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arXiv:2603.15773v1 Announce Type: new Abstract: This work investigates how effectively large language models (LLMs) and their tokenization schemes represent and generate Arabic root-pattern morphology, probing …
arXiv:2603.15677v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly central to clinician workflows, spanning clinical decision support, medical education, and patient communication. However, …
arXiv:2603.15653v1 Announce Type: new Abstract: Long-context handling remains a core challenge for language models: even with extended context windows, models often fail to reliably extract, …
arXiv:2603.16313v1 Announce Type: new Abstract: Electronic control units (ECUs) embedded within modern vehicles generate a large number of asynchronous events known as diagnostic trouble codes …
arXiv:2603.16307v1 Announce Type: new Abstract: Remote sensing underpins crucial applications such as disaster relief and ecological field surveys, where systems must understand complex scenes and …
arXiv:2603.16264v1 Announce Type: new Abstract: Theory of Mind (ToM) refers to the ability to reason about others' mental states, and higher-order ToM involves considering that …
arXiv:2603.16210v1 Announce Type: new Abstract: Safety alignment in large language models (LLMs) is commonly implemented as a single static policy embedded in model parameters. However, …
arXiv:2603.16207v1 Announce Type: new Abstract: As Large Language Models (LLMs) transition from information providers to embodied agents in the Internet of Things (IoT), they face …
arXiv:2603.16197v1 Announce Type: new Abstract: Public leaderboards increasingly suggest that large language models (LLMs) surpass human experts on benchmarks spanning academic knowledge, law, and programming. …
arXiv:2603.16161v1 Announce Type: new Abstract: Agentic Reinforcement Learning (RL) shows promise for complex tasks, but Text-to-SQL remains mostly restricted to single-turn paradigms. A primary bottleneck …
arXiv:2603.16148v1 Announce Type: new Abstract: We ask whether a pure spiking backbone can learn large-scale language modeling from random initialization, without Transformer distillation. We introduce …
arXiv:2603.16110v1 Announce Type: new Abstract: Enterprise IT support is constrained by heterogeneous devices, evolving policies, and long-tail failure modes that are difficult to resolve centrally. …