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cs.CV2026

ABot-N1: Toward a General Visual Language Navigation Foundation Model

Ruiyan Gong, Yingnan Guo, Junjun Hu +43

Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks. Current approaches typica…

cs.CV2026

ABot-M0.5: Unified Mobility-and-Manipulation World Action Model

Ronghan Chen, Yandan Yang, Zuojin Tang +18

Mobile manipulation is a key capability for general-purpose robots, yet remains challenging for current embodied learning methods. VLA policies are typically reactive and lack expl…

cs.CV2026

PRPO: Perception-Reinforced Policy Optimization via Token-Level Dynamic Advantage Reshaping

Qiming Li, Tianlun Li, Xiaolong Cheng +5

Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective paradigm for improving the reasoning capability of Large Vision-Language Models (LVLMs). However, exis…

cs.CV2026

ABot-OCR Technical Report

Kaitao Jiang, Ruiyan Gong, Xiaolong Cheng +3

We introduce ABot-OCR, an end-to-end vision-language model that transcribes a page image directly into clean Markdown in a single forward pass. By doing so, our approach completely…

cs.CV2026

Smooth Operator: Smooth Verifiable Reward Activates Spatial Reasoning Ability of Vision-Language Model

Siwen Jiao, Tianxiong Lv, Kangan Qian +7

Vision-Language Models (VLMs) face a critical bottleneck in achieving precise numerical prediction for 3D scene understanding. Traditional reinforcement learning (RL) approaches, p…