collaborators

9 papers

cs.RO2026

Mind-VLA: Instruction-Aware Spatial Representation Alignment for Vision-Language-Action Models

Xingyu Ding, Yuzhong Zhao, Yang Wu +4

Recent Vision-Language-Action (VLA) methods improve generalization by aligning their representations with 3D scene geometry. However, these methods are fundamentally instruction-ag…

cs.CL2026

ReST-KV: Robust KV Cache Eviction with Layer-wise Output Reconstruction and Spatial-Temporal Smoothing

Yongqi An, Chang Lu, Kuan Zhu +5

Large language models (LLMs) face growing challenges in efficient generative inference due to the increasing memory demands of Key-Value (KV) caches, especially for long sequences.…

cs.CV2026

PLUME: Latent Reasoning Based Universal Multimodal Embedding

Chenwei He, Xiangzhao Hao, Tianyu Yang +6

Universal multimodal embedding (UME) maps heterogeneous inputs into a shared retrieval space with a single model. Recent approaches improve UME by generating explicit chain-of-thou…

cs.CV2026

Listening with the Eyes: Benchmarking Egocentric Co-Speech Grounding across Space and Time

Weijie Zhou, Xuantang Xiong, Zhenlin Hu +6

In situated collaboration, speakers often use intentionally underspecified deictic commands (e.g., ``pass me \textit{that}''), whose referent becomes identifiable only by aligning…

cs.AI2025

ESearch-R1: Learning Cost-Aware MLLM Agents for Interactive Embodied Search via Reinforcement Learning

Weijie Zhou, Xuangtang Xiong, Ye Tian +8

Multimodal Large Language Models (MLLMs) have empowered embodied agents with remarkable capabilities in planning and reasoning. However, when facing ambiguous natural language inst…

cs.CV2025

PhysVLM-AVR: Active Visual Reasoning for Multimodal Large Language Models in Physical Environments

Weijie Zhou, Xuantang Xiong, Yi Peng +5

Visual reasoning in multimodal large language models (MLLMs) has primarily been studied in static, fully observable settings, limiting their effectiveness in real-world environment…