Wenhai Wang

Visual Embodied Brain-1.5: Enhanced Perception, Spatial Reasoning and Robot Control in Spaces.

The remarkable progress of Multimodal Large Language Models (MLLMs) has attracted increasing attention to extending them to physical entities like legged robots and robotic arms. This typically requires MLLMs to not only grasp multimodal understanding abilities, but also integrate visual-spatial reasoning and physical interaction capabilities. Nevertheless, existing methods struggle to jointly accommodate these capabilities within a shared multimodal model, due to the substantial differences between vision-language reasoning and low-level physical control. In this paper, we present Visual Embodied Brain-1.5 (VeBrain-1.5), a task-level unified framework that connects multimodal perception and spatial reasoning with robot control through a shared MLLM-compatible decision interface. VeBrain-1.5 reformulates robotic control into common text-based MLLM tasks in the 2D visual space, thereby aligning the supervision format and prediction interface of multimodal understanding, spatial reasoning, and robot control. A modular robotic adapter subsequently translates these high-level textual decisions into embodiment-specific motion commands and executes them in a closed loop. From the data perspective, we further introduce VeBrain-1M, a high-quality instruction dataset encompassing various capabilities of VeBrain-1.5. In VeBrain-1M, we take hundreds of hours to collect, curate and annotate the data, and adopt multimodal chain-of-thought (CoT) to mix the different capabilities into a single conversation. Extensive experiments on 13 multimodal benchmarks, 7 spatial intelligence benchmarks and 4 simulation and real-world robotic benchmarks demonstrate the superior performance of VeBrain-1.5 over existing MLLMs like Qwen2.5-VL. To further eliminate the task ambiguity and enhance the reasoning capability, we adopt an offline RL policy learning with verifiable reward to increase the task success rate. When deployed to two robotic entities including legged robots and robotic arms, VeBrain-1.5 shows strong adaptability, flexibility, and compositional capabilities compared to existing methods.

AutoJudger : An Adaptive Evaluation Framework for Efficient Benchmarking of MLLMs

ParVL: Parallel Scaling and Expandable Compute Allocation for Multimodal LLMs

Existing scaling strategies for Multimodal Large Language Models (MLLMs) typically expand either model parameters or sequential inference computation, incurring substantial memory or latency overhead. More importantly, most existing methods fail to alter the rigid, fixed computation allocation between the Vision Transformer and the Large Language Model components, limiting task-specific optimization. To address this, we introduce the Parallel Vision-Language (ParVL) scaling framework for MLLMs, which scales parallel computation by reusing the existing ViT and LLM backbone parameters across multiple vision and language branches. This framework raises a central question: given a fixed backbone parameter budget, how should additional shared-backbone computation be allocated between the vision and language modalities? We instantiate each parallel computational stream with branch-specific prefix parameters over a shared backbone, and train the entire model end-to-end via full-parameter supervised fine-tuning on roughly 13B tokens. We systematically study the computation-allocation trade-off between the ViT encoder and LLM decoder. ParVL improves overall multimodal performance over same-recipe single-branch baselines, and the best evaluated vision--language allocation varies across tasks. Code is available at https://github.com/YangYangGirl/ParVL.