OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation

• URL: http://arxiv.org/abs/2604.18486v1
• Date: Mon, 20 Apr 2026 16:37:22 GMT
• Title: OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
• Abstract summary: Chain-of-Thought (CoT) reasoning is a powerful driver of trajectory prediction in VLA-based autonomous driving.<n>We present OneVL, a unified VLA and World Model framework that routes reasoning through compact latent tokens supervised by dual auxiliary decoders.<n>OneVL becomes the first latent CoT method to surpass explicit CoT, delivering state-of-the-art accuracy at answer-only latency.
• Score: 61.18260993245354
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Chain-of-Thought (CoT) reasoning has become a powerful driver of trajectory prediction in VLA-based autonomous driving, yet its autoregressive nature imposes a latency cost that is prohibitive for real-time deployment. Latent CoT methods attempt to close this gap by compressing reasoning into continuous hidden states, but consistently fall short of their explicit counterparts. We suggest that this is due to purely linguistic latent representations compressing a symbolic abstraction of the world, rather than the causal dynamics that actually govern driving. Thus, we present OneVL (One-step latent reasoning and planning with Vision-Language explanations), a unified VLA and World Model framework that routes reasoning through compact latent tokens supervised by dual auxiliary decoders. Alongside a language decoder that reconstructs text CoT, we introduce a visual world model decoder that predicts future-frame tokens, forcing the latent space to internalize the causal dynamics of road geometry, agent motion, and environmental change. A three-stage training pipeline progressively aligns these latents with trajectory, language, and visual objectives, ensuring stable joint optimization. At inference, the auxiliary decoders are discarded and all latent tokens are prefilled in a single parallel pass, matching the speed of answer-only prediction. Across four benchmarks, OneVL becomes the first latent CoT method to surpass explicit CoT, delivering state-of-the-art accuracy at answer-only latency, and providing direct evidence that tighter compression, when guided in both language and world-model supervision, produces more generalizable representations than verbose token-by-token reasoning. Project Page: https://xiaomi-embodied-intelligence.github.io/OneVL

