LM-X: Explainable Vision--Language--Action Modeling via Progress, Event, and Uncertainty Prediction

Jin Lou; Zhiyuan Jing; Xupeng Wang; Andong Chen; Xingdong Zhu; Yuexuan Li; Yuan Xu; Zhijie Zhu; Yingwei Ji; Wenpeng Nie; Renxing Feng; Liangliang Chen; Ying Chu; Jingyi Li; Jinyan Liu; Zhiqi Song; Jingxuan Zhu; Jidong Zhang; Yufei Liu; Boyang Xing; Lei Jiang; Yan Cui; Hongming Li; Yuchen Zhu

arXiv · 2608.25757

LM-X: Explainable Vision--Language--Action Modeling via Progress, Event, and Uncertainty Prediction

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Abstract

Large-scale vision--language--action (VLA) policies have advanced generalist robot control, yet most remain stimulus-to-action black boxes: actions are exposed, but their explanatory state is not. They provide no native account of three explanatory signals: task progress, the next semantic transition, or local command reliability. Prior work shows that progress and event structure aid long-horizon control and that uncertainty supports monitoring; however, such capabilities are typically added or extracted only after action pretraining. The field therefore lacks a VLA foundation model whose explanatory state is jointly pretrained with control. Drawing on biological sensorimotor organization, in which outcome-sensitive, event-segmented, and probabilistic predictions structure behavior, we introduce LM-X. LM-X learns three directly supervised online signals: return-to-go (RTG) estimates visible progress and state quality; event-to-go (ETG) predicts the action sequence to the next semantic event; and heteroscedastic action-flow variance reports local command reliability. RTG conditions ETG and both condition action generation; uncertainty is estimated inside the action expert, making explanation part of control rather than a post-hoc description. We pretrain LM-X on more than 20,000 hours of heterogeneous real-robot trajectories, including over 1,000 hours of failed rollouts. A controlled gate favors joint over post-hoc training. LM-X achieves 74.1\% success on 50 randomized-hard RoboTwin2.0 tasks and 73.5\% on seven real-robot tasks, compared with 55.4\% and 50.7\% for GR00T N1.7. Its signals track progress and regression, anticipate event-scale motion, detect high-error actions, and provide advance failure warning. These results establish LM-X as an explainable VLA foundation model that couples transparent predictive state with stronger generalist control.

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Jin Lou, Zhiyuan Jing, Xupeng Wang, Andong Chen, Xingdong Zhu, Yuexuan Li, Yuan Xu, Zhijie Zhu, Yingwei Ji, Wenpeng Nie, Renxing Feng, Liangliang Chen, Ying Chu, Jingyi Li, Jinyan Liu, Zhiqi Song, Jingxuan Zhu, Jidong Zhang, Yufei Liu, Boyang Xing, Lei Jiang, Yan Cui, Hongming Li, Yuchen Zhu. 2026-09-09. LM-X: Explainable Vision--Language--Action Modeling via Progress, Event, and Uncertainty Prediction. https://arxiv.org/abs/2608.25757

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