hold for machine minds that can be copied, edited, or …
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:2603.11770v1 Announce Type: new Abstract: This work describes an automatic text classification method implemented in a software tool called NETHIC, which takes advantage of the …
arXiv:2603.11768v1 Announce Type: new Abstract: Long-term memory has emerged as a foundational component of autonomous Large Language Model (LLM) agents, enabling continuous adaptation, lifelong multimodal …
arXiv:2603.11802v1 Announce Type: new Abstract: We introduce an expressive framework and algorithms for the semi-decentralized control of cooperative agents in environments with communication uncertainty. Whereas …
arXiv:2603.11535v1 Announce Type: new Abstract: Token-choice Mixture-of-Experts (TC-MoE) routes each token to a fixed number of experts, limiting dynamic computation allocation and requiring auxiliary losses …
arXiv:2603.11093v1 Announce Type: new Abstract: The development of high-level autonomous driving (AD) is shifting from perception-centric limitations to a more fundamental bottleneck, namely, a deficit …
arXiv:2603.11333v1 Announce Type: new Abstract: Short-video platforms are closed-loop, human-in-the-loop ecosystems where platform policy, creator incentives, and user behavior co-evolve. This feedback structure makes counterfactual …
arXiv:2603.11691v1 Announce Type: new Abstract: Offline multi-agent reinforcement learning (MARL) with multi-task datasets is challenging due to varying numbers of agents across tasks and the …
arXiv:2603.11067v1 Announce Type: new Abstract: Large language models (LLMs) achieve remarkable performance, yet further gains often require costly training. This has motivated growing interest in …
arXiv:2603.11559v1 Announce Type: new Abstract: Large language models perform reliably when their outputs can be checked: solving equations, writing code, retrieving facts. They perform differently …
arXiv:2603.11689v1 Announce Type: new Abstract: Frontier Multimodal Large Language Models (MLLMs) exhibit remarkable capabilities in Visual-Language Comprehension (VLC) tasks. However, they are often deployed as …