SSTA-ABLE: Synergistic Sequential-Topological Architectures for Adaptive Behaviour in Linguistic Environments - From Evolutionary Premises to Computational Demonstration

Abstract

We present a theoretical and computational investigation of the hypothesis that language functions as a dual representation of adaptive interactions between consciousness and environment: a sequential stream encoding local order and a topological landscape encoding global structural and affective relations. Building on evolutionary accounts of consciousness as a mechanism that shifts adaptive timescales from phylogenetic to ontogenetic and cultural regimes, we argue that artificial systems capable of the most adaptive behaviour within the „reality of language” must integrate sequential and topological information in a non-additive, synergistic manner. We propose an optimal synergic architecture combining sequential processing, explicit topological (graph and geometric) modules, multiplicative cross-interactions, certainty-gated fusion, and optional global-workspace integration under an adaptive objective. We then implement a controlled simulation of sequential-only, non-synergic (concatenative) hybrid, and synergic hybrid models under strong language shifts induced by abrupt rewiring of an underlying concept graph. Results demonstrate that the synergic hybrid achieves higher post-adaptation accuracy than either sequential-only or simple concatenative hybrid models, confirming super-additive behavioural adaptation. We discuss implications for artificial consciousness understood functionally as maximal adaptive behaviour in linguistic reality, limitations of the present toy regime, and pathways toward scalable implementations.