MAIN-VLA: Modeling Abstraction of Intention and eNvironment for Vision-Language-Action Models
This addresses a bottleneck for AI systems operating in real-time, unpredictable interactive environments, representing a novel method rather than an incremental improvement.
The paper tackles the inefficiency of existing Vision-Language-Action models in extracting action-critical signals from redundant sensor streams in complex, dynamic environments like 3D open worlds and PvP games, resulting in a new state-of-the-art framework that achieves superior decision quality, stronger generalization, and cutting-edge inference efficiency.
Despite significant progress in Visual-Language-Action (VLA), in highly complex and dynamic environments that involve real-time unpredictable interactions (such as 3D open worlds and large-scale PvP games), existing approaches remain inefficient at extracting action-critical signals from redundant sensor streams. To tackle this, we introduce MAIN-VLA, a framework that explicitly Models the Abstraction of Intention and eNvironment to ground decision-making in deep semantic alignment rather than superficial pattern matching. Specifically, our Intention Abstraction (IA) extracts verbose linguistic instructions and their associated reasoning into compact, explicit semantic primitives, while the Environment Semantics Abstraction (ESA) projects overwhelming visual streams into a structured, topological affordance representation. Furthermore, aligning these two abstract modalities induces an emergent attention-concentration effect, enabling a parameter-free token-pruning strategy that filters out perceptual redundancy without degrading performance. Extensive experiments in open-world Minecraft and large-scale PvP environments (Game for Peace and Valorant) demonstrate that MAIN-VLA sets a new state-of-the-art, which achieves superior decision quality, stronger generalization, and cutting-edge inference efficiency.