Related papers

• ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model [53.15040805435013]
  Vision-language models (VLMs) provide strong semantic grounding and general knowledge by reasoning over uniformly sampled frames.<n>We propose a VLM-guided JEPA-style latent world modeling framework that combines dense-frame dynamics modeling with long-horizon semantic guidance.
  arXiv  Detail & Related papers  (2026-03-23T17:59:42Z)
• DualCoT-VLA: Visual-Linguistic Chain of Thought via Parallel Reasoning for Vision-Language-Action Models [50.07453075750711]
  Vision-Language-Action (VLA) models map visual observations and language instructions directly to robotic actions.<n>Recent efforts have incorporated Chain-of-Thought (CoT) reasoning to endow VLA models with a thinking before acting'' capability.<n>We propose DualCoT-VLA, a visual-linguistic CoT method for VLA models with a parallel reasoning mechanism.
  arXiv  Detail & Related papers  (2026-03-23T17:59:25Z)
• LaST-VLA: Thinking in Latent Spatio-Temporal Space for Vision-Language-Action in Autonomous Driving [21.38662345656532]
  Vision-Language-Action (VLA) models have revolutionized autonomous driving by unifying perception and planning.<n>Their reliance on explicit textual Chain-of-Thought (CoT) leads to semantic-perceptual decoupling and perceptual-symbolic conflicts.<n>Recent shifts toward latent reasoning attempt to bypass these bottlenecks by thinking in continuous hidden space.
  arXiv  Detail & Related papers  (2026-03-02T14:42:36Z)
• Efficient and Explainable End-to-End Autonomous Driving via Masked Vision-Language-Action Diffusion [23.834662472392694]
  Masked Vision-Language-Action Diffusion for Autonomous Driving (MVLAD-AD) is a novel framework designed to bridge the gap between efficient planning and semantic explainability.<n>We introduce a discrete action tokenization strategy that constructs a compact codebook of kinematically feasible waypoints from real-world driving distributions.<n>Experiments on nuScenes and derived benchmarks demonstrate that MVLAD-AD achieves superior efficiency and outperforms state-of-the-art autoregressive and diffusion baselines in planning precision.
  arXiv  Detail & Related papers  (2026-02-24T05:59:10Z)
• Accelerating Structured Chain-of-Thought in Autonomous Vehicles [55.78214218065611]
  Chain-of-Thought (CoT) reasoning enhances the decision-making capabilities of vision-language-action models in autonomous driving.<n>FastDriveCoT is a novel parallel decoding method that accelerates template-structured CoT.
  arXiv  Detail & Related papers  (2026-02-02T22:14:26Z)
• Generative Scenario Rollouts for End-to-End Autonomous Driving [58.99809446189301]
  Vision-Language-Action (VLA) models are emerging as highly effective planning models for end-to-end autonomous driving systems.<n>We propose Generative Scenario Rollouts (GeRo), a plug-and-play framework for VLA models that jointly performs planning and generation of language-grounded future traffic scenes.
  arXiv  Detail & Related papers  (2026-01-16T17:59:28Z)
• LaST$_{0}$: Latent Spatio-Temporal Chain-of-Thought for Robotic Vision-Language-Action Model [102.60980325911106]
  We propose a framework that enables efficient reasoning before acting through a Latent Spatio-Temporal Chain-of-Thought (CoT)<n>Specifically, we introduce a token-efficient latent CoT space that models future visual dynamics, 3D structural information, and robot proprioceptive states, and further extends these representations across time to enable temporally consistent implicit reasoning trajectories.
  arXiv  Detail & Related papers  (2026-01-08T18:59:53Z)
• ColaVLA: Leveraging Cognitive Latent Reasoning for Hierarchical Parallel Trajectory Planning in Autonomous Driving [44.008287454538596]
  Vision-language models (VLMs) enrich this paradigm by introducing cross-modal priors and commonsense reasoning.<n>Current VLM-based planners face three key challenges: (i) a mismatch between discrete text reasoning and continuous control, (ii) high latency from autoregressive chain-of-thought decoding, and (iii) inefficient or non-causal planners that limit real-time deployment.<n>We propose ColaVLA, a unified vision-language-action framework that transfers reasoning from text to a unified latent space and couples it with a hierarchical, parallel trajectory decoder.
  arXiv  Detail & Related papers  (2025-12-28T14:06:37Z)
• Latent Chain-of-Thought World Modeling for End-to-End Driving [45.726304769312414]
  We present Latent-CoT-Drive (LCDrive), a model that expresses CoT in a latent language.<n>Our approach unifies CoT reasoning and decision making by representing both in an action-aligned latent space.<n>On a large-scale end-to-end driving benchmark, LCDrive achieves faster inference, better trajectory quality, and larger improvements from interactive reinforcement learning.
  arXiv  Detail & Related papers  (2025-12-11T02:22:07Z)
• CoT4AD: A Vision-Language-Action Model with Explicit Chain-of-Thought Reasoning for Autonomous Driving [10.836513600206118]
  We propose Chain-of-Thought (CoT) reasoning for autonomous driving to enhance both numerical and causal reasoning in Vision-Language Models (VLMs)<n>CoT4AD integrates visual observations and language instructions to perform semantic reasoning, scene understanding, and trajectory planning.<n>Experiments on both real-world and simulated benchmarks, including nuScenes and Bench2Drive, demonstrate that CoT4AD achieves state-of-the-art performance in both open-loop and closed-loop evaluations.
  arXiv  Detail & Related papers  (2025-11-27T15:13:13Z)
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving [49.07731497951963]
  ReCogDrive is a novel Reinforced Cognitive framework for end-to-end autonomous driving.<n>We introduce a hierarchical data pipeline that mimics the sequential cognitive process of human drivers.<n>We then address the language-action mismatch by injecting the VLM's learned driving priors into a diffusion planner.
  arXiv  Detail & Related papers  (2025-06-09T03:14:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.