Anonymous Submission
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.
We establish a taxonomy of six atomic tasks that encapsulate the complete lifecycle of a battle royale match at an intermediate difficulty level (Gold and Silver tiers).
Controlling descent trajectory to land within a minimal radius of a designated waypoint.
Exploring the environment to identify, pick up, and collect essential loot such as weapons and armor.
Detecting adversaries and managing recoil to inflict lethal damage in encounters.
Identifying and reviving knocked-down teammates to restore their combat status.
Searching for, locating, and boarding available vehicles to secure strategic mobility across the battlefield.
Navigating towards the shrinking safe zone while avoiding obstacles under strict time constraints.
