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arxiv.org arXiv
arxiv.org › abs › 2412.14810v2
In healthcare, the integration of multimodal data is pivotal for developing comprehensive diagnostic and predictive models. However, managing missing data remains a significant challenge in real-world...
arxiv.org arXiv
arxiv.org › abs › 2312.08935v3
In this paper, we present an innovative process-oriented math process reward model called \textbf{Math-Shepherd}, which assigns a reward score to each step of math problem solutions. The training of M...
arxiv.org arXiv
arxiv.org › abs › 2406.18629v1
Mathematical reasoning presents a significant challenge for Large Language Models (LLMs) due to the extensive and precise chain of reasoning required for accuracy. Ensuring the correctness of each rea...
arxiv.org arXiv
arxiv.org › abs › 2602.10604v2
We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most when building agents: ...
arxiv.org arXiv
arxiv.org › abs › 2502.08941v3
This paper analyzes multi-step temporal difference (TD)-learning algorithms within the ``deadly triad'' scenario, characterized by linear function approximation, off-policy learning, and bootstrapping...
arxiv.org arXiv
arxiv.org › abs › 2507.16632v3
This paper presents Step-Audio 2, an end-to-end multi-modal large language model designed for industry-strength audio understanding and speech conversation. By integrating a latent audio encoder and r...
arxiv.org arXiv
arxiv.org › abs › 1708.00023v2
As the effort to scale up existing quantum hardware proceeds, it becomes necessary to schedule quantum gates in a way that minimizes the number of operations. There are three constraints that have to ...
arxiv.org arXiv
arxiv.org › abs › 2511.18834v1
With the success of flow matching in visual generation, sampling efficiency remains a critical bottleneck for its practical application. Among flow models' accelerating methods, ReFlow has been someho...
arxiv.org arXiv
arxiv.org › abs › 2511.04247v2
Multimodal co-embedding models, especially CLIP, have advanced the state of the art in zero-shot classification and multimedia information retrieval in recent years by aligning images and text in a sh...
arxiv.org arXiv
arxiv.org › abs › 2508.10669v1
Conversational recommender systems (CRSs) aim to proactively capture user preferences through natural language dialogue and recommend high-quality items. To achieve this, CRS gathers user preferences ...