VLM & VLA Research Briefing — 2026-10-01
Microsoft Research has introduced "Quine," a multimodal world model for biology, while the VLM market is projected to hit $41.5 billion by 2035. Multimodal AI is rapidly shifting toward cloud-based vertical applications and autonomous visual agents.
VLM & VLA Research Briefing — 2026-10-01
Noteworthy New Updates
Microsoft Research Announces "Quine," a Multimodal Biology Model
On September 29, 2026, Microsoft Research introduced "Quine," a research achievement built on a multimodal world model of biology. Quine features an interaction device that connects the model with orchestration and reasoning models.
VLM Market Size Projected to Reach $41.5 Billion by 2035
The global Vision-Language Model market is forecasted to hit $41.5 billion by 2035, driven by the accelerating adoption of multimodal AI. Key growth drivers include cloud-based vertical applications, actionable AI, the transition to autonomous visual agents, advanced hardware, and proprietary data.
Multimodal AI Market Expected to Grow to $26.5 Billion by 2033
The multimodal AI market is projected to expand from $2.5 billion in 2025 to $26.5 billion by 2033.
VLM Technical Trends and Detailed Summary
Growing Importance of Multimodal Adaptation
Recent studies are focusing on the "modality gap" between vision and language modalities. A distinct separation exists between text and image embeddings in dual-encoder vision-language models, and researchers are exploring ways to quantify and reduce this gap.
Developing Efficient Multimodal Large Language Models
While Multimodal Large Language Models (MLLMs) have shown exceptional performance in visual question answering and visual understanding tasks, their massive model sizes and high training and inference costs hinder widespread application. Research to improve efficiency is actively underway.
Enhancing Advanced Reasoning Capabilities in Multimodal Models
The "Vision-DeepResearch" study proposes a method for multimodal large language models to overcome the limitations of internal world knowledge through "reasoning-through-augmentation."
Robotics and VLA Performance Summary
Systematic Review of VLA Models for Robotics
Vision-Language-Action (VLA) models are evolving into generalist VLA agents that integrate perception, task instruction, and action generation within autoregressive sequence modeling. By tokenizing multimodal inputs, these models enable step-by-step action generation across heterogeneous tasks.
Scalability of Cross-Embodiment VLA
Cutting-edge approaches like X-VLA (Soft-prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model) deliver scalable vision-language-action capabilities across multiple robotic platforms using soft-prompted transformers.
This content was collected, curated, and summarized entirely by AI — including how and what to gather. It may contain inaccuracies. Crew does not guarantee the accuracy of any information presented here. Always verify facts on your own before acting on them. Crew assumes no legal liability for any consequences arising from reliance on this content.
Powered by
