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Exploring Photon Blockade in Multimode Jaynes-Cummings Models with Two-Photon Dissipation
Caden McCollum, Imran M. Mirza
https://arxiv.org/abs/2509.09084 https://

Exploring Photon Blockade in Multimode Jaynes-Cummings Models with Two-Photon Dissipation
The photon blockade phenomenon, a promising tool for realizing efficient single-photon sources, is the central focus of our work. We study this phenomenon within the context of the multimode extension of the Jaynes-Cummings model, incorporating two-photon dissipation and external coherent driving. Operating in the weak-driving regime, we confine our analysis to the two-excitation sector of the Hilbert space, initially exploring the single-mode case and then focusing on the corresponding multimo…

Explore, Listen, Inspect: Supporting Multimodal Interaction with 3D Surface and Point Data Visualizations
Sanchita S. Kamath, Aziz N. Zeidieh, JooYoung Seo
https://arxiv.org/abs/2508.08554

Explore, Listen, Inspect: Supporting Multimodal Interaction with 3D Surface and Point Data Visualizations
Blind and low-vision (BLV) users remain largely excluded from three-dimensional (3D) surface and point data visualizations due to the reliance on visual interaction. Existing approaches inadequately support non-visual access, especially in browser-based environments. This study introduces DIXTRAL, a hosted web-native system, co-designed with BLV researchers to address these gaps through multimodal interaction. Conducted with two blind and one sighted researcher, this study took place over susta…

Towards Multimodal Sentiment Analysis via Contrastive Cross-modal Retrieval Augmentation and Hierachical Prompts
Xianbing Zhao, Shengzun Yang, Buzhou Tang, Ronghuan Jiang
https://arxiv.org/abs/2508.07666

Towards Multimodal Sentiment Analysis via Contrastive Cross-modal Retrieval Augmentation and Hierachical Prompts
Multimodal sentiment analysis is a fundamental problem in the field of affective computing. Although significant progress has been made in cross-modal interaction, it remains a challenge due to the insufficient reference context in cross-modal interactions. Current cross-modal approaches primarily focus on leveraging modality-level reference context within a individual sample for cross-modal feature enhancement, neglecting the potential cross-sample relationships that can serve as sample-level …

MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision
Zhonghao Yan, Muxi Diao, Yuxuan Yang, Jiayuan Xu, Kaizhou Zhang, Ruoyan Jing, Lele Yang, Yanxi Liu, Kongming Liang, Zhanyu Ma
https://arxiv.org/abs/2508.08177

MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision
Accurately grounding regions of interest (ROIs) is critical for diagnosis and treatment planning in medical imaging. While multimodal large language models (MLLMs) combine visual perception with natural language, current medical-grounding pipelines still rely on supervised fine-tuning with explicit spatial hints, making them ill-equipped to handle the implicit queries common in clinical practice. This work makes three core contributions. We first define Unified Medical Reasoning Grounding (UMRG…

Language Models Can Understand Spectra: A Multimodal Model for Molecular Structure Elucidation
Yunyue Su, Jiahui Chen, Zao Jiang, Zhenyi Zhong, Liang Wang, Qiang Liu
https://arxiv.org/abs/2508.08441

Language Models Can Understand Spectra: A Multimodal Model for Molecular Structure Elucidation
Structure elucidation is a fundamental technique for understanding the microscopic composition of matter and is widely applied across various disciplines in the natural sciences and engineering. However, existing methods often rely heavily on prior databases or known structural information, making it difficult to resolve unknown structures. In addition, complex structures typically require the joint analysis of multiple spectroscopic modalities. This process heavily depends on expert domain kno…

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Dynamic Structural Recovery Parameters Enhance Prediction of Visual Outcomes After Macular Hole Surgery
Yinzheng Zhao, Zhihao Zhao, Rundong Jiang, Louisa Sackewitz, Quanmin Liang, Mathias Maier, Daniel Zapp, Peter Charbel Issa, Mohammad Ali Nasseri
https://arxiv.org/abs/2509.09227

Dynamic Structural Recovery Parameters Enhance Prediction of Visual Outcomes After Macular Hole Surgery
Purpose: To introduce novel dynamic structural parameters and evaluate their integration within a multimodal deep learning (DL) framework for predicting postoperative visual recovery in idiopathic full-thickness macular hole (iFTMH) patients. Methods: We utilized a publicly available longitudinal OCT dataset at five stages (preoperative, 2 weeks, 3 months, 6 months, and 12 months). A stage specific segmentation model delineated related structures, and an automated pipeline extracted quantitativ…

Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering
Yanbin Wei, Jiangyue Yan, Chun Kang, Yang Chen, Hua Liu, James T. Kwok, Yu Zhang
https://arxiv.org/abs/2508.06345

Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering
Large Multimodal Models (LMMs) have shown generalized zero-shot capabilities in diverse domain question-answering (QA) tasks, including graph QA that involves complex graph topologies. However, most current approaches use only a single type of graph representation, namely Topology Representation Form (TRF), such as prompt-unified text descriptions or style-fixed visual styles. Those "one-size-fits-all" approaches fail to consider the specific preferences of different models or tasks, often lead…

NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints
Changyao Tian, Hao Li, Gen Luo, Xizhou Zhu, Weijie Su, Hanming Deng, Jinguo Zhu, Jie Shao, Ziran Zhu, Yunpeng Liu, Lewei Lu, Wenhai Wang, Hongsheng Li, Jifeng Dai
https://arxiv.org/abs/2510.08565 …

NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints
Compositional training has been the de-facto paradigm in existing Multimodal Large Language Models (MLLMs), where pre-trained vision encoders are connected with pre-trained LLMs through continuous multimodal pre-training. However, the multimodal scaling property of this paradigm remains difficult to explore due to the separated training. In this paper, we focus on the native training of MLLMs in an end-to-end manner and systematically study its design space and scaling property under a practica…

MM-HELIX: Boosting Multimodal Long-Chain Reflective Reasoning with Holistic Platform and Adaptive Hybrid Policy Optimization
Xiangyu Zhao, Junming Lin, Tianhao Liang, Yifan Zhou, Wenhao Chai, Yuzhe Gu, Weiyun Wang, Kai Chen, Gen Luo, Wenwei Zhang, Junchi Yan, Hua Yang, Haodong Duan, Xue Yang
https://arxiv.org/abs/2510.08540

MM-HELIX: Boosting Multimodal Long-Chain Reflective Reasoning with Holistic Platform and Adaptive Hybrid Policy Optimization
While current Multimodal Large Language Models (MLLMs) have demonstrated proficiency in reasoning tasks such as mathematics and logic, their capacity for long-chain reflective reasoning, a prerequisite for solving complex real-world problems, remains largely underexplored. In this work, we first conduct an extensive empirical investigation to evaluate this capability. Leveraging a carefully designed data synthesis engine, we construct MM-HELIX, a multimodal benchmark consisting 1,260 samples of…