3 citations · 12 across the 16 of their papers we have counts for
17 papers
Progress Reward Modeling for Robotic Learning: A Comprehensive Survey
Jianshu Zhang, Keliang Wu, Haoran Lu +8
Robotic learning takes place in dynamic environments with large behavior spaces. A terminal success signal only tells the robot whether the task is completed. It does not explain w…
LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents
Haoyang Fang, Wei Zhu, Boran Han +11
RL post-training strategies are dataset-dependent and reveal a recurring empirical pattern: capacity parameters accumulate monotonically across stages, while regularization paramet…
Attention Sinks and Outliers in Attention Residuals
Haozheng Luo, Haoran Dai, Shaoyang Zhang +10
We propose OASIS, an outlier- and sink-aware method that stabilizes dual-normalized attention-residual architectures through explicit null routing and token-to-depth null coupling.…
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
Yolo Y. Tang, Jing Bi, Pinxin Liu +24
Video understanding represents the most challenging frontier in computer vision, requiring models to reason about complex spatiotemporal relationships, long-term dependencies, and…
MetaFind: Scene-Aware 3D Asset Retrieval for Coherent Metaverse Scene Generation
Zhenyu Pan, Yucheng Lu, Han Liu
We present MetaFind, a scene-aware tri-modal compositional retrieval framework designed to enhance scene generation in the metaverse by retrieving 3D assets from large-scale reposi…
AdvEvo-MARL: Shaping Internalized Safety through Adversarial Co-Evolution in Multi-Agent Reinforcement Learning
Zhenyu Pan, Yiting Zhang, Zhuo Liu +13
LLM-based multi-agent systems excel at planning, tool use, and role coordination, but their openness and interaction complexity also expose them to jailbreak, prompt-injection, and…
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety
Zhenyu Pan, Yiting Zhang, Yutong Zhang +8
Multi-agent systems (MAS) built on multimodal large language models exhibit strong collaboration and performance. However, their growing openness and interaction complexity pose se…
FairReason: Balancing Reasoning and Social Bias in MLLMs
Zhenyu Pan, Yutong Zhang, Jianshu Zhang +6
Multimodal Large Language Models (MLLMs) already achieve state-of-the-art results across a wide range of tasks and modalities. To push their reasoning ability further, recent studi…
SciSciGPT: Advancing Human-AI Collaboration in the Science of Science
Erzhuo Shao, Yifang Wang, Yifan Qian +3
The increasing availability of large-scale datasets has fueled rapid progress across many scientific fields, creating unprecedented opportunities for research and discovery while p…
MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse
Zhenyu Pan, Han Liu
We present MetaSpatial, the first reinforcement learning (RL)-based framework designed to enhance 3D spatial reasoning in vision-language models (VLMs), enabling real-time 3D scene…
Retrieval-Augmented Generation with Hierarchical Knowledge
Haoyu Huang, Yongfeng Huang, Junjie Yang +5
Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks. However, existing RAG…
Do Code LLMs Understand Design Patterns?
Zhenyu Pan, Xuefeng Song, Yunkun Wang +4
Code Large Language Models (LLMs) demonstrate great versatility in adapting to various downstream tasks, including code generation and completion, as well as bug detection and fixi…
Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?
Zhenyu Pan, Rongyu Cao, Yongchang Cao +5
Code completion, a key downstream task in code generation, is one of the most frequent and impactful methods for enhancing developer productivity in software development. As intell…
Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action
Zhenyu Pan, Haozheng Luo, Manling Li +1
We present a Conversational Chain-of-Action (Conv-CoA) framework for Open-domain Conversational Question Answering (OCQA). Compared with literature, Conv-CoA addresses three major…
HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model
Zhenyu Pan, Yoonsung Jeong, Xiaoda Liu +1
We propose a heterogeneous graph mamba network (HGMN) as the first exploration in leveraging the selective state space models (SSSMs) for heterogeneous graph learning. Compared wit…
CoRMF: Criticality-Ordered Recurrent Mean Field Ising Solver
Zhenyu Pan, Ammar Gilani, En-Jui Kuo +1
We propose an RNN-based efficient Ising model solver, the Criticality-ordered Recurrent Mean Field (CoRMF), for forward Ising problems. In its core, a criticality-ordered spin sequ…
Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models
Zhenyu Pan, Haozheng Luo, Manling Li +1
We present a Chain-of-Action (CoA) framework for multimodal and retrieval-augmented Question-Answering (QA). Compared to the literature, CoA overcomes two major challenges of curre…