Abstract
Contemporary transformer architectures achieve strong performance through large-scale parameterization, retrieval augmentation, and extended context windows. However, persistent token-level retention scales poorly for lifelong or long-horizon cognition due to increasing computational cost, retrieval interference, context fragmentation, and memory saturation. This paper proposes a speculative but computationally grounded framework termed Dream-State Consolidation Transformers (DSCT), in which artificial systems alternate between online inference phases and offline consolidation phases. During online operation, systems process high-resolution episodic information from external environments. During offline consolidation, episodic traces are replayed under stochastic perturbation and compressed into semantically stable latent representations.
Unlike conventional persistent-memory systems, DSCT prioritizes abstraction stability rather than reconstruction fidelity. The framework introduces mechanisms for perturbative replay, latent attractor consolidation, entropy-regulated forgetting, and counterfactual reconstruction. These processes are hypothesized to improve long-horizon reasoning, continual adaptation, semantic compression efficiency, and robustness under bounded memory constraints. The paper formalizes the proposed architecture, relates it to existing work in continual learning and biological memory consolidation, identifies potential failure modes, and proposes experimentally testable research directions.
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, "Dream-State Consolidation Transformers for Long-Horizon Artificial Intelligence", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10118