Gasgoo Munich-SynapX has secured $50 million in a fresh funding round, bringing its total raised over the past three months to nearly 1 billion yuan, according to Gasgoo.
The round was led by Jinqiu Fund, Xincun Capital, and Huangpujiang Capital, with participation from JAFCO Asia, Houxue Capital, HKTT, and Panga Capital. Existing backer Linear Capital also doubled down. Meanwhile, a follow-up round worth 500 million yuan is already nearing completion.
With the new capital, SynapX plans to accelerate development of its world foundation model and "hand-brain integrated" platform. The company aims to launch the model and its core products in the second half of 2026.
Founded in early 2026, SynapX is focused on building sustainable, self-evolving embodied intelligence. Founder Du Dalong previously established Gaussian Robotics and was a founding member of Baidu's IDL and the sixth employee at Horizon Robotics. The founding team—including Liang Zhujin, Pan Yangjiayi, and Fan Qingyuan—also brings deep experience from Horizon Robotics and Gaussian Robotics.
The broader team is largely drawn from top-tier tech firms like ByteDance Seed, Baidu, Xiaomi, Huawei, NIO, XPENG, Li Auto, and Meta. Headcount is rapidly approaching 100.
SynapX has established a closed-loop system spanning models, data, and physical execution. Its core is a world foundation model, supported by a strategy model, the SYNData vision-force-tactile multimodal data system, and a dexterous hardware platform.
Image Source: SynapX
SynapX has outlined a three-stage evolution roadmap for world models, based on the depth and breadth of their generative capabilities:
WM1 - Generation/Interaction: The model possesses weak physics modeling capabilities, sufficient for content generation and gaming interaction in the digital world.
WM2 - Physics/Action: The model requires strong macroscopic physics modeling to predict how actions alter states and outcomes. It emphasizes physical rationality, interactivity, and controllability, enabling embodied intelligence to operate within real-world environments.
WM3 - Universal Optimization: The world model advances from macroscopic physics to microscopic physics and complex high-dimensional systems, serving as a foundational method for humans to understand laws, design interventions, and optimize the real world.
SynapX is currently focused on the WM2 stage: transitioning from weak to strong physics modeling. The goal is to move beyond predicting the next frame to forecasting physical world states and actions, thereby achieving more generalized physical causal modeling.
Image Source: SynapX
Furthermore, SynapX has defined a five-level progression for physical AI productivity: L1 rule-driven systems, L2 single-task generalization, L3 cross-task generalization in restricted scenarios, L4 cross-task generalization in open scenarios, and L5 superhuman physical intelligence.
Building on this framework, SynapX introduced the SYNTH Deep Thought Architecture. This connects real-world multimodal operation data, physics-based world models, and strategic execution, endowing the embodied "brain" with robust, generalized physics modeling and action capabilities.
Specifically, SYNData captures real operation data, converting human processes into learnable assets for robots. SYNWorld learns physical causality to predict how actions affect world states and task outcomes. Finally, SYNAction translates these predictions into executable strategies, effectively integrating model capabilities into robotic operations.
SynapX's physics-based world model and "hand-brain integrated" platform, built around the SYNTH architecture, have already garnered significant interest and recognition from clients and industrial partners both at home and abroad.
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