9 papers
MemWM: Memory-Augmented Text-Based World Model
Yujun Wang, Tao Zhang, Jinhe Bi +9
World models are increasingly used to support planning in agents by predicting how environment states evolve in response to agent actions. Yet fluent next-state predictions can sti…
Perceive-to-Reason: Decoupling Perception and Reasoning for Fine-Grained Visual Reasoning
Hongxing Li, Xiufeng Huang, Dingming Li +11
Fine-grained visual reasoning remains challenging for vision-language models, especially when small but critical visual cues are buried in high-resolution images. Existing approach…
T-Mem: Memory That Anticipates, Not Archives
Weidong Guo, Dakai Wang, Zixuan Wang +2
Long-term memory is essential for conversational agents to remain coherent across extended dialogues, follow through on commitments made many sessions earlier, and adapt their beha…
GroundAct: Can LLM Agents Ground Actions in Environmental States?
Zixuan Wang, Dingming Li, Hongxing Li +8
LLM agents achieve 85-96% success on tasks where instructions fully specify the action, but drop to 29-53% when action feasibility depends on environmental state that the instructi…
Milestone-Guided Policy Learning for Long-Horizon Language Agents
Zixuan Wang, Yuchen Yan, Hongxing Li +7
While long-horizon agentic tasks require language agents to perform dozens of sequential decisions, training such agents with reinforcement learning remains challenging. We identif…
SpatialEvo: Self-Evolving Spatial Intelligence via Deterministic Geometric Environments
Dinging Li, Yingxiu Zhao, Xinrui Cheng +16
Spatial reasoning over three-dimensional scenes is a core capability for embodied intelligence, yet continuous model improvement remains bottlenecked by the cost of geometric annot…