arXiv:2509.16839v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities not only in language generation but also in advancing scientific discovery. A growing body of work has explored ways to improve their reasoning, from self-consistency and chain-of-thought…
arXiv:2509.09498v2 Announce Type: replace Abstract: Long-term multi-agent systems inevitably generate vast amounts of trajectories and historical interactions, which makes efficient memory management essential for both performance and scalability. Existing methods typically depend on vector retrieval and hierarchical storage, yet they…
arXiv:2509.16859v1 Announce Type: new Abstract: Determining whether another system, biological or artificial, possesses phenomenal consciousness has long been a central challenge in consciousness studies. This attribution problem has become especially pressing with the rise of large language models and other…
arXiv:2405.04760v5 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in a variety of application domains, including cybersecurity. As the volume and sophistication of cyber threats continue to…
arXiv:2509.16865v1 Announce Type: new Abstract: Combinatorial optimization (CO) problems, central to decision-making scenarios like logistics and manufacturing, are traditionally solved using problem-specific algorithms requiring significant domain expertise. While large language models (LLMs) have shown promise in automating CO problem solving,…
arXiv:2501.08760v2 Announce Type: replace-cross Abstract: Translating configurations between different network devices is a common yet challenging task in modern network operations. This challenge arises in typical scenarios such as replacing obsolete hardware and adapting configurations to emerging paradigms like Software…
arXiv:2509.16866v1 Announce Type: new Abstract: We introduce seqBench, a parametrized benchmark for probing sequential reasoning limits in Large Language Models (LLMs) through precise, multi-dimensional control over several key complexity dimensions. seqBench allows systematic variation of (1) the logical depth, defined…
arXiv:2502.13767v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (AI). We argue that successfully deploying AI software engineers requires a level…
arXiv:2509.16891v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated impressive reasoning and planning abilities in textual domains and can effectively follow instructions for complex tasks, their capacity for spatial understanding and reasoning remains limited. Such capabilities, however,…
arXiv:2504.08771v2 Announce Type: replace-cross Abstract: This paper proposes a generative method to dynamically simulate users’ short video watching journey for watch time prediction in short video recommendation. Unlike existing methods that rely on multimodal features for video content understanding, our…
