We present Orbit Wars Phase 4, an open-source Kaggle agent unifying Nature Foundation Models (state–action–dynamics world modeling), Atlas-GS (2D Gaussian spatial value splat for target prioritization), and ASRA (hypothesis-driven simulation with five strategic theories tested per turn). The agent operates under a one-second decision budget in a continuous 2D RTS with orbiting planets, comets, and multi-agent free-for-all play.
We present Atlas-GS v1, the first end-to-end implementation of a 3D Gaussian world-modeling pipeline within the Nature Foundation Models (NFM) hierarchy. Atlas-GS ingests RGB-D observations, constructs a persistent Gaussian field, localizes against that map, persists world state across sessions, and logs state–action–state transitions. The system implements Phases 0–6 as a modular Python package with CLI tools and demo video generation—without requiring GPU hardware for v1 validation. We report empirical results on TUM RGB-D (4,018 Gaussians, 0.0102 m localization RMSE) and synthetic sequences.
We propose Nature Foundation Models (NFM), a research program for systems that learn representations, dynamics, causal structure, and mechanisms directly from interaction with the natural world. NFM organizes scientific intelligence as a hierarchy—NFM-Worlds, NFM-Robotics, Atlas, and Atlas-GS—with a shared state–action–dynamics abstraction and a seven-stage developmental pipeline from world representation to adaptive scientific reasoning. The central thesis is that scientific reasoning should emerge from increasingly sophisticated interactions with learned world models rather than from an independent symbolic module.