collaborators

5 papers

cs.LG2026

An AI4AI Framework for Visual Token Pruning

Zhen Liu, Wenli Huang, Wei Song +3

Visual-token pruning can substantially reduce the inference cost of multimodal large language models (MLLMs), yet existing methods largely rely on fixed, handcrafted heuristics and…

cs.CV2026

The Essence of Balance for Self-Improving Agents in Vision-and-Language Navigation

Zhen Liu, Yuhan Liu, Jinjun Wang +3

In vision-and-language navigation (VLN), self-improvement from policy-induced experience, using only standard VLN action supervision, critically depends on balancing behavioral div…

cs.CV2026

Instruction-as-State: Environment-Guided and State-Conditioned Semantic Understanding for Embodied Navigation

Zhen Liu, Yuhan Liu, Jinjun Wang +3

Vision-and-Language Navigation requires agents to follow natural-language instructions in visually changing environments. A central challenge is the dynamic entanglement between la…

cs.RO2026

Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection

Zhen Liu, Xinyu Ning, Zhe Hu +8

Recent vision-language-action (VLA) systems have demonstrated strong capabilities in embodied manipulation. However, most existing VLA policies rely on limited observation windows…

cs.RO2026

HSC-VLA: Hierarchical Scene-Clearing for Robust Bimanual Manipulation in Dense Clutter

Zhen Liu, Xinyu Ning, Zhe Hu +3

Modern Vision--Language--Action models often suffer from critical instruction-following failures in high-density manipulation environments, where task-irrelevant visual clutter dil…