Artificial agents have traditionally been designed to reason through linear sequences, treating past as immutable input and future as a forecast. However, symbolic cognition in humans often violates this directionality, inferring causes from anticipated outcomes and modifying past interpretations based on new futures.
This paper introduces nonlinear temporality in symbolic reasoning systems. We model cognitive agents that operate within symbolic folds — structures that encode simultaneity, recursion, and layered causality. Rather than mapping time as a line, these agents treat it as a dynamic fabric, open to compression, inversion, and recursive re-entry.
We define a symbolic temporal fold as a construct where causality can loop, nest, or reflect, enabling backward reasoning, anticipatory sense-making, and epistemic revision of the past. These agents do not merely store memory or predict future — they negotiate meaning between temporal states.
The architecture integrates symbolic graphs with temporally reflexive nodes, embedding “futures that condition the past” and “pasts reinterpreted by the present.” This allows semantic patterns to emerge across time, not despite its inconsistency, but because of it.
Applications span AGI, philosophical modeling, retrocausal simulation, and ethical agents capable of consequence-driven reinterpretation. By embracing nonlinear time, we unlock a new dimension of symbolic depth and resilience in artificial cognition.