arXiv:2505.13864v2 Announce Type: replace Abstract: Social networks have a small number of large hubs, and a large number of small dense communities. We propose a generative model that captures both hub and dense structures. Based on recent results about graphons…
arXiv:2507.12843v2 Announce Type: replace-cross Abstract: The distribution closeness testing (DCT) assesses whether the distance between a distribution pair is at least $epsilon$-far. Existing DCT methods mainly measure discrepancies between a distribution pair defined on discrete one-dimensional spaces (e.g., using total…
arXiv:2510.08554v1 Announce Type: cross Abstract: Diffusion language models (DLMs) enable parallel, order-agnostic generation with iterative refinement, offering a flexible alternative to autoregressive large language models (LLMs). However, adapting reinforcement learning (RL) fine-tuning to DLMs remains an open challenge because of…
arXiv:2510.08469v1 Announce Type: cross Abstract: We present a platform-agnostic modular architecture that addresses the increasingly fragmented landscape of quantum computing benchmarking by decoupling problem generation, circuit execution, and results analysis into independent, interoperable components. Supporting over 20 benchmark variants ranging…
arXiv:2510.07772v1 Announce Type: new Abstract: Large Language Models (LLMs) suffer from reliability issues on complex tasks, as existing decomposition methods are heuristic and rely on agent or manual decomposition. This work introduces a novel, systematic decomposition framework that we call…
arXiv:2510.07762v1 Announce Type: new Abstract: Graph domain adaptation (GDA) has achieved great attention due to its effectiveness in addressing the domain shift between train and test data. A significant bottleneck in existing graph domain adaptation methods is their reliance on…
arXiv:2510.07181v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have shown remarkable capabilities in spatial reasoning, yet they remain fundamentally limited to qualitative precision and lack the computational precision required for real-world robotics. Current approaches fail to leverage metric cues from…
arXiv:2510.07748v1 Announce Type: new Abstract: Large Language Models (LLMs) show promise in medicine but are prone to factual and logical errors, which is unacceptable in this high-stakes field. To address this, we introduce the “Haibu Mathematical-Medical Intelligent Agent” (MMIA), an…
arXiv:2510.04020v2 Announce Type: replace-cross Abstract: To address the dual challenges of inherent stochasticity and non-differentiable metrics in physical spatiotemporal forecasting, we propose Spatiotemporal Forecasting as Planning (SFP), a new paradigm grounded in Model-Based Reinforcement Learning. SFP constructs a novel Generative…
arXiv:2510.07733v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly adopted for automating survey paper generation cite{wang2406autosurvey, liang2025surveyx, yan2025surveyforge,su2025benchmarking,wen2025interactivesurvey}. Existing approaches typically extract content from a large collection of related papers and prompt LLMs to summarize them directly. However,…
