21 citations · 37 across the 19 of their papers we have counts for
11 papers · 1 filter
Uni-LaDiR: Latent Diffusion Unifies Multimodal Reasoning
Haoqiang Kang, Yizhe Zhang, Nikki Lijing Kuang +3
Multimodal models increasingly think with different modalities such as images, 3D point clouds, and robot states, not just text. Yet each modality is still encoded into its own rep…
DeliveryGym: An RL Environment for Long-Horizon Embodied Agent Planning with Adaptive Curriculum
Haoqiang Kang, Yiming Zhang, Yiyang Guo +5
Executable environments enable LLM agents to learn from the consequences of their actions. For embodied agents, those consequences extend beyond whether the current task succeeds:…
Scaffolding Minds: Optimizing Latent Visual Target Representations for Multimodal Reasoning
Haoqiang Kang, Yinpeng Chen, Luyang Liu +5
Latent reasoning has advanced multimodal reasoning through a two-stage training paradigm: (1) a helper image is encoded into latent tokens to teach visual chain-of-thought during a…
Balancing Efficiency and Efficacy: Training-Free Attention-Guided Switching Between Explicit and Latent Thoughts for MLLMs
Haoqian Kang, Liupeng Li, Kuofeng Gao +5
Reasoning in Multimodal Large Language Models (MLLMs) requires both fine-grained visual perception and rigorous logical deduction. Explicit text-based Chain-of-Thought (CoT) is com…
AgentSpec: Understanding Embodied Agent Scaffolds Through Controlled Composition
Jixuan Chen, Jianzhi Shen, Haoqiang Kang +10
LLM agents are increasingly built not as single model calls, but as scaffolded systems that combine reasoning, memory, reflection, action execution, and learning. While such scaffo…
Latent Reasoning with Normalizing Flows
Guancheng Tu, Xiangjun Fu, Suhao Yu +5
Large language models often improve reasoning by generating explicit chain-of-thought (CoT), demonstrating the importance of intermediate computation. However, textual CoT forces t…
CVSearch: Empowering Multimodal LLMs with Cognitive Visual Search for High-Resolution Image Perception
Liupeng Li, Haoqian Kang, Zhenyu Lu +4
High-resolution (HR) image perception presents a key bottleneck for multimodal large language models (MLLMs). While visual search offers a promising solution, existing methods stru…
SegCompass: Exploring Interpretable Alignment with Sparse Autoencoders for Enhanced Reasoning Segmentation
Zhenyu Lu, Liupeng Li, Jinpeng Wang +4
While large language models provide strong compositional reasoning, existing reasoning segmentation pipelines fail to transparently connect this reasoning to visual perception. Cur…
SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning
Haoqiang Kang, Xiaokang Ye, Yuhan Liu +5
LLM/VLM-based digital agents have advanced rapidly thanks to scalable sandboxes for coding, web navigation, and computer use, which provide rich interactive training grounds. In co…
LaDi-RL: Latent Diffusion Reasoning Prevents Entropy Collapse in Reinforcement Learning
Haoqiang Kang, Yizhe Zhang, Nikki Lijing Kuang +2
Reinforcement learning has become a central paradigm for improving LLM reasoning, but most existing methods optimize policies over discrete token sequences. This creates a mismatch…
TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models
Fangxu Yu, Xingang Guo, Lingzhi Yuan +6
Time series are ubiquitous in real-world scenarios and crucial for applications ranging from energy management to traffic control. Consequently, the ability to reason over time ser…