9 papers
Event2Reward: Learning Robotic Rewards from Progress Relations
Junnan Nie, Zitian Fan, Jiachen Zhang +14
Learning task progress from robot demonstrations provides a practical way to construct dense shaping rewards for robotic reinforcement learning. A common approach uses normalized d…
CASE: Cost-Aware Stopping for Efficient Long-Video Agents
Yiming Du, Chenghao Liu, Zhiyuan Liu +4
Long-video agents can actively gather question-relevant evidence, but they typically leave a central decision implicit: when has the agent seen enough to answer? We propose CASE, a…
MemOPD: On-Policy Distillation through Memory State Alignment for Long-Horizon Agents
Zhiyuan Liu, Tinghong Ye, Chenghao Liu +2
Long-horizon agents accumulate growing contexts during interaction, impairing performance and stability. Compact memory mitigates this problem by compressing and rewriting the hist…
PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking
Junnan Nie, Jiayi Li, Jiachen Zhang +5
Action-chunking robot policies predict sequences of future actions, but deployment requires choosing how many actions to execute before replanning. The execution horizon trades off…
What Frozen VLAs Already Know About Success: A Probing Study of Value-Like Structure in Foundation Robot Policies
Jiachen Zhang, Junnan Nie, Junyi Lao +4
Vision--language--action (VLA) policies are trained to imitate actions; their loss never asks them to estimate reward, progress, or future success. Their frozen representations nev…
VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis
Jiachen Zhang, Junyi Lao, Chenghao Liu +5
Finite Element Analysis (FEA) serves as the cornerstone of modern engineering design. However, its workflow is inherently complex and relies heavily on domain expertise. Although r…
MSNav: Zero-Shot Vision-and-Language Navigation with Dynamic Memory and LLM Spatial Reasoning
Chenghao Liu, Zhimu Zhou, Jiachen Zhang +3
Vision-and-Language Navigation (VLN) requires an agent to interpret natural language instructions and navigate complex environments. Current approaches often adopt a "black-box" pa…
TTF-VLA: Temporal Token Fusion via Pixel-Attention Integration for Vision-Language-Action Models
Chenghao Liu, Jiachen Zhang, Chengxuan Li +4
Vision-Language-Action (VLA) models process visual inputs independently at each timestep, discarding valuable temporal information inherent in robotic manipulation tasks. This fram…
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models
Qingkai Min, Qipeng Guo, Xiangkun Hu +3
Cross-document event coreference resolution (CDECR) involves clustering event mentions across multiple documents that refer to the same real-world events. Existing approaches utili…