arXiv:2509.16456v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in various domains, showing impressive potential on different tasks. Recently, reasoning LLMs have been proposed to improve the textit{reasoning} or textit{thinking} capabilities of LLMs to solve complex problems.…
arXiv:2508.03251v2 Announce Type: replace Abstract: Modeling evolving interactions among entities is critical in many real-world tasks. For example, predicting driver maneuvers in traffic requires tracking how neighboring vehicles accelerate, brake, and change lanes relative to one another over consecutive frames.…
arXiv:2509.16547v1 Announce Type: new Abstract: In this paper we investigate formal verification of extracted rules for Neural Networks under a complexity theoretic point of view. A rule is a global property or a pattern concerning a large portion of the…
arXiv:2410.16531v4 Announce Type: replace-cross Abstract: In-context learning (ICL) is a powerful technique for getting language models to perform complex tasks with no training updates. Prior work has established strong correlations between the number of in-context examples provided and the accuracy…
arXiv:2509.16561v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting enhances the math reasoning capability of large language models (LLMs) to a large margin. However, the mechanism underlying such improvements remains unexplored. In this paper, we present textbf{SalaMAnder} (textbf{S}htextbf{a}ptextbf{l}ey-btextbf{a}sed textbf{M}athematical Expression textbf{A}ttribution…
arXiv:2503.13222v3 Announce Type: replace-cross Abstract: Despite significant achievements in improving the instruction-following capabilities of large language models (LLMs), the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge. Real-world scenarios often require consistency across multiple instructions…
arXiv:2509.16578v1 Announce Type: new Abstract: Human mobility forecasting is important for applications such as transportation planning, urban management, and personalized recommendations. However, existing methods often fail to generalize to unseen users or locations and struggle to capture dynamic intent due…
arXiv:2505.17616v2 Announce Type: replace-cross Abstract: Agents powered by large language models (LLMs) have demonstrated strong planning and decision-making capabilities in complex embodied environments. However, such agents often suffer from inefficiencies in multi-turn interactions, frequently trapped in repetitive loops or issuing…
arXiv:2509.16590v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at understanding natural language but struggle with explicit commonsense reasoning. A recent trend of research suggests that the combination of LLM with robust symbolic reasoning systems can overcome this problem…
arXiv:2506.23085v2 Announce Type: replace-cross Abstract: The purpose of this paper is to explore a multi-modal approach to enhancing live broadcast engagement by developing a short video recommendation system that incorporates Multi-modal Graph Convolutional Networks (MMGCN) with user preferences. To provide…
