Cognitive Manifold Time-Slice Budgeting for Persistent AI Reasoning
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Solution Overview
Problem
Current AI systems lack the ability to develop persistent cognitive capabilities, such as learning from experiences, maintaining awareness when not actively responding to prompts, or initiating interactions based on internally generated stimuli, due to their operational paradigm of discrete prompt-response interactions.
Innovation Solution
Implementing a persistent cognitive machine (PCM) with a continuous, differentiable cognitive manifold in geometric space that foliates into time slices, using salience maps, budget functions, and extrinsic curvature tensors to manage cognition evolution, allowing for human-like thought processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If discrete prompt-response interactions are used, then operational simplicity is maintained, but persistent cognitive capabilities cannot be developed
Solution Approach 1:
The system transitions from static discrete prompt-response interactions to dynamic continuous cognitive processing. The cognitive manifold evolves continuously over time, allowing the system to maintain persistent cognitive states, learn from experiences, and adapt its behavior dynamically rather than resetting between interactions.
Solution Approach 2:
The patent introduces a temporal dimension to the cognitive processing by foliating the cognitive manifold into time slices. This transforms the operational paradigm from discrete time steps to continuous time evolution, enabling persistent cognitive capabilities while maintaining mathematical tractability through the ADM formalism.
2Stability of the object's composition
If continuous cognitive manifold is implemented, then cognitive continuity is achieved, but computational complexity increases
Solution Approach 1:
The continuous cognitive manifold is segmented into discrete time slices through foliation. This segmentation allows the system to maintain cognitive continuity within each slice while enabling computational processing by breaking down the continuous problem into manageable discrete steps that can be handled algorithmically.
Solution Approach 2:
The ADM formalism acts as an intermediary mathematical framework that bridges the continuous cognitive manifold and discrete computational processing. It provides the tools to evolve the manifold continuously while maintaining computational tractability through slice-by-slice evolution with budget constraints.
3Use of energy by moving object
If budget constraints are applied to time slices, then cognitive resource management is improved, but reasoning trajectory flexibility is reduced
Solution Approach 1:
The system dynamically adjusts the budget parameter ε_t for each time slice based on the salience map and cognitive state. This allows flexible resource allocation where high-salience regions receive larger budgets enabling more exploration, while low-salience regions use smaller budgets for efficiency, balancing resource management with trajectory flexibility.
Data Source
AI summary
Systems and methods for latent slice budgeting on a persistent cognitive machine (PCM) that uses a continuous, differentiable, cognitive manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with cognitive manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. Methods for latent slice budgeting on the cognitive manifold are disclosed that foliation of the cognitive manifold into time slices and budgeting change between the time slices.