LINGO: Latent Initialization and Gradient Optimization for Sparse-view X-ray Novel View Synthesis and CT Reconstruction with 3D Gaussian Splatting
为解决稀疏视角X射线成像中的结构模糊和噪声累积问题，提出LINGO框架，结合潜在初始化与梯度优化，提升点云结构完整性和训练速度。
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Separating Stream Stability from Long-Term Recall in Language Models
本文区分了流式语言模型的稳定性与长期记忆问题，通过引入三个时间范围并提出ThreeH评估方法来解决这一区分问题。
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Evidence, Logic, and Compliance: Multi-Agent Structured Graph Reasoning with Expert Arbitration for Medical Referral
为解决医学转诊中的信息过载和非结构化协作问题，提出多智能体结构化图推理框架MASGR，通过构建临床推理图和引入知识引导仲裁机制提高转诊准确性。
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Linear Coding of LTI Sources Over Vector Gaussian Channels: A Majorization Approach
研究了通过反馈的功率受限并行高斯信道传输离散时间LTI向量源状态的问题，提出基于主化方法的线性编码解码器设计。
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Entity-Faithful Repair of Synthetic Supervision for Zero-Shot Image Captioning
This work addresses the challenge in zero-shot image captioning where synthetic training data generated by text-to-image models often suffers from fine-grained entity misalignment—such as missing objects or mislocalized attributes—leading to distorted supervision signals. To mitigate this, the authors propose ReCap, a framework that explicitly detects image entities and guides caption rewriting to achieve fine-grained image-text alignment. ReCap further incorporates an adaptive dynamic weighting strategy to downweight unreliable synthetic samples during training. By shifting data refinement from implicit global matching to explicit entity-level realignment, the method introduces a plug-and-play mechanism for fine-grained correction. Experiments demonstrate that ReCap achieves state-of-the-art performance on both in-domain and cross-domain zero-shot image captioning benchmarks, significantly improving caption consistency and supervision fidelity.
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LINGO: Latent Initialization and Gradient Optimization for Sparse-view X-ray Novel View Synthesis and CT Reconstruction with 3D Gaussian Splatting
为解决稀疏视角X射线成像中的结构模糊和噪声累积问题，提出LINGO框架，结合潜在初始化与梯度优化，提升点云结构完整性和训练速度。
0 citationsRead paper
Separating Stream Stability from Long-Term Recall in Language Models
本文区分了流式语言模型的稳定性与长期记忆问题，通过引入三个时间范围并提出ThreeH评估方法来解决这一区分问题。
0 citationsRead paper
Evidence, Logic, and Compliance: Multi-Agent Structured Graph Reasoning with Expert Arbitration for Medical Referral
为解决医学转诊中的信息过载和非结构化协作问题，提出多智能体结构化图推理框架MASGR，通过构建临床推理图和引入知识引导仲裁机制提高转诊准确性。
0 citationsRead paper
Linear Coding of LTI Sources Over Vector Gaussian Channels: A Majorization Approach
研究了通过反馈的功率受限并行高斯信道传输离散时间LTI向量源状态的问题，提出基于主化方法的线性编码解码器设计。
0 citationsRead paper
Entity-Faithful Repair of Synthetic Supervision for Zero-Shot Image Captioning
This work addresses the challenge in zero-shot image captioning where synthetic training data generated by text-to-image models often suffers from fine-grained entity misalignment—such as missing objects or mislocalized attributes—leading to distorted supervision signals. To mitigate this, the authors propose ReCap, a framework that explicitly detects image entities and guides caption rewriting to achieve fine-grained image-text alignment. ReCap further incorporates an adaptive dynamic weighting strategy to downweight unreliable synthetic samples during training. By shifting data refinement from implicit global matching to explicit entity-level realignment, the method introduces a plug-and-play mechanism for fine-grained correction. Experiments demonstrate that ReCap achieves state-of-the-art performance on both in-domain and cross-domain zero-shot image captioning benchmarks, significantly improving caption consistency and supervision fidelity.
0 citationsRead paper