Abstract
Contemporary recommendation architectures optimize short-horizon behavioral objectives such as click-through rate, retention, conversion probability, watch-time maximization, and engagement persistence. While these systems achieve high efficiency in immediate interaction optimization, they systematically fail to model longitudinal human development, identity continuity, aspiration stabilization, and temporally coherent cognitive evolution. Existing recommender systems operate under an implicit assumption that preferences are stationary or slowly drifting latent variables inferable from interaction sequences. This assumption becomes increasingly invalid in high-dimensional socio-cognitive environments where users are not static consumers but dynamically evolving agents whose identities emerge through recursive interactions between cognition, social exposure, emotional state transitions, epistemic uncertainty, and environmental reinforcement.
This paper introduces Intent Gravity Engines (IGE), a novel computational architecture for longitudinal identity-field modeling and future-self trajectory orchestration. Rather than optimizing immediate utility functions, IGE constructs temporally continuous latent manifolds representing probabilistic future identity attractors. The proposed framework models users as evolving dynamical systems embedded within high-dimensional intention fields influenced by curiosity gradients, ambition vectors, cognitive fatigue tensors, emotional drift processes, uncertainty pressure fields, and social resonance forces. Recommendations are reformulated as controlled perturbations applied to evolving identity trajectories rather than discrete content-ranking decisions.
The architecture integrates active inference, temporal latent self-modeling, hierarchical memory-field transformers, multi-timescale diffusion dynamics, graph-based aspiration topology learning, and counterfactual trajectory simulation. We introduce a Temporal Identity Field Transformer (TIFT), a Memory Persistence Lattice (MPL), and a Future Identity Projection Module (FIPM) trained jointly using a multi-objective optimization framework combining predictive consistency loss, identity coherence regularization, aspiration entropy minimization, and long-horizon flourishing reward estimation.
Experiments conducted across synthetic longitudinal behavioral corpora and large-scale multimodal interaction datasets demonstrate that IGE significantly outperforms conventional recommendation systems in future-state stability prediction, longitudinal aspiration persistence, educational continuity forecasting, and psychological coherence estimation. Compared against transformer-based sequential recommenders and reinforcement learning recommender agents, IGE achieves a 38.7% improvement in long-horizon trajectory prediction accuracy, a 52.4% reduction in identity fragmentation metrics, and a 31.2% increase in aspiration continuity retention over 24-month simulated deployment intervals.
We further discuss scalability constraints, online adaptation mechanisms, memory pruning strategies, distributed inference architectures, ethical governance considerations, and societal implications associated with large-scale deployment of trajectory orchestration systems. The paper argues that recommendation systems are transitioning from attention optimization infrastructures toward civilization-scale identity navigation architectures, fundamentally redefining the computational role of digital platforms in shaping human becoming.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Manuel-Devadoss, Johny; Johnson, Joanna; and Johnson, Jayden Jack, "Intent Gravity Engines", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10166