Ranked-Occurrence Temporology for Uncertainty-Aware LLM Decision Systems

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Timeline Paradigm

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A Symmetry/Asymmetry_Framework_for_Temporal_Coherence

Ranked-Occurrence Temporology for Uncertainty-Aware LLM Decision Systems: A Symmetry/Asymmetry Framework for Temporal Coherence

Abstract

Large language model (LLM) systems increasingly require memory architectures that preserve contextual coherence across discontinuous, ambiguous, and partially contradictory interaction histories. Conventional approaches typically organize dialogue memory by clock time, vector similarity, or manually imposed hierarchy. This paper introduces ranked-occurrence temporology, a formal architecture in which temporal order is represented not by absolute timestamps but by ranked occurrences within semantically partitioned event streams. The method is developed through the XenoEngineer Timeline Paradigm, where each dialogue moment is encoded as a “Now” containing a ranked temporal index, a holon path, an embedding vector, and a morphemic concept field.

The central contribution is a symmetry/asymmetry model for uncertainty-aware machine cognition. Symmetry appears in the reusable structural contract shared across records, operators, and holonic partitions; asymmetry appears in the ranked displacement between events, where contextual novelty, contradiction, and latent meaning emerge. We formalize this displacement as a ghost interval: a disparity field between ranked coordinates from which higher-order semantic patterns can be extracted. This reframes memory retrieval as an operation over temporal disparity rather than static similarity alone.

The proposed framework is particularly relevant to intelligent decision-making under uncertainty. By treating dialogue history as a holon-partitioned TimeField, the architecture enables local coherence within semantic branches while preserving global rank-order relations across the system. A Markovian operator, Clio, traverses these ranked slices to identify morpheme transitions, anticipatory branches, and latent decision-relevant patterns. This yields a practical mechanism for modeling epistemic instability in LLM outputs, including ambiguity, hallucination risk, and context drift.

The paper situates ranked-occurrence temporology within broader research on symmetry, uncertainty representation, multi-criteria decision analysis, and machine learning under uncertainty, aligning with the MDPI Symmetry special issue on “Symmetry in Uncertainty and Intelligent Decision-Making.” Rather than presenting consciousness synthesis as a metaphysical claim, the article treats temporal coherence as an engineering problem: how can an artificial system maintain structured continuity across uncertain, evolving, and asymmetric informational states? The resulting framework offers a formal path toward uncertainty-aware LLM memory, anticipatory decision support, and auditable temporal reasoning.

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Keywords

Ranked-occurrence temporology; uncertainty modeling; large language models; temporal coherence; symmetry and asymmetry; intelligent decision-making; holonic architecture; memory systems; Markovian operators; hallucination evaluation.