Project RIFT: A Self-Evolving Memory-Driven Architecture for Autonomous Intelligence

Project RIFT investigates the emergence of anticipatory intelligence in environments governed by irreversibility, delayed consequence, and metabolic constraint, where conventional prediction-centric artificial intelligence systems systematically fail. Across more than fifty controlled simulations, agents are evaluated under progressively constrained physical conditions including ballistic impact, continuous mortality, energetic depletion, perceptual ambiguity, discontinuous motor control, physical memory formation, morphological commitment, ecological selection, and formal falsification testing. Predictive architectures—Transformers, recurrent neural networks, and reinforcement learning agents—consistently exhibit hedging behavior, delaying irreversible action in order to minimize uncertainty. In irreversible environments, this delay is catastrophic and leads to systematic survival failure. In contrast, simple geometric and morphological agents succeed without prediction, symbolic memory, or reward optimization. These agents survive by collapsing uncertainty through early irreversible physical commitment, demonstrating that anticipation can emerge from structural and thermodynamic constraints rather than probabilistic forecasting. A final minimal validation environment (RIFT-A) confirms a sharp dissociation: geometric commitment agents achieve non-trivial survival while predictive agents fail despite identical information access and training exposure. This result suggests a fundamental limitation of prediction-driven intelligence under irreversible conditions and motivates an alternative foundation for artificial intelligence grounded in physics, embodiment, and constraint-driven necessity. PASS 1 establishes that: Anticipation need not depend on prediction or reward maximization Memory can arise as persistent physical state rather than symbolic storage Irreversible commitment can outperform probabilistic decision-making Intelligence may be fundamentally structural rather than representational These findings position Project RIFT within the broader traditions of embodied cognition, morphological computation, and physically grounded intelligence, while proposing a falsifiable experimental framework for future investigation of non-predictive agency.