collaborators

7 papers

cs.RO2026

-VLA: a Hierarchical Robot Foundation Model with World-Model-Guided Test-Time Computation

Xiaowei Cai, Yunuo Cai, Bingao Chen +36

Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks. Most hierarchical vision-language-acti…

cs.RO2026

SpikeVLA: Vision-Language-Action Models with Spiking Neural Networks

Ruiqi Song, Dujun Nie, Siyu Teng +7

Vision-Language-Action (VLA) models have become a dominant paradigm for embodied intelligence. However, most existing approaches are built on large-scale transformers, resulting in…

cs.CL2026

Unveiling the Entropy Dynamics of Chain-of-Thought Reasoning

Ting Xu, Xu He, Yupu Lu +4

This paper investigates the entropy dynamics of Chain-of-Thought (CoT) and uncovers a consistent two-phase structure: an Uncertainty Region of exploration transitioning sharply to…

cs.AI2026

PhGPO: Pheromone-Guided Policy Optimization for Long-Horizon Tool Planning

Yu Li, Guangfeng Cai, Shengtian Yang +5

Recent advancements in Large Language Model (LLM) agents have demonstrated strong capabilities in executing complex tasks through tool use. However, long-horizon multi-step tool pl…

cs.CV2026

JEPA-VLA: Video Predictive Embedding is Needed for VLA Models

Shangchen Miao, Ningya Feng, Jialong Wu +4

Recent vision-language-action (VLA) models built upon pretrained vision-language models (VLMs) have achieved significant improvements in robotic manipulation. However, current VLAs…

cs.CL2026

ASTER: Agentic Scaling with Tool-integrated Extended Reasoning

Xuqin Zhang, Quan He, Zhenrui Zheng +3

Reinforcement learning (RL) has emerged as a dominant paradigm for eliciting long-horizon reasoning in Large Language Models (LLMs). However, scaling Tool-Integrated Reasoning (TIR…

stat.ML2024

Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection

Yewen Li, Chaojie Wang, Xiaobo Xia +6

Unsupervised out-of-distribution (U-OOD) detection is to identify OOD data samples with a detector trained solely on unlabeled in-distribution (ID) data. The likelihood function es…