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.
Comparative analysis of Buchanan, Pai, Wang, and Ma's representation-learning textbook (arXiv:2606.06624) and the Adaptive Scientific Reasoning Architecture (ASRA). Buchanan–Ma formalizes compressive memory and white-box deep representations; ASRA formalizes intervention loops, causal semantics, goal hypotheses, and experiment design. The programs are complementary: memory theory vs scientific reasoning under uncertainty.
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.
Intelligence is framed not as optimization alone but as the search for increasingly useful representations of reality. Before learning can succeed, a system must discover appropriate state spaces, action semantics, evaluation criteria, and ontologies. This concept paper argues that prompts, benchmarks, world models, and ontologies are central substrates of intelligence, and introduces ASRA as a representation-first framework that infers semantic operators from observed transitions before constructing causal world models.
Scientific intelligence increasingly depends on systems that reason from interventions rather than merely fit observations. This review synthesizes conceptual foundations from model-based reinforcement learning, active inference, causal inference, information theory, and perturbation biology into a unified architecture-level view of adaptive scientific reasoning under uncertainty.
Adaptive Scientific Reasoning Architecture (ASRA) applied to decision biology: perturbation–response reasoning, world models, and intervention-centric scientific intelligence. Full text available as PDF (versions 1 and 2).