VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making

• URL: http://arxiv.org/abs/2410.15885v3
• Date: Mon, 25 Aug 2025 06:23:29 GMT
• Title: VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making
• Authors: Zuojin Tang, Bin Hu, Chenyang Zhao, De Ma, Gang Pan, Bin Liu,
• Abstract summary: We show that the multi-input single-output (MISO) paradigm limits performance in multi-input multi-output (MIMO) scenarios.<n>In MISO architectures, tasks compete for a shared output channel, creating mutual exclusion effects that cause unbalanced optimization and degraded performance.<n>We introduce a unified training framework that enables concurrent multi-task outputs, exemplified by simultaneous dialogue generation and decision-making.
• Score: 29.23206299246665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent large pretrained models such as LLMs (e.g., GPT series) and VLAs (e.g., OpenVLA) have achieved notable progress on multimodal tasks, yet they are built upon a multi-input single-output (MISO) paradigm. We show that this paradigm fundamentally limits performance in multi-input multi-output (MIMO) scenarios, where parallel task execution is required. In MISO architectures, tasks compete for a shared output channel, creating mutual exclusion effects that cause unbalanced optimization and degraded performance. To address this gap, we introduce MIMO-VLA (VLASCD), a unified training framework that enables concurrent multi-task outputs, exemplified by simultaneous dialogue generation and decision-making. Inspired by human cognition, MIMO-VLA eliminates interference between tasks and supports efficient parallel processing. Experiments on the CARLA autonomous driving platform demonstrate that MIMO-VLA substantially outperforms state-of-the-art MISO-based LLMs, reinforcement learning models, and VLAs in MIMO settings, establishing a new direction for multimodal and multitask learning.

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