12 citations · 24 across the 20 of their papers we have counts for
22 papers
ExeWorldBench: Benchmarking Code-Based Executable 3D World Modeling
Xinge Peng, Zhen Fang, Ruibing Wang +8
Coding agents are increasingly evolving beyond reproducing what a world looks like toward constructing executable worlds that capture how a world works. This shift calls for execut…
FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis
Kou Shi, Zun Wang, Qisheng Su +10
Training terminal agents requires scalable executable supervision, yet synthesizing high-quality terminal tasks remains challenging. Each task couples an instruction, an initialize…
VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System
Haodong Li, Tianfei Ren, Xiaoxiao Ma +25
Text-to-video models have achieved remarkable visual quality, yet they still struggle to generate physically consistent dynamics because the temporal evolution of a scene must be i…
ACC: Compiling Agent Trajectories for Long-Context Training
Qisheng Su, Zhen Fang, Shiting Huang +8
Recent development of agents has renewed demand for long-context reasoning capacity of LLMs. However, training LLMs for this capacity requires costly long-document curation or heur…
SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering
Qingnan Ren, Shun Zou, Shiting Huang +11
As autonomous coding agents become capable of handling increasingly long-horizon tasks, they have gradually demonstrated the potential to complete end-to-end software development.…
AsyncTool: Evaluating the Asynchronous Function Calling Capability under Multi-Task Scenarios
Kou Shi, Ziao Zhang, Shiting Huang +7
Large language model (LLM)-based agents have shown strong capabilities in using external tools to solve complex tasks. However, existing evaluations often overlook the temporal dim…
SkillFlow:Benchmarking Lifelong Skill Discovery and Evolution for Autonomous Agents
Ziao Zhang, Kou Shi, Shiting Huang +13
As the capability frontier of autonomous agents continues to expand, they are increasingly able to complete specialized tasks through plug-and-play external skills. Yet current ben…
HorizonBench: Long-Horizon Personalization with Evolving Preferences
Shuyue Stella Li, Bhargavi Paranjape, Kerem Oktar +9
User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent life event. We define this prob…
Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models
Shun Zou, Yong Wang, Zehui Chen +4
Diffusion Large Language Models (dLLMs) have recently become a promising alternative to autoregressive large language models (ARMs). Semi-autoregressive (Semi-AR) decoding is widel…
Internalizing Meta-Experience into Memory for Guided Reinforcement Learning in Large Language Models
Shiting Huang, Zecheng Li, Yu Zeng +7
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach for enhancing the reasoning capabilities of Large Language Models (LLMs). Despite its eff…
ADORA: Training Reasoning Models with Dynamic Advantage Estimation on Reinforcement Learning
Qingnan Ren, Shiting Huang, Zhen Fang +4
Reinforcement learning has become a cornerstone technique for developing reasoning models in complex tasks, ranging from mathematical problem-solving to imaginary reasoning. The op…
UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision
Ruiyan Han, Zhen Fang, XinYu Sun +9
While Unified Multimodal Models (UMMs) have achieved remarkable success in cross-modal comprehension, a significant gap persists in their ability to leverage such internal knowledg…
DualVLA: Building a Generalizable Embodied Agent via Partial Decoupling of Reasoning and Action
Zhen Fang, Zhuoyang Liu, Jiaming Liu +7
To build a generalizable Vision-Language-Action (VLA) model with strong reasoning ability, a common strategy is to first train a specialist VLA on robot demonstrations to acquire r…
Agentic Jigsaw Interaction Learning for Enhancing Visual Perception and Reasoning in Vision-Language Models
Yu Zeng, Wenxuan Huang, Shiting Huang +9
Although current large Vision-Language Models (VLMs) have advanced in multimodal understanding and reasoning, their fundamental perceptual and reasoning abilities remain limited. S…
CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios
Shiting Huang, Zhen Fang, Zehui Chen +6
The ability of large language models (LLMs) to utilize external tools has enabled them to tackle an increasingly diverse range of tasks. However, as the tasks become more complex a…
VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning
Qiuchen Wang, Ruixue Ding, Yu Zeng +6
Effectively retrieving, reasoning and understanding visually rich information remains a challenge for RAG methods. Traditional text-based methods cannot handle visual-related infor…
VCR-Bench: A Comprehensive Evaluation Framework for Video Chain-of-Thought Reasoning
Yukun Qi, Yiming Zhao, Yu Zeng +7
The advancement of Chain-of-Thought (CoT) reasoning has significantly enhanced the capabilities of large language models (LLMs) and large vision-language models (LVLMs). However, a…
V2P-Bench: Evaluating Video-Language Understanding with Visual Prompts for Better Human-Model Interaction
Yiming Zhao, Yu Zeng, Yukun Qi +8
Large Vision-Language Models (LVLMs) have made significant strides in the field of video understanding in recent times. Nevertheless, existing video benchmarks predominantly rely o…
ShareGPT4Video: Improving Video Understanding and Generation with Better Captions
Lin Chen, Xilin Wei, Jinsong Li +12
We present the ShareGPT4Video series, aiming to facilitate the video understanding of large video-language models (LVLMs) and the video generation of text-to-video models (T2VMs) v…
Are We on the Right Way for Evaluating Large Vision-Language Models?
Lin Chen, Jinsong Li, Xiaoyi Dong +8
Large vision-language models (LVLMs) have recently achieved rapid progress, sparking numerous studies to evaluate their multi-modal capabilities. However, we dig into current evalu…
Point-DETR3D: Leveraging Imagery Data with Spatial Point Prior for Weakly Semi-supervised 3D Object Detection
Hongzhi Gao, Zheng Chen, Zehui Chen +4
Training high-accuracy 3D detectors necessitates massive labeled 3D annotations with 7 degree-of-freedom, which is laborious and time-consuming. Therefore, the form of point annota…
ShareGPT4V: Improving Large Multi-Modal Models with Better Captions
Lin Chen, Jinsong Li, Xiaoyi Dong +5
In the realm of large multi-modal models (LMMs), efficient modality alignment is crucial yet often constrained by the scarcity of high-quality image-text data. To address this bott